vignettes/AMR.Rmd
AMR.Rmd
# [1] "AMX" "AMC" "CIP" "GEN" data <- data %>% - mutate(across(where(is_sir_eligible), as.sir))
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package @@ -627,9 +632,9 @@ takes into account the antimicrobial susceptibility test results using # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. -# => Found 12,429 'phenotype-based' first isolates (62.1% of total where a +# => Found 20,000 'phenotype-based' first isolates (100.0% of total where a # microbial ID was available) -
So only 62.1% is suitable for resistance analysis! We can now filter +
So only 100% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -638,15 +643,13 @@ on it with the For future use, the above two syntaxes can be shortened: data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU. -So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
For future use, the above two syntaxes can be shortened: data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU. -So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
data_1st <- data %>% - filter_first_isolate() -# Including isolates from ICU.
So we end up with 12,429 isolates for analysis. Now our data looks + filter_first_isolate()
So we end up with 20,000 isolates for analysis. Now our data looks like:
head(data_1st)
Time for the analysis!
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,429 -Available: 12,429 (100%, NA: 0 = 0%) +Length: 20,000 +Available: 20,000 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1018,51 +1014,51 @@ different bug/drug combinations, you can use the
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5882211
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1158,19 +1154,19 @@ own: Hospital A -0.5883152 +0.6017567 Hospital B -0.5836154 +0.6139130 Hospital C -0.5717427 +0.6064538 Hospital D -0.6082433 +0.5972906 @@ -1195,23 +1191,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5883152 -3680 +0.6017567 +6034 Hospital B -0.5836154 -4431 +0.6139130 +6900 Hospital C -0.5717427 -1819 +0.6064538 +3006 Hospital D -0.6082433 -2499 +0.5972906 +4060 @@ -1236,27 +1232,27 @@ therapies very easily: Escherichia -0.6621849 -0.6502521 -0.8878992 +0.6691442 +0.6721575 +0.8938329 Klebsiella -0.6415797 -0.6684054 -0.8941878 +0.6503906 +0.6533203 +0.8803711 Staphylococcus -0.6676683 -0.6529447 -0.8846154 +0.6654655 +0.6648649 +0.8852853 Streptococcus -0.4693201 +0.4325225 0.0000000 -0.4693201 +0.4325225 @@ -1284,23 +1280,23 @@ classes, use a antibiotic class selector such as Hospital A -58.8% -36.2% +60.2% +36.8% Hospital B -58.4% -36.9% +61.4% +37.8% Hospital C -57.2% -36.3% +60.6% +36.6% Hospital D -60.8% -37.1% +59.7% +35.9% @@ -1416,16 +1412,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 256 64 0.001 0.01 8 8 1 0.5 0.5 16 -# [11] 0.125 256 0.5 0.002 2 0.005 1 1 256 0.002 -# [21] 0.002 0.01 0.25 2 256 64 0.125 128 0.001 4 -# [31] 0.125 0.0625 1 64 256 0.25 128 0.5 0.5 32 -# [41] 16 2 0.125 0.005 8 0.0625 0.01 0.25 0.125 0.025 -# [51] 0.0625 4 0.125 8 0.002 0.5 0.01 0.025 0.125 0.025 -# [61] 0.001 16 1 0.25 0.5 2 8 0.01 8 0.0625 -# [71] 2 0.025 8 0.005 0.025 8 0.002 0.002 256 2 -# [81] 0.01 0.002 16 0.0625 64 8 0.005 32 0.125 0.001 -# [91] 0.002 2 0.125 8 1 128 0.01 1 0.001 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 256 64 0.001 0.01 8 8 1 0.5 0.5 16 -# [11] 0.125 256 0.5 0.002 2 0.005 1 1 256 0.002 -# [21] 0.002 0.01 0.25 2 256 64 0.125 128 0.001 4 -# [31] 0.125 0.0625 1 64 256 0.25 128 0.5 0.5 32 -# [41] 16 2 0.125 0.005 8 0.0625 0.01 0.25 0.125 0.025 -# [51] 0.0625 4 0.125 8 0.002 0.5 0.01 0.025 0.125 0.025 -# [61] 0.001 16 1 0.25 0.5 2 8 0.01 8 0.0625 -# [71] 2 0.025 8 0.005 0.025 8 0.002 0.002 256 2 -# [81] 0.01 0.002 16 0.0625 64 8 0.005 32 0.125 0.001 -# [91] 0.002 2 0.125 8 1 128 0.01 1 0.001 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 25 20 25 25 26 18 20 24 23 28 30 18 27 27 25 20 28 21 19 23 27 28 28 24 28 -# [26] 17 25 19 23 30 22 17 31 23 31 24 28 25 28 28 27 31 20 24 18 18 24 29 17 31 -# [51] 17 28 23 26 19 25 18 22 24 18 17 27 26 26 25 18 23 21 21 28 17 25 30 29 29 -# [76] 18 31 20 22 28 23 26 24 27 28 20 25 19 19 17 24 24 17 18 17 30 24 21 29 27
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S S R I I R -# 2 R R S S I I -# 3 S R S S I S -# 4 R I R S S R -# 5 R I R I S S -# 6 I I I R S I +# 1 I S I I S I +# 2 S S S S I S +# 3 R R I S I R +# 4 I I R S S R +# 5 R I S S I S +# 6 R S R I R I # kanamycin # 1 I -# 2 R -# 3 R -# 4 I +# 2 S +# 3 S +# 4 S # 5 R -# 6 R
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -433,40 +438,40 @@ Unique: 5 1 Mono-resistant -3174 -63.48% -3174 -63.48% +3141 +62.82% +3141 +62.82% 2 Negative -1008 -20.16% -4182 -83.64% +1012 +20.24% +4153 +83.06% 3 Multi-drug-resistant -451 -9.02% -4633 -92.66% +477 +9.54% +4630 +92.60% 4 Poly-resistant -255 -5.10% -4888 -97.76% +257 +5.14% +4887 +97.74% 5 Extensively drug-resistant -112 -2.24% +113 +2.26% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index 43b4eeecc..3298944f2 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/articles/SPSS.html b/articles/SPSS.html index dcbe56a44..b8a30efb9 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -173,7 +178,7 @@ Dr. Matthijs Berends - 24 January 2023 + 08 February 2023 Source: vignettes/SPSS.Rmd SPSS.Rmd @@ -252,7 +257,7 @@ data using a custom made website. The webdesign knowledge needed R has a huge community. Many R users just ask questions on websites like StackOverflow.com, the largest -online community for programmers. At the time of writing, 478,396 +online community for programmers. At the time of writing, 480,015 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of diff --git a/articles/WHONET.html b/articles/WHONET.html index 0ab21a9be..4a0690d24 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/articles/datasets.html b/articles/datasets.html index 86765655f..74f96d190 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -171,7 +176,7 @@ Data sets for download / own use - 24 January 2023 + 08 February 2023 Source: vignettes/datasets.Rmd datasets.Rmd @@ -192,7 +197,7 @@ of the data sets look like. microorganisms: Full Microbial Taxonomy -A data set with 52,141 rows and 22 columns, containing the following +A data set with 52,142 rows and 22 columns, containing the following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, @@ -201,7 +206,7 @@ column names:mo, fullname, status, kingdomgbif_renamed_to, prevalence and snomed. This data set is in R available as microorganisms, after you load the AMR package. -It was last updated on 20 December 2022 15:14:04 UTC. Find more info +It was last updated on 6 February 2023 10:57:22 UTC. Find more info about the structure of this data set here. Direct download links: @@ -278,7 +283,7 @@ Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: Bacteria -36,475 +36,476 Fungi diff --git a/articles/index.html b/articles/index.html index c3cb74d28..df274b6d6 100644 --- a/articles/index.html +++ b/articles/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html index 68003e2e2..5e5cbacd8 100644 --- a/articles/resistance_predict.html +++ b/articles/resistance_predict.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index 41169a960..3e635baeb 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/authors.html b/authors.html index 24aec68fd..1afb036b6 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/extra.css b/extra.css index eb2fc57de..0995fd150 100644 --- a/extra.css +++ b/extra.css @@ -49,6 +49,11 @@ content: 'Maintainers'; } +/* marked words for after using the search box */ +mark, .mark { + background: rgba(17, 143, 118, 0.25) !important; +} + /* SYNTAX */ /* These are simple changes for the syntax highlighting */ diff --git a/index.html b/index.html index 7838161af..87b327c44 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -75,6 +75,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -338,9 +343,148 @@ This base R snippet will work in any version of R since April 2013 (R-3.0). +Generating antibiograms + +The AMR package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). +If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.). + +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems())) + + +Pathogen (N min-max) +AMK +GEN +IPM +KAN +MEM +TOB + + + +CoNS (43-309) +0 +86 +52 +0 +52 +22 + + +E. coli (0-462) +100 +98 +100 +NA +100 +97 + + +E. faecalis (0-39) +0 +0 +100 +0 +NA +0 + + +K. pneumoniae (0-58) +NA +90 +100 +NA +100 +90 + + +P. aeruginosa (17-30) +NA +100 +NA +0 +NA +100 + + +P. mirabilis (0-34) +NA +94 +94 +NA +NA +94 + + +S. aureus (2-233) +NA +99 +NA +NA +NA +98 + + +S. epidermidis (8-163) +0 +79 +NA +0 +NA +51 + + +S. hominis (3-80) +NA +92 +NA +NA +NA +85 + + +S. pneumoniae (11-117) +0 +0 +NA +0 +NA +0 + + + +In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage: + +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain") + + +Pathogen (N min-max) +TZP +TZP + GEN +TZP + TOB + + + +Gram-negative (641-693) +88 +99 +98 + + +Gram-positive (345-1044) +86 +98 +95 + + + + + Calculating resistance per group - + library(AMR) library(dplyr) @@ -388,7 +532,7 @@ - + # transform the antibiotic columns to names: out %>% set_ab_names() @@ -435,7 +579,7 @@ - + # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = "atc") @@ -515,7 +659,7 @@ This package is available here on the official R network (CRAN). Install this package in R from CRAN by using the command: - + install.packages("AMR") It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install. Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version. @@ -529,13 +673,13 @@ Manually, using: - + install.packages("remotes") # if you haven't already remotes::install_github("msberends/AMR") Automatically, using the rOpenSci R-universe platform, by adding our R-universe address to your list of repositories (‘repos’): - + options(repos = c(getOption("repos"), msberends = "https://msberends.r-universe.dev")) After this, you can install and update this AMR package like any official release (e.g., using install.packages("AMR") or in RStudio via Tools > Check for Package Updates…). diff --git a/news/index.html b/news/index.html index 3e94fffa3..6bf49017e 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -137,39 +142,46 @@ -AMR 1.8.2.9103 +AMR 1.8.2.9111 (this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!) This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below. TL;DR All functions and arguments with ‘rsi’ were replaced with ‘sir’, such as the interpretation of MIC values (now as.sir() instead of as.rsi()) - all old functions still work for now +Many new interesting functions, such as antibiogram() (for generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval() and mean_amr_distance(), and add_custom_microorganisms() to add custom microorganisms to this package +Clinical breakpoints added for EUCAST 2022 and CLSI 2022 Microbiological taxonomy (microorganisms data set) updated to 2022 and now based on LPSN and GBIF Much increased algorithms to translate user input to valid taxonomy, e.g. by using recent scientific work about per-species human pathogenicity -Clinical breakpoints added for EUCAST 2022 and CLSI 2022 20 new antibiotics added and updated all DDDs and ATC codes Extended support for antiviral agents (antivirals data set), with many new functions Now available in 16 languages -Many new interesting functions, such as sir_confidence_interval() and mean_amr_distance(), and add_custom_microorganisms() to add custom microorganisms to this package Many small bug fixes -New +New -SIR vs. RSI +SIR vs. RSI For this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR. While existing functions such as as.rsi(), rsi_df() and ggplot_rsi() still work, their replacements as.sir(), sir_df(), ggplot_sir() are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set rsi_translation is now called clinical_breakpoints to better reflect its content. The ‘RSI functions’ will be removed in a future version, but not before late 2023 / early 2024. -Interpretation of MIC and disk diffusion values +New antibiogram function +Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). +With the new antibiogram() function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). +The help page for antibiogram() extensively elaborates on use cases, and antibiogram() also supports printing in R Markdown and Quarto, with support for 16 languages. +Furthermore, different plotting methods were implemented to allow for graphical visualisations as well. + + +Interpretation of MIC and disk diffusion values The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for as.sir(). EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules() to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline option, such as: options(AMR_guideline = "CLSI 2020"). Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013. -Supported languages +Supported languages We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language. We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 16 languages and according to download stats used in almost all countries in the world! -Microbiological taxonomy +Microbiological taxonomy The microorganisms data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022. We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score() on how their work was incorporated into the prevalence column of the microorganisms data set. Using their results, the as.mo() and all mo_*() functions are now much better capable of converting user input to valid taxonomic records. The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package. @@ -193,7 +205,7 @@ The microorganisms.old data set was removed, and all previously accepted names are now included in the microorganisms data set. A new column status contains "accepted" for currently accepted names and "synonym" for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier. -Antibiotic agents and selectors +Antibiotic agents and selectors The new function add_custom_antimicrobials() allows users to add custom antimicrobial codes and names to the AMR package. The antibiotics data set was greatly updated: The following 20 antibiotics have been added (also includes the new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), and thioacetazone (TAT) @@ -205,14 +217,14 @@ Also, we added support for using antibiotic selectors in scoped dplyr verbs (with or without using vars()), such as in: ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() for examples. -Antiviral agents +Antiviral agents We now added extensive support for antiviral agents! For the first time, the AMR package has extensive support for antiviral drugs and to work with their names, codes and other data in any way. The antivirals data set has been extended with 18 new drugs (also from the new J05AJ ATC group) and now also contains antiviral identifiers and LOINC codes A new data type av (antivirals) has been added, which is functionally similar to ab for antibiotics Functions as.av(), av_name(), av_atc(), av_synonyms(), av_from_text() have all been added as siblings to their ab_*() equivalents -Other new functions +Other new functions Function sir_confidence_interval() to add confidence intervals in AMR calculation. This is now also included in sir_df() and proportion_df(). Function mean_amr_distance() to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. Function sir_interpretation_history() to view the history of previous runs of as.sir() (previously as.rsi()). This returns a ‘logbook’ with the selected guideline, reference table and specific interpretation of each row in a data set on which as.sir() was run. @@ -221,10 +233,10 @@ -Changes +Changes Argument combine_IR has been removed from this package (affecting functions count_df(), proportion_df(), and sir_df() and some plotting functions), since it was replaced with combine_SI three years ago Using units in ab_ddd(..., units = "...") had been deprecated for some time and is now not supported anymore. Use ab_ddd_units() instead. -Support for data.frame-enhancing R packages, more specifically: data.table::data.table, janitor::tabyl, tibble::tibble, and tsibble::tsibble. AMR package functions that have a data set as output (such as sir_df() and bug_drug_combinations()), will now return the same data type as the input. +Support for data.frame-enhancing R packages, more specifically: data.table::data.table, janitor::tabyl, tibble::tibble, and tsibble::tsibble. AMR package functions that have a data set as output (such as sir_df() and bug_drug_combinations()), will now return the same data type as the input. All data sets in this package are now a tibble, instead of base R data.frames. Older R versions are still supported, even if they do not support tibbles. Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info in this article on our website. For as.sir(): @@ -258,9 +270,10 @@ Fix for mo_shortname() in case of higher taxonomic ranks (order, class, phylum) Cleaning columns with as.sir(), as.mic(), or as.disk() will now show the column name in the warning for invalid results +Fix for using g.test() with zeroes in a 2x2 table -Other +Other Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, and Jonas Salm as contributors, to thank them for their valuable input New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions All R and Rmd files in this project are now styled using the styler package diff --git a/pkgdown.yml b/pkgdown.yml index b57efb007..b95f326df 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2023-01-24T15:35Z +last_built: 2023-02-08T15:58Z urls: reference: https://msberends.github.io/AMR/reference article: https://msberends.github.io/AMR/articles diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html index b76bad45a..4c4ab36a1 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/AMR-options.html b/reference/AMR-options.html index ecf0b645d..44f9db297 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/AMR.html b/reference/AMR.html index 67843c0d2..1dcafe8b7 100644 --- a/reference/AMR.html +++ b/reference/AMR.html @@ -18,7 +18,7 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German, AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -50,6 +50,11 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German, Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/Rplot003.png b/reference/Rplot003.png index 1ed92087b..b470d2333 100644 Binary files a/reference/Rplot003.png and b/reference/Rplot003.png differ diff --git a/reference/Rplot005.png b/reference/Rplot005.png index cba65bc28..f7b60774c 100644 Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ diff --git a/reference/Rplot006.png b/reference/Rplot006.png index 0e487a53a..b913b5db2 100644 Binary files a/reference/Rplot006.png and b/reference/Rplot006.png differ diff --git a/reference/Rplot007.png b/reference/Rplot007.png index a9a3af1ea..5d1df79b2 100644 Binary files a/reference/Rplot007.png and b/reference/Rplot007.png differ diff --git a/reference/Rplot008.png b/reference/Rplot008.png index bce895010..4cdf8d40e 100644 Binary files a/reference/Rplot008.png and b/reference/Rplot008.png differ diff --git a/reference/Rplot009.png b/reference/Rplot009.png index 752cb97d5..b4a86c2f6 100644 Binary files a/reference/Rplot009.png and b/reference/Rplot009.png differ diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 68396f6a6..4d1cddd14 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/WHONET.html b/reference/WHONET.html index 4fc1f3bae..d936bc7a6 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 9146817c8..fe6fee3b8 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/ab_property.html b/reference/ab_property.html index 1bee0d0f8..46ca6bddf 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -469,22 +474,18 @@ # \donttest{ if (require("dplyr")) { example_isolates %>% - set_ab_names() %>% - head() + set_ab_names() # this does the same: example_isolates %>% - rename_with(set_ab_names) %>% - head() + rename_with(set_ab_names) # set_ab_names() works with any AB property: example_isolates %>% - set_ab_names(property = "atc") %>% - head() + set_ab_names(property = "atc") example_isolates %>% - set_ab_names(where(is.sir)) %>% - colnames() + set_ab_names(where(is.sir)) example_isolates %>% set_ab_names(NIT:VAN) %>% diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index b7b6f5b2a..54d640604 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 98aec5e8e..15620ca6f 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -260,7 +265,7 @@ #> [1] "Self-added, 2023" #> #> $snomed -#> character(0) +#> [1] NA #> diff --git a/reference/age.html b/reference/age.html index e296e4052..b812429ff 100644 --- a/reference/age.html +++ b/reference/age.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -200,16 +205,16 @@ df #> birth_date age age_exact age_at_y2k -#> 1 1941-04-16 81 81.77534 58 -#> 2 1966-08-03 56 56.47671 33 -#> 3 1980-02-04 42 42.96986 19 -#> 4 1967-03-18 55 55.85479 32 -#> 5 1947-05-25 75 75.66849 52 -#> 6 1948-08-19 74 74.43288 51 -#> 7 1933-07-04 89 89.55890 66 -#> 8 1954-12-03 68 68.14247 45 -#> 9 1956-07-25 66 66.50137 43 -#> 10 1998-12-10 24 24.12329 1 +#> 1 1980-09-24 42 42.37534 19 +#> 2 1957-01-21 66 66.04932 42 +#> 3 1935-11-17 87 87.22740 64 +#> 4 1945-08-15 77 77.48493 54 +#> 5 1986-04-06 36 36.84384 13 +#> 6 1952-04-20 70 70.80548 47 +#> 7 1985-09-08 37 37.41918 14 +#> 8 1993-09-17 29 29.39452 6 +#> 9 1947-07-04 75 75.60000 52 +#> 10 1943-09-17 79 79.39452 56 diff --git a/reference/antibiogram-1.png b/reference/antibiogram-1.png new file mode 100644 index 000000000..d4e50977b Binary files /dev/null and b/reference/antibiogram-1.png differ diff --git a/reference/antibiogram-2.png b/reference/antibiogram-2.png new file mode 100644 index 000000000..a1555e745 Binary files /dev/null and b/reference/antibiogram-2.png differ diff --git a/reference/antibiogram-3.png b/reference/antibiogram-3.png new file mode 100644 index 000000000..4f2284a5f Binary files /dev/null and b/reference/antibiogram-3.png differ diff --git a/reference/antibiogram-4.png b/reference/antibiogram-4.png new file mode 100644 index 000000000..0b1b9be7b Binary files /dev/null and b/reference/antibiogram-4.png differ diff --git a/reference/antibiogram.html b/reference/antibiogram.html new file mode 100644 index 000000000..ffc2d6348 --- /dev/null +++ b/reference/antibiogram.html @@ -0,0 +1,526 @@ + +Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram • AMR (for R) + Skip to contents + + + + + AMR (for R) + + 1.8.2.9111 + + + + + + + + + + + + Home + + + + + + + How to + + + + + + User- Or Team-specific Package Settings + + + + + Conduct AMR Analysis + + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + + + + + Predict Antimicrobial Resistance + + + + + Data Sets for Download / Own Use + + + + + Conduct Principal Component Analysis for AMR + + + + + Determine Multi-Drug Resistance (MDR) + + + + + Work with WHONET Data + + + + + Import Data From SPSS/SAS/Stata + + + + + Apply Eucast Rules + + + + + Get Taxonomy of a Microorganism + + + + + Get Properties of an Antibiotic Drug + + + + + Get Properties of an Antiviral Drug + + + + + + + + Manual + + + + + + + Authors + + + + + + + Changelog + + + + + + + + + + Source Code + + + + + + + + + + Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) + Source: R/antibiogram.R + antibiogram.Rd + + + + Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker et al. (2021, doi:10.1177/20499361211011373 +) and Barbieri et al. (2021, doi:10.1186/s13756-021-00939-2 +), and allow reporting in e.g. R Markdown and Quarto as well. + + + + Usage + antibiogram( + x, + antibiotics = where(is.sir), + mo_transform = "shortname", + ab_transform = NULL, + syndromic_group = NULL, + add_total_n = TRUE, + only_all_tested = FALSE, + digits = 0, + col_mo = NULL, + language = get_AMR_locale(), + minimum = 30, + combine_SI = TRUE, + sep = " + " +) + +# S3 method for antibiogram +plot(x, ...) + +# S3 method for antibiogram +autoplot(object, ...) + +# S3 method for antibiogram +print(x, as_kable = !interactive(), ...) + + + + Source + +Klinker KP et al. (2021). Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms. Therapeutic Advances in Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 +Barbieri E et al. (2021). Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 +M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. + + + Arguments + x +a data.frame containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see as.sir()) + + +antibiotics +vector of column names, or (any combinations of) antibiotic selectors such as aminoglycosides() or carbapenems(). For combination antibiograms, this can also be column names separated with "+", such as "TZP+TOB" given that the data set contains columns "TZP" and "TOB". See Examples. + + +mo_transform +a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed". Can also be NULL to not transform the input. + + +ab_transform +a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units" or "loinc". Can also be NULL to not transform the input. + + +syndromic_group +a column name of x, or values calculated to split rows of x, e.g. by using ifelse() or case_when(). See Examples. + + +add_total_n +a logical to indicate whether total available numbers per pathogen should be added to the table (defaults to TRUE). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for E. coli 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). + + +only_all_tested +(for combination antibiograms): a logical to indicate that isolates must be tested for all antibiotics, see Details + + +digits +number of digits to use for rounding + + +col_mo +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). + + +language +language to translate text, which defaults to the system language (see get_AMR_locale()) + + +minimum +the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. + + +combine_SI +a logical to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (defaults to TRUE) + + +sep +a separating character for antibiotic columns in combination antibiograms + + +... +method extensions + + +object +an antibiogram() object + + +as_kable +a logical to indicate whether the printing should be done using knitr::kable() (which is the default in non-interactive sessions) + + + + Details + This function returns a table with values between 0 and 100 for susceptibility, not resistance. +Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms. +There are four antibiogram types, as proposed by Klinker et al. (2021, doi:10.1177/20499361211011373 +), and they are all supported by antibiogram():Traditional Antibiogram +Case example: Susceptibility of Pseudomonas aeruginosa to piperacillin/tazobactam (TZP) +Code example: +antibiogram(your_data, + antibiotics = "TZP") +Combination Antibiogram +Case example: Additional susceptibility of Pseudomonas aeruginosa to TZP + tobramycin versus TZP alone +Code example: +antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN")) +Syndromic Antibiogram +Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) +Code example: +antibiogram(your_data, + antibiotics = penicillins(), + syndromic_group = "ward") +Weighted-Incidence Syndromic Combination Antibiogram (WISCA) +Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure +Code example: +antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male", + "Group 1", "Group 2")) +All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using ggplot2::autoplot() or base R plot()/barplot()) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. flextable::as_flextable() or gt::gt(). +Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (defaults to FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI: +-------------------------------------------------------------------- + only_all_tested = FALSE only_all_tested = TRUE + ----------------------- ----------------------- + Drug A Drug B include as include as include as include as + numerator denominator numerator denominator +-------- -------- ---------- ----------- ---------- ----------- + S or I S or I X X X X + R S or I X X X X + <NA> S or I X X - - + S or I R X X X X + R R - X - X + <NA> R - - - - + S or I <NA> X X - - + R <NA> - - - - + <NA> <NA> - - - - +-------------------------------------------------------------------- +Printing the antibiogram in non-interactive sessions will be done by knitr::kable(), with support for all their implemented formats, such as "markdown". The knitr format will be automatically determined if printed inside a knitr document (LaTeX, HTML, etc.). + + + + Examples + # example_isolates is a data set available in the AMR package. +# run ?example_isolates for more info. +example_isolates +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +#> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +#> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +#> # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +#> # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +#> # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> + + +# Traditional antibiogram ---------------------------------------------- + +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems())) +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 502 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 10 × 7 +#> `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 CoNS (43-309) 0 0 0 0 0 0 +#> 2 E. coli (0-462) 0 0 0 NA 0 0 +#> 3 E. faecalis (0-39) 0 0 0 0 NA 0 +#> 4 K. pneumoniae (0-58) NA 0 0 NA 0 0 +#> 5 P. aeruginosa (17-30) NA 0 NA 0 NA 0 +#> 6 P. mirabilis (0-34) NA 0 0 NA NA 0 +#> 7 S. aureus (2-233) NA 0 NA NA NA 0 +#> 8 S. epidermidis (8-163) 0 0 NA 0 NA 0 +#> 9 S. hominis (3-80) NA 0 NA NA NA 0 +#> 10 S. pneumoniae (11-117) 0 0 NA 0 NA 0 + +antibiogram(example_isolates, + antibiotics = aminoglycosides(), + ab_transform = "atc", + mo_transform = "gramstain") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Pathogen (N min-max)` J01GB01 J01GB03 J01GB04 J01GB06 +#> * <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 Gram-negative (35-686) 0 0 0 0 +#> 2 Gram-positive (436-1170) 0 0 0 0 + +antibiogram(example_isolates, + antibiotics = carbapenems(), + ab_transform = "name", + mo_transform = "name") +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 172 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 5 × 3 +#> `Pathogen (N min-max)` Imipenem Meropenem +#> * <chr> <dbl> <dbl> +#> 1 Coagulase-negative Staphylococcus (CoNS) (48-48) 2 2 +#> 2 Enterococcus faecalis (0-38) 3 NA +#> 3 Escherichia coli (418-422) 0 0 +#> 4 Klebsiella pneumoniae (51-53) 2 2 +#> 5 Proteus mirabilis (27-32) 3 NA + + +# Combined antibiogram ------------------------------------------------- + +# combined antibiotics yield higher empiric coverage +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain") +#> ℹ 3 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 4 +#> `Pathogen (N min-max)` TZP `TZP + GEN` `TZP + TOB` +#> * <chr> <dbl> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 2 +#> 2 Gram-positive (345-1044) 4 1 3 + +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB"), + mo_transform = "gramstain", + ab_transform = "name", + sep = " & ") +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 3 +#> `Pathogen (N min-max)` `Piperacillin/tazobactam` Piperacillin/tazobactam & …¹ +#> * <chr> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 +#> 2 Gram-positive (345-550) 4 3 +#> # … with abbreviated variable name ¹`Piperacillin/tazobactam & Tobramycin` + + +# Syndromic antibiogram ------------------------------------------------ + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems()), + syndromic_group = "ward") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 897 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 14 × 8 +#> `Syndromic Group` `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 Clinical CoNS (23-205) NA 0 0 NA 0 0 +#> 2 ICU CoNS (10-73) NA 0 NA NA NA NA +#> 3 Outpatient CoNS (3-31) NA 0 NA NA NA NA +#> 4 Clinical E. coli (0-299) 0 0 0 NA 0 0 +#> 5 ICU E. coli (0-137) 0 0 0 NA 0 0 +#> 6 Clinical K. pneumoniae (0-51) NA 0 0 NA 0 0 +#> 7 Clinical P. mirabilis (0-30) NA 0 NA NA NA 0 +#> 8 Clinical S. aureus (2-150) NA 0 NA NA NA 0 +#> 9 ICU S. aureus (0-66) NA 0 NA NA NA NA +#> 10 Clinical S. epidermidis (4-79) NA 0 NA NA NA 0 +#> 11 ICU S. epidermidis (4-75) NA 0 NA NA NA 0 +#> 12 Clinical S. hominis (1-45) NA 0 NA NA NA 0 +#> 13 Clinical S. pneumoniae (5-78) 0 0 NA 0 NA 0 +#> 14 ICU S. pneumoniae (5-30) 0 0 NA 0 NA 0 + +# with a custom language, though this will be determined automatically +# (i.e., this table will be in Spanish on Spanish systems) +ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ] +#> ℹ Using column 'mo' as input for mo_genus() +antibiogram(ex1, + antibiotics = aminoglycosides(), + ab_transform = "name", + syndromic_group = ifelse(ex1$ward == "ICU", + "UCI", "No UCI"), + language = "es") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina +#> * <chr> <chr> <dbl> <dbl> <dbl> +#> 1 No UCI E. coli (0-325) 100 37 37 +#> 2 UCI E. coli (0-137) 229 87 87 + + +# Weighted-incidence syndromic combination antibiogram (WISCA) --------- + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + minimum = 10, # this should be >= 30, but now just as example + syndromic_group = ifelse(example_isolates$age >= 65 & + example_isolates$gender == "M", + "WISCA Group 1", "WISCA Group 2")) +#> ℹ 8 combinations had less than minimum = 10 results and were ignored +#> # A tibble: 4 × 6 +#> `Syndromic Group` `Pathogen (N min-max)` AMC `AMC + CIP` TZP `TZP + TOB` +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 WISCA Group 1 Gram-negative (261-285) 3 3 3 3 +#> 2 WISCA Group 2 Gram-negative (380-442) 2 2 2 2 +#> 3 WISCA Group 1 Gram-positive (123-406) 2 2 7 5 +#> 4 WISCA Group 2 Gram-positive (222-732) 1 1 4 3 + + +# Generate plots with ggplot2 or base R -------------------------------- + +ab1 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain") +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +ab2 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + syndromic_group = "ward") +#> ℹ 16 combinations had less than minimum = 30 results and were ignored + +plot(ab1) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab1) +} + + +plot(ab2) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab2) +} + + + + + + + + + + + + + + diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html index ba1f7d1d3..221ddafc9 100644 --- a/reference/antibiotic_class_selectors.html +++ b/reference/antibiotic_class_selectors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -232,7 +237,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). version_expertrules @@ -596,7 +601,11 @@ } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) -#> Warning: Introducing NA: only 23 results available for KAN in group: ward = +#> Warning: There was 1 warning in `summarise()`. +#> ℹ In argument: `across(aminoglycosides(), resistance)`. +#> ℹ In group 3: `ward = "Outpatient"`. +#> Caused by warning: +#> ! Introducing NA: only 23 results available for KAN in group: ward = #> "Outpatient" (minimum = 30). #> # A tibble: 3 × 5 #> ward GEN TOB AMK KAN @@ -648,110 +657,14 @@ #> (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #> (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #> (azithromycin) and 'RIF' (rifampicin) -#> Warning: Introducing NA: no results available for OXA in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 25 results available for AMX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for AMC in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for AMP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 24 results available for TZP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CZO in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 10 results available for CZO in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 20 results available for FEP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CXM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 22 results available for FOX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for CTX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CAZ in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for CRO in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for GEN in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TOB in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for AMK in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 22 results available for TMP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for SXT in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for NIT in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for FOS in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CIP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for MFX in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 4 results available for MFX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for TCY in group: ward = -#> "Clinical" (minimum = 30). -#> Warning: Introducing NA: no results available for TCY in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for TCY in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 13 results available for TGC in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TGC in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 24 results available for IPM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: ward = -#> "Clinical" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "Outpatient" +#> Warning: There were 52 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. +#> ℹ In group 1: `ward = "Clinical"`. +#> Caused by warning: +#> ! Introducing NA: no results available for OXA in group: ward = "Clinical" #> (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # A tibble: 3 × 33 #> ward OXA FLC AMX AMC AMP TZP CZO FEP CXM FOX #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> @@ -769,15 +682,14 @@ group_by(ward) %>% summarise(across(ab_selector(name %like% "trim"), susceptibility)) } -#> Including isolates from ICU. #> ℹ For ab_selector(name %like% "trim") using columns 'TMP' (trimethoprim) #> and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #> ward TMP SXT #> <chr> <dbl> <dbl> -#> 1 Clinical 0.627 0.808 -#> 2 ICU 0.549 0.778 -#> 3 Outpatient 0.667 0.821 +#> 1 Clinical 0.633 0.793 +#> 2 ICU 0.578 0.792 +#> 3 Outpatient 0.705 0.835 if (require("dplyr")) { # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>% diff --git a/reference/antibiotics.html b/reference/antibiotics.html index 324da1df7..fafc9e935 100644 --- a/reference/antibiotics.html +++ b/reference/antibiotics.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.ab.html b/reference/as.ab.html index f017e9f7e..9cfee9280 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -272,7 +277,7 @@ if (require("dplyr")) { # you can quickly rename 'sir' columns using set_ab_names() with dplyr: example_isolates %>% - set_ab_names(where(is.sir), property = "atc") + set_ab_names(where(is.sir), property = "atc") } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo J01CE01 J01CF04 J01CF05 diff --git a/reference/as.av.html b/reference/as.av.html index c63e2c381..124c6b828 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.disk.html b/reference/as.disk.html index c4c3730df..cd2f2a6ad 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png index e43a9606c..f1b1cd1d5 100644 Binary files a/reference/as.mic-4.png and b/reference/as.mic-4.png differ diff --git a/reference/as.mic-5.png b/reference/as.mic-5.png index 26b403a78..62e5a977b 100644 Binary files a/reference/as.mic-5.png and b/reference/as.mic-5.png differ diff --git a/reference/as.mic.html b/reference/as.mic.html index e0122fd31..e917b07a6 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.mo.html b/reference/as.mo.html index 4a0b657fd..4c798188d 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.sir.html b/reference/as.sir.html index 3d9291806..5014ae6f1 100644 --- a/reference/as.sir.html +++ b/reference/as.sir.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,7 +256,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). clean @@ -312,7 +317,7 @@ Machine-Readable Interpretation Guidelines -The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18,308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed. +The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18 308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed. @@ -519,16 +524,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of #> # A tibble: 50 × 12 #> datetime index ab_input ab_guid…¹ mo_in…² mo_guideline guide…³ #> <dttm> <int> <chr> <ab> <chr> <mo> <chr> -#> 1 2023-01-24 15:36:22 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… -#> 2 2023-01-24 15:36:22 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… -#> 3 2023-01-24 15:36:21 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… -#> 4 2023-01-24 15:36:21 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… -#> 5 2023-01-24 15:36:15 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… -#> 6 2023-01-24 15:36:15 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… -#> 7 2023-01-24 15:36:15 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… -#> 8 2023-01-24 15:36:15 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… -#> 9 2023-01-24 15:36:15 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… -#> 10 2023-01-24 15:36:15 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 1 2023-02-08 15:59:02 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… +#> 2 2023-02-08 15:59:02 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… +#> 3 2023-02-08 15:59:01 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… +#> 4 2023-02-08 15:59:01 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… +#> 5 2023-02-08 15:58:56 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 6 2023-02-08 15:58:56 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 7 2023-02-08 15:58:56 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 8 2023-02-08 15:58:56 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 9 2023-02-08 15:58:56 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… +#> 10 2023-02-08 15:58:56 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table <chr>, method <chr>, #> # input <dbl>, outcome <sir>, breakpoint_S_R <chr>, and abbreviated variable #> # names ¹ab_guideline, ²mo_input, ³guideline @@ -565,7 +570,7 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of if (require("dplyr")) { df %>% mutate_if(is.mic, as.sir) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) - df %>% mutate(across(where(is.mic), as.sir)) + df %>% mutate(across(where(is.mic), as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir) df %>% mutate(across(AMP:TOB, as.sir)) diff --git a/reference/atc_online.html b/reference/atc_online.html index a4d893c55..6ab4674cf 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 80adf28c8..19825fcbc 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/av_property.html b/reference/av_property.html index 280564ee7..0d1f626d2 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/availability.html b/reference/availability.html index 7a31892cf..ab3ef6d35 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index aa999ce46..fd0d9f8eb 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -155,7 +160,7 @@ add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption("OutDec"), - big.mark = ifelse(decimal.mark == ",", ".", ","), + big.mark = ifelse(decimal.mark == ",", " ", ","), ... ) @@ -167,7 +172,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). FUN @@ -227,18 +232,31 @@ Examples # \donttest{ +#' # example_isolates is a data set available in the AMR package. +# run ?example_isolates for more info. +example_isolates +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +#> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +#> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +#> # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +#> # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +#> # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> + x <- bug_drug_combinations(example_isolates) -head(x) -#> # A tibble: 6 × 6 -#> mo ab S I R total -#> <chr> <chr> <int> <int> <int> <int> -#> 1 (unknown species) PEN 14 0 1 15 -#> 2 (unknown species) OXA 0 0 1 1 -#> 3 (unknown species) FLC 0 0 0 0 -#> 4 (unknown species) AMX 15 0 1 16 -#> 5 (unknown species) AMC 15 0 0 15 -#> 6 (unknown species) AMP 15 0 1 16 -#> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = "name (atc)") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ @@ -262,20 +280,20 @@ bug_drug_combinations(example_isolates, FUN = mo_gramstain ) -#> # A tibble: 80 × 6 +#> # A tibble: 120 × 6 #> mo ab S I R total #> * <chr> <chr> <int> <int> <int> <int> -#> 1 Gram-negative PEN 8 0 717 725 -#> 2 Gram-negative OXA 6 0 0 6 -#> 3 Gram-negative FLC 6 0 0 6 +#> 1 Gram-negative AMC 463 89 174 726 +#> 2 Gram-negative AMK 251 0 5 256 +#> 3 Gram-negative AMP 226 0 405 631 #> 4 Gram-negative AMX 226 0 405 631 -#> 5 Gram-negative AMC 463 89 174 726 -#> 6 Gram-negative AMP 226 0 405 631 -#> 7 Gram-negative TZP 554 11 76 641 -#> 8 Gram-negative CZO 94 2 110 206 -#> 9 Gram-negative FEP 470 1 14 485 -#> 10 Gram-negative CXM 539 22 142 703 -#> # … with 70 more rows +#> 5 Gram-negative AZM 1 2 696 699 +#> 6 Gram-negative CAZ 607 0 27 634 +#> 7 Gram-negative CHL 1 0 30 31 +#> 8 Gram-negative CIP 610 11 63 684 +#> 9 Gram-negative CLI 18 1 709 728 +#> 10 Gram-negative COL 309 0 78 387 +#> # … with 110 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, @@ -289,16 +307,16 @@ #> # A tibble: 80 × 6 #> mo ab S I R total #> * <chr> <chr> <int> <int> <int> <int> -#> 1 E. coli PEN 0 0 467 467 -#> 2 E. coli OXA 0 0 0 0 -#> 3 E. coli FLC 0 0 0 0 +#> 1 E. coli AMC 332 74 61 467 +#> 2 E. coli AMK 171 0 0 171 +#> 3 E. coli AMP 196 0 196 392 #> 4 E. coli AMX 196 0 196 392 -#> 5 E. coli AMC 332 74 61 467 -#> 6 E. coli AMP 196 0 196 392 -#> 7 E. coli TZP 388 5 23 416 -#> 8 E. coli CZO 79 1 2 82 -#> 9 E. coli FEP 308 0 9 317 -#> 10 E. coli CXM 425 15 25 465 +#> 5 E. coli AZM 0 0 467 467 +#> 6 E. coli CAZ 449 0 11 460 +#> 7 E. coli CHL 0 0 0 0 +#> 8 E. coli CIP 398 1 57 456 +#> 9 E. coli CLI 0 0 467 467 +#> 10 E. coli COL 240 0 0 240 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # } diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 1d2f80733..44866f505 100644 --- a/reference/clinical_breakpoints.html +++ b/reference/clinical_breakpoints.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -148,7 +153,7 @@ Format - A tibble with 18,308 observations and 11 variables:guideline Name of the guideline + A tibble with 18 308 observations and 11 variables:guideline Name of the guideline method Either "DISK" or "MIC" site Body site, e.g. "Oral" or "Respiratory" mo Microbial ID, see as.mo() diff --git a/reference/count.html b/reference/count.html index 8b8386344..2398ed973 100644 --- a/reference/count.html +++ b/reference/count.html @@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible( AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ count_resistant() should be used to count resistant isolates, count_susceptible( Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index 71e6eaa97..7a3ba6f0f 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/dosage.html b/reference/dosage.html index 874eac63e..6c11a77e2 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index ebcab1318..88715447f 100644 --- a/reference/eucast_rules.html +++ b/reference/eucast_rules.html @@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -183,7 +188,7 @@ Leclercq et al. EUCAST expert rules in antimicrobial susceptibility test col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). info diff --git a/reference/example_isolates.html b/reference/example_isolates.html index e00bd1843..831df3efc 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -1,5 +1,5 @@ -Data Set with 2,000 Example Isolates — example_isolates • AMR (for R)Data Set with 2 000 Example Isolates — example_isolates • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,13 +137,13 @@ - Data Set with 2,000 Example Isolates + Data Set with 2 000 Example Isolates Source: R/data.R example_isolates.Rd - A data set containing 2,000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website. + A data set containing 2 000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website. @@ -148,7 +153,7 @@ Format - A tibble with 2,000 observations and 46 variables:date Date of receipt at the laboratory + A tibble with 2 000 observations and 46 variables:date Date of receipt at the laboratory patient ID of the patient age Age of the patient gender Gender of the patient, either "F" or "M" diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index 53ee9e342..a7da9f22d 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -1,5 +1,5 @@ -Data Set with Unclean Data — example_isolates_unclean • AMR (for R)Data Set with Unclean Data — example_isolates_unclean • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -138,7 +143,7 @@ - A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice. + A data set containing 3 000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice. @@ -148,7 +153,7 @@ Format - A tibble with 3,000 observations and 8 variables:patient_id ID of the patient + A tibble with 3 000 observations and 8 variables:patient_id ID of the patient date date of receipt at the laboratory hospital ID of the hospital, from A to C bacteria info about microorganism that can be transformed with as.mo(), see also microorganisms diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 49d4931d6..3936a4dca 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -12,7 +12,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -200,7 +205,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). col_testcode @@ -284,7 +289,7 @@ According to Hindler et al. (2007, doi:10.1086/511864 ), there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies). -All mentioned methods are covered in the first_isolate() function:MethodFunction to applyIsolate-basedfirst_isolate(x, method = "isolate-based")(= all isolates)Patient-basedfirst_isolate(x, method = "patient-based")(= first isolate per patient)Episode-basedfirst_isolate(x, method = "episode-based"), or:(= first isolate per episode)- 7-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 7)- 30-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 30)Phenotype-basedfirst_isolate(x, method = "phenotype-based"), or:(= first isolate per phenotype)- Major difference in any antimicrobial result- first_isolate(x, type = "points")- Any difference in key antimicrobial results- first_isolate(x, type = "keyantimicrobials") +All mentioned methods are covered in the first_isolate() function:MethodFunction to applyIsolate-basedfirst_isolate(x, method = "isolate-based")(= all isolates)Patient-basedfirst_isolate(x, method = "patient-based")(= first isolate per patient)Episode-basedfirst_isolate(x, method = "episode-based"), or:(= first isolate per episode)- 7-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 7)- 30-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 30)Phenotype-basedfirst_isolate(x, method = "phenotype-based"), or:(= first isolate per phenotype)- Major difference in any antimicrobial result- first_isolate(x, type = "points")- Any difference in key antimicrobial results- first_isolate(x, type = "keyantimicrobials") Isolate-based @@ -332,22 +337,30 @@ # `example_isolates` is a data set available in the AMR package. # See ?example_isolates. -example_isolates[first_isolate(), ] +example_isolates[first_isolate(info = TRUE), ] +#> Determining first isolates using an episode length of 365 days +#> ℹ Using column 'date' as input for col_date. +#> ℹ Using column 'patient' as input for col_patient_id. +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -357,22 +370,21 @@ # \donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] -#> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() -#> # A tibble: 437 × 46 +#> # A tibble: 731 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R -#> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R -#> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R -#> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R -#> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA -#> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA -#> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 4 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 5 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R +#> 6 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 7 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 8 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 9 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 10 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> # … with 721 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> # CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> # CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> # SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, @@ -383,23 +395,29 @@ if (require("dplyr")) { # filter on first isolates using dplyr: example_isolates %>% - filter(first_isolate()) + filter(first_isolate(info = TRUE)) } +#> Determining first isolates using an episode length of 365 days +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -411,21 +429,20 @@ example_isolates %>% filter_first_isolate(info = FALSE) } -#> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -436,25 +453,22 @@ # flag the first isolates per group: example_isolates %>% group_by(ward) %>% - mutate(first = first_isolate()) %>% + mutate(first = first_isolate(info = FALSE)) %>% select(ward, date, patient, mo, first) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> <chr> <date> <chr> <mo> <lgl> #> 1 Clinical 2002-01-02 A77334 B_ESCHR_COLI TRUE -#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI FALSE +#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI TRUE #> 3 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE -#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR FALSE -#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE -#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE +#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE +#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE +#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE #> 7 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE -#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS FALSE -#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR FALSE +#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE +#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR TRUE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # … with 1,990 more rows # } diff --git a/reference/g.test.html b/reference/g.test.html index 6d54325f6..f4c7119ad 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/get_episode.html b/reference/get_episode.html index da0f76bd6..b24cf3e76 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -1,5 +1,5 @@ -Determine (New) Episodes for Patients — get_episode • AMR (for R)Determine (New) Episodes for Patients — get_episode • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -133,12 +138,12 @@ Determine (New) Episodes for Patients - Source: R/episode.R + Source: R/get_episode.R get_episode.Rd - These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode. + These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE for where get_episode() returns 1, and is thus equal to get_episode(...) == 1. @@ -173,7 +178,7 @@ Details Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored. The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods. -The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). +The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). See also @@ -184,47 +189,32 @@ Examples # `example_isolates` is a data set available in the AMR package. # See ?example_isolates -df <- example_isolates[sample(seq_len(2000), size = 200), ] +df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices -#> [1] 14 30 51 29 53 28 49 36 9 7 29 9 43 30 20 51 5 2 5 9 27 6 21 25 14 -#> [26] 20 29 22 42 51 56 39 31 47 1 10 56 7 54 13 39 20 44 10 37 45 26 42 53 3 -#> [51] 2 7 13 51 48 52 31 9 38 50 54 15 41 53 38 23 16 9 26 12 5 26 4 6 31 -#> [76] 7 40 27 55 57 22 52 27 36 26 16 10 48 23 36 49 47 6 6 56 22 51 48 44 24 -#> [101] 58 33 47 49 58 32 8 6 21 3 14 33 57 43 14 50 6 3 53 6 18 15 52 31 22 -#> [126] 7 47 6 42 39 45 1 49 23 53 13 18 35 8 57 12 11 17 3 55 16 12 54 55 14 -#> [151] 9 46 51 55 19 7 36 8 57 4 14 45 45 59 38 55 25 23 60 57 39 38 54 16 31 -#> [176] 3 16 39 50 37 40 14 53 60 6 34 32 14 3 30 52 24 23 1 59 52 11 41 7 26 +#> [1] 24 25 6 9 9 40 28 6 15 42 34 3 1 21 39 3 5 22 30 13 14 31 12 41 36 +#> [26] 34 38 46 18 25 45 43 2 5 17 44 23 43 8 37 17 28 31 43 26 47 43 6 1 28 +#> [51] 18 12 30 22 20 29 31 34 18 13 48 12 31 15 4 3 16 2 7 35 6 19 29 11 24 +#> [76] 16 12 28 14 32 1 23 13 4 6 9 46 38 37 10 13 17 12 40 33 30 7 27 48 38 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE -#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE -#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE -#> [25] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE -#> [37] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE -#> [49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE -#> [61] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE -#> [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE -#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [97] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE -#> [109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE -#> [121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE -#> [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE -#> [157] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE -#> [169] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [181] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE -#> [193] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE +#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE +#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [97] FALSE FALSE FALSE FALSE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] -#> # A tibble: 6 × 46 -#> date patient age gender ward mo PEN OXA FLC AMX -#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> -#> 1 2002-11-07 430011 82 F Clinical B_STPHY_CONS R NA R R -#> 2 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 3 2002-11-14 058917 76 F ICU B_STPHY_HMNS R NA S NA -#> 4 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 5 2002-11-18 956065 89 F Clinical B_ESCHR_COLI R NA NA NA -#> 6 2002-10-14 FCC668 54 F ICU B_ACNTB R NA NA NA +#> # A tibble: 3 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2003-02-26 869648 64 M Outpatie… B_STPHY_AURS R NA R R +#> 2 2003-03-22 E44854 60 F ICU B_STRPT_PNMN S NA NA S +#> 3 2003-01-25 088256 73 F ICU B_STPHY_HMNS R NA R R #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, @@ -234,13 +224,9 @@ #> # MUP <sir>, RIF <sir> # the functions also work for less than a day, e.g. to include one per hour: -get_episode( - c( - Sys.time(), - Sys.time() + 60 * 60 - ), - episode_days = 1 / 24 -) +get_episode(c(Sys.time(), + Sys.time() + 60 * 60), + episode_days = 1 / 24) #> [1] 1 2 # \donttest{ @@ -257,45 +243,36 @@ mutate(new_episode = is_new_episode(date, 365)) %>% select(patient, date, condition, new_episode) } -#> # A tibble: 200 × 4 -#> # Groups: condition [3] -#> patient date condition new_episode -#> <chr> <date> <chr> <lgl> -#> 1 277241 2005-08-31 A FALSE -#> 2 A86006 2009-08-09 C FALSE -#> 3 16DC39 2015-11-19 A FALSE -#> 4 F68330 2009-04-09 A FALSE -#> 5 A79917 2016-05-21 B FALSE -#> 6 A80D37 2009-01-19 A FALSE -#> 7 335263 2015-04-30 B FALSE -#> 8 257844 2011-05-22 B FALSE -#> 9 B8F499 2004-08-22 B FALSE -#> 10 F35553 2004-02-28 A FALSE -#> # … with 190 more rows +#> Error in mutate(., condition = sample(x = c("A", "B", "C"), size = 200, replace = TRUE)): ℹ In argument: `condition = sample(x = c("A", "B", "C"), size = 200, +#> replace = TRUE)`. +#> Caused by error: +#> ! `condition` must be size 100 or 1, not 200. + if (require("dplyr")) { df %>% group_by(ward, patient) %>% - transmute(date, + transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) } -#> # A tibble: 200 × 5 -#> # Groups: ward, patient [183] +#> # A tibble: 100 × 5 +#> # Groups: ward, patient [96] #> ward date patient new_index new_logical #> <chr> <date> <chr> <dbl> <lgl> -#> 1 ICU 2005-08-31 277241 1 TRUE -#> 2 Clinical 2009-08-09 A86006 1 TRUE -#> 3 ICU 2015-11-19 16DC39 1 TRUE -#> 4 Clinical 2009-04-09 F68330 1 TRUE -#> 5 Clinical 2016-05-21 A79917 1 TRUE -#> 6 Clinical 2009-01-19 A80D37 1 TRUE -#> 7 Clinical 2015-04-30 335263 1 TRUE -#> 8 Clinical 2011-05-22 257844 1 TRUE -#> 9 Clinical 2004-08-22 B8F499 1 TRUE -#> 10 ICU 2004-02-28 F35553 2 TRUE -#> # … with 190 more rows +#> 1 Clinical 2009-12-30 6D2377 1 TRUE +#> 2 Clinical 2010-04-05 192353 1 TRUE +#> 3 Clinical 2004-02-02 136315 1 TRUE +#> 4 ICU 2004-09-22 F35553 1 TRUE +#> 5 ICU 2004-11-03 D65308 1 TRUE +#> 6 Clinical 2015-08-14 A84726 1 TRUE +#> 7 Clinical 2011-04-25 023456 1 TRUE +#> 8 Clinical 2004-03-03 1435C8 1 TRUE +#> 9 Clinical 2006-05-26 54890C 1 TRUE +#> 10 ICU 2016-01-28 845227 1 TRUE +#> # … with 90 more rows + if (require("dplyr")) { df %>% group_by(ward) %>% @@ -309,50 +286,33 @@ #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> <chr> <int> <int> <int> <int> -#> 1 Clinical 114 15 53 73 -#> 2 ICU 62 12 38 46 -#> 3 Outpatient 7 6 7 7 -if (require("dplyr")) { - # grouping on patients and microorganisms leads to the same - # results as first_isolate() when using 'episode-based': - x <- df %>% - filter_first_isolate( - include_unknown = TRUE, - method = "episode-based" - ) +#> 1 Clinical 61 2 1 1 +#> 2 ICU 31 6 2 1 +#> 3 Outpatient 4 2 1 1 - y <- df %>% - group_by(patient, mo) %>% - filter(is_new_episode(date, 365)) %>% - ungroup() - - identical(x, y) -} -#> Including isolates from ICU. -#> [1] FALSE if (require("dplyr")) { - # but is_new_episode() has a lot more flexibility than first_isolate(), - # since you can now group on anything that seems relevant: + # is_new_episode() has a lot more flexibility than first_isolate(), + # since you can group on anything that seems relevant: df %>% group_by(patient, mo, ward) %>% mutate(flag_episode = is_new_episode(date, 365)) %>% select(group_vars(.), flag_episode) } -#> # A tibble: 200 × 4 -#> # Groups: patient, mo, ward [189] -#> patient mo ward flag_episode -#> <chr> <mo> <chr> <lgl> -#> 1 277241 B_STPHY_AURS ICU TRUE -#> 2 A86006 B_STPHY_CONS Clinical TRUE -#> 3 16DC39 B_STPHY_HMLY ICU TRUE -#> 4 F68330 B_STRPT_PNMN Clinical TRUE -#> 5 A79917 B_ENTRC_FACM Clinical TRUE -#> 6 A80D37 B_ESCHR_COLI Clinical TRUE -#> 7 335263 B_ENTRBC_CLOC Clinical TRUE -#> 8 257844 B_STPHY_CONS Clinical TRUE -#> 9 B8F499 B_STPHY_CONS Clinical TRUE -#> 10 F35553 B_STPHY_AURS ICU TRUE -#> # … with 190 more rows +#> # A tibble: 100 × 4 +#> # Groups: patient, mo, ward [98] +#> patient mo ward flag_episode +#> <chr> <mo> <chr> <lgl> +#> 1 6D2377 B_FSBCT Clinical TRUE +#> 2 192353 B_STRPT_PNMN Clinical TRUE +#> 3 136315 B_STRPT Clinical TRUE +#> 4 F35553 B_STRPT_ORLS ICU TRUE +#> 5 D65308 B_STPHY_EPDR ICU TRUE +#> 6 A84726 B_STPHY_HMNS Clinical TRUE +#> 7 023456 B_PROTS_MRBL Clinical TRUE +#> 8 1435C8 B_ESCHR_COLI Clinical TRUE +#> 9 54890C B_ESCHR_COLI Clinical TRUE +#> 10 845227 B_STRPT_PNMN ICU TRUE +#> # … with 90 more rows # } diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 428654615..839d2ba6f 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -318,152 +323,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/ggplot_sir-8.png b/reference/ggplot_sir-8.png index 8daa19d00..5b10bb883 100644 Binary files a/reference/ggplot_sir-8.png and b/reference/ggplot_sir-8.png differ diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 3674610f4..667fa2887 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -391,9 +396,8 @@ select(age_group, CIP) %>% ggplot_sir(x = "age_group") } -#> Including isolates from ICU. -#> Warning: Removed 6 rows containing missing values (`position_stack()`). -#> Warning: Removed 6 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). if (require("ggplot2") && require("dplyr")) { # a shorter version which also adjusts data label colours: diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 80103ab93..bddb62312 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/index.html b/reference/index.html index 3a20b446b..ac5d0b75c 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -241,7 +246,7 @@ Analysing data: antimicrobial resistance - Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). + Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. With antibiogram(), you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination antibiogram(WISCA). This function also comes with support for R Markdown and Quarto. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). @@ -251,9 +256,14 @@ + antibiogram() plot(<antibiogram>) autoplot(<antibiogram>) print(<antibiogram>) + + Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) + + resistance() susceptibility() sir_confidence_interval() proportion_R() proportion_IR() proportion_I() proportion_SI() proportion_S() proportion_df() sir_df() - Calculate Microbial Resistance + Calculate Antimicrobial Resistance count_resistant() count_susceptible() count_R() count_IR() count_I() count_SI() count_S() count_all() n_sir() count_df() @@ -376,17 +386,17 @@ example_isolates - Data Set with 2,000 Example Isolates + Data Set with 2 000 Example Isolates microorganisms - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms microorganisms.codes - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes antibiotics antivirals diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index e783f702a..fef9e64a3 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -148,7 +153,7 @@ Format - A tibble with 134,634 observations and 2 variables:mo Microorganism ID + A tibble with 134 634 observations and 2 variables:mo Microorganism ID ab Antibiotic ID diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index f064f2185..9cee16b62 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/join.html b/reference/join.html index 25a516dfd..4dcd176e4 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index ed0d93bad..2168637aa 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,7 +182,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). universal @@ -289,9 +294,7 @@ sum(my_patients$first_regular, na.rm = TRUE) sum(my_patients$first_weighted, na.rm = TRUE) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> [1] 1395 +#> [1] 1984 # } diff --git a/reference/kurtosis.html b/reference/kurtosis.html index e20a8d946..36bf8d83b 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,9 +182,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 2.880473 +#> [1] 2.938161 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.007374585 +#> [1] 0.0159802 + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mdro.html b/reference/mdro.html index 1629593e0..6a9222333 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -183,7 +188,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). info @@ -347,7 +352,10 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> (16 isolates had no test results) -#> Warning: in mdro(): NA introduced for isolates where the available percentage of +#> Warning: There was 1 warning in `mutate()`. +#> ℹ In argument: `MDRO = mdro()`. +#> Caused by warning: +#> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 385f62672..6b9a76860 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -196,31 +201,31 @@ sir <- random_sir(10) sir #> Class 'sir' -#> [1] S I R I S R R I R S +#> [1] R S R R S R I S I R mean_amr_distance(sir) -#> [1] -0.7745967 -0.7745967 1.1618950 -0.7745967 -0.7745967 1.1618950 -#> [7] 1.1618950 -0.7745967 1.1618950 -0.7745967 +#> [1] 0.9486833 -0.9486833 0.9486833 0.9486833 -0.9486833 0.9486833 +#> [7] -0.9486833 -0.9486833 -0.9486833 0.9486833 mic <- random_mic(10) mic #> Class 'mic' -#> [1] 0.5 0.005 4 0.005 4 16 8 >=128 <=0.002 -#> [10] 0.125 +#> [1] <=0.001 8 2 <=0.001 16 <=0.001 0.25 128 0.125 +#> [10] <=0.001 mean_amr_distance(mic) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 disk <- random_disk(10) disk #> Class 'disk' -#> [1] 8 32 43 15 44 37 16 14 29 29 +#> [1] 38 7 36 29 48 15 25 50 50 39 mean_amr_distance(disk) -#> [1] -1.4621577 0.4144083 1.2745011 -0.9148259 1.3526913 0.8053596 -#> [7] -0.8366357 -0.9930162 0.1798376 0.1798376 +#> [1] 0.2922699 -1.8147922 0.1563304 -0.3194578 0.9719673 -1.2710342 +#> [7] -0.5913368 1.1079068 1.1079068 0.3602396 y <- data.frame( id = LETTERS[1:10], @@ -230,36 +235,26 @@ tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") ) y -#> id amox cipr gent tobr -#> 1 A I 25 16 0.25 -#> 2 B I 30 16 0.25 -#> 3 C I 28 0.5 8 -#> 4 D I 26 16 0.25 -#> 5 E S 26 8 0.5 -#> 6 F R 21 16 0.25 -#> 7 G S 19 2 2 -#> 8 H I 27 8 1 -#> 9 I R 19 8 1 -#> 10 J R 28 8 4 +#> id amox cipr gent tobr +#> 1 A S 26 <=0.5 0.5 +#> 2 B I 20 >=16 2 +#> 3 C R 22 1 0.5 +#> 4 D S 25 >=16 >=8 +#> 5 E I 18 >=16 0.5 +#> 6 F S 18 <=0.5 <=0.25 +#> 7 G S 19 2 <=0.25 +#> 8 H S 21 8 0.5 +#> 9 I R 25 1 <=0.25 +#> 10 J S 25 <=0.5 <=0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> [1] -0.19598774 0.12451337 -0.09209741 -0.13188752 -0.14956959 0.06516053 -#> [7] -0.63363528 0.05123363 0.05598101 0.90628900 -y$amr_distance <- mean_amr_distance(y, where(is.mic)) -#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr" +#> [1] -0.08308833 0.31310698 0.30459906 1.01959980 -0.15475222 -0.87382971 +#> [7] -0.56840820 -0.02905708 0.38884763 -0.31701792 +y$amr_distance <- mean_amr_distance(y, where(is.mic)) +#> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] -#> id amox cipr gent tobr amr_distance -#> 3 C I 28 0.5 8 -0.27108669 -#> 7 G S 19 2 2 -0.20035843 -#> 5 E S 26 8 0.5 -0.12963016 -#> 1 A I 25 16 0.25 -0.09426603 -#> 2 B I 30 16 0.25 -0.09426603 -#> 4 D I 26 16 0.25 -0.09426603 -#> 6 F R 21 16 0.25 -0.09426603 -#> 8 H I 27 8 1 0.14377582 -#> 9 I R 19 8 1 0.14377582 -#> 10 J R 28 8 4 0.69058778 +#> Error in order(y$amr_distance): argument 1 is not a vector if (require("dplyr")) { y %>% @@ -271,17 +266,17 @@ } #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> id amox cipr gent tobr amr_distance check_id_C -#> 1 C I 28 0.5 8 -0.09209741 0.00000000 -#> 2 D I 26 16 0.25 -0.13188752 0.03979011 -#> 3 E S 26 8 0.5 -0.14956959 0.05747218 -#> 4 A I 25 16 0.25 -0.19598774 0.10389034 -#> 5 H I 27 8 1 0.05123363 0.14333103 -#> 6 I R 19 8 1 0.05598101 0.14807842 -#> 7 F R 21 16 0.25 0.06516053 0.15725794 -#> 8 B I 30 16 0.25 0.12451337 0.21661078 -#> 9 G S 19 2 2 -0.63363528 0.54153787 -#> 10 J R 28 8 4 0.90628900 0.99838641 +#> id amox cipr gent tobr amr_distance check_id_C +#> 1 C R 22 1 0.5 0.30459906 0.000000000 +#> 2 B I 20 >=16 2 0.31310698 0.008507921 +#> 3 I R 25 1 <=0.25 0.38884763 0.084248568 +#> 4 H S 21 8 0.5 -0.02905708 0.333656139 +#> 5 A S 26 <=0.5 0.5 -0.08308833 0.387687383 +#> 6 E I 18 >=16 0.5 -0.15475222 0.459351276 +#> 7 J S 25 <=0.5 <=0.25 -0.31701792 0.621616981 +#> 8 D S 25 >=16 >=8 1.01959980 0.715000742 +#> 9 G S 19 2 <=0.25 -0.56840820 0.873007255 +#> 10 F S 18 <=0.5 <=0.25 -0.87382971 1.178428772 if (require("dplyr")) { # support for groups example_isolates %>% diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 944c454cc..e71b9798c 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -1,5 +1,5 @@ -Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5 910 Common Microorganism Codes — microorganisms.codes • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes Source: R/data.R microorganisms.codes.Rd @@ -148,7 +153,7 @@ Format - A tibble with 5,910 observations and 2 variables:code Commonly used code of a microorganism + A tibble with 5 910 observations and 2 variables:code Commonly used code of a microorganism mo ID of the microorganism in the microorganisms data set diff --git a/reference/microorganisms.html b/reference/microorganisms.html index aea7321a9..ce5728cf0 100644 --- a/reference/microorganisms.html +++ b/reference/microorganisms.html @@ -1,5 +1,5 @@ -Data Set with 52,141 Microorganisms — microorganisms • AMR (for R)Data Set with 52 142 Microorganisms — microorganisms • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms Source: R/data.R microorganisms.Rd @@ -148,7 +153,7 @@ Format - A tibble with 52,141 observations and 22 variables:mo ID of microorganism as used by this package + A tibble with 52 142 observations and 22 variables:mo ID of microorganism as used by this package fullname Full name, like "Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. status Status of the taxon, either "accepted" or "synonym" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank of the microorganism @@ -231,20 +236,20 @@ Examples microorganisms -#> # A tibble: 52,141 × 22 +#> # A tibble: 52,142 × 22 #> mo fullname status kingdom phylum class order family genus #> <mo> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… -#> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 1 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 2 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 3 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 4 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… "(un… "(un… "(unk… "(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "Abdi… "" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "" "" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… "Abd… "" "" "" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "" "" "" "" -#> # … with 52,131 more rows, and 13 more variables: species <chr>, +#> # … with 52,132 more rows, and 13 more variables: species <chr>, #> # subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> # lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> # gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 6f8cccad9..f9d933796 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mo_property.html b/reference/mo_property.html index 19a8773cb..c2a62e79c 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -400,7 +405,7 @@ property -one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname" +one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed" @@ -506,8 +511,10 @@ mo_gramstain("Klebsiella pneumoniae") #> [1] "Gram-negative" mo_snomed("Klebsiella pneumoniae") -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [[1]] +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" +#> mo_type("Klebsiella pneumoniae") #> [1] "Bacteria" mo_rank("Klebsiella pneumoniae") @@ -748,8 +755,8 @@ #> [1] "Trevisan, 1887" #> #> $snomed -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" #> # } diff --git a/reference/mo_source.html b/reference/mo_source.html index 7f67d7ff8..5dae97f8f 100644 --- a/reference/mo_source.html +++ b/reference/mo_source.html @@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ This is the fastest way to have your organisation (or analysis) specific codes p Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/pca.html b/reference/pca.html index ab16b25c1..f67028628 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,152 +256,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/plot-1.png b/reference/plot-1.png index c7f0a0e23..7b06b86c3 100644 Binary files a/reference/plot-1.png and b/reference/plot-1.png differ diff --git a/reference/plot-2.png b/reference/plot-2.png index 4da7bb826..05d760db2 100644 Binary files a/reference/plot-2.png and b/reference/plot-2.png differ diff --git a/reference/plot-3.png b/reference/plot-3.png index 9dc828c91..8f7ea9a35 100644 Binary files a/reference/plot-3.png and b/reference/plot-3.png differ diff --git a/reference/plot-4.png b/reference/plot-4.png index 76abf039b..3c8519ae8 100644 Binary files a/reference/plot-4.png and b/reference/plot-4.png differ diff --git a/reference/plot-5.png b/reference/plot-5.png index be91a6a92..9d0b334fd 100644 Binary files a/reference/plot-5.png and b/reference/plot-5.png differ diff --git a/reference/plot-6.png b/reference/plot-6.png index 59a1b63fb..7de420901 100644 Binary files a/reference/plot-6.png and b/reference/plot-6.png differ diff --git a/reference/plot-7.png b/reference/plot-7.png index ea5d1e4fc..9fc466395 100644 Binary files a/reference/plot-7.png and b/reference/plot-7.png differ diff --git a/reference/plot-8.png b/reference/plot-8.png index 94cc6370a..eac02c0ad 100644 Binary files a/reference/plot-8.png and b/reference/plot-8.png differ diff --git a/reference/plot-9.png b/reference/plot-9.png index acefed1df..991774f96 100644 Binary files a/reference/plot-9.png and b/reference/plot-9.png differ diff --git a/reference/plot.html b/reference/plot.html index 4aa05522a..d3c4b1f2f 100644 --- a/reference/plot.html +++ b/reference/plot.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/proportion.html b/reference/proportion.html index a3578b281..ee2c1affd 100644 --- a/reference/proportion.html +++ b/reference/proportion.html @@ -1,6 +1,6 @@ Calculate Microbial Resistance — proportion • AMR (for R)Calculate Antimicrobial Resistance — proportion • AMR (for R) subspecies, a 3-5 letter acronym | | \\----> species, a 3-6 letter acronym | \\----> genus, a 4-8 letter acronym \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #> 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #> will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...) NA_sir_ is.sir(x) is_sir_eligible(x, threshold = 0.05) # S3 method for mic as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for disk as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for data.frame as.sir( x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) ) sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. Can also set option AMR_include_PKPD. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF summary(example_isolates) # see all SIR results at a glance #> date patient age gender #> Min. :2002-01-02 Length:2000 Min. : 0.00 Length:2000 #> 1st Qu.:2005-07-31 Class :character 1st Qu.:63.00 Class :character #> Median :2009-07-31 Mode :character Median :74.00 Mode :character #> Mean :2009-11-20 Mean :70.69 #> 3rd Qu.:2014-05-30 3rd Qu.:82.00 #> Max. :2017-12-28 Max. :97.00 #> ward mo PEN #> Length:2000 Class :mo Class:sir #> Class :character :0 %R :73.7% (n=1201) #> Mode :character Unique:90 %SI :26.3% (n=428) #> #1 :B_ESCHR_COLI - %S :25.6% (n=417) #> #2 :B_STPHY_CONS - %I : 0.7% (n=11) #> #3 :B_STPHY_AURS #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %SI :68.8% (n=251) %SI :70.5% (n=665) %SI :40.4% (n=546) #> - %S :68.8% (n=251) - %S :70.5% (n=665) - %S :40.2% (n=543) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.2% (n=3) #> #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %SI :76.3% (n=1433) %SI :40.4% (n=546) %SI :87.4% (n=875) #> - %S :71.4% (n=1342) - %S :40.2% (n=543) - %S :86.1% (n=862) #> - %I : 4.8% (n=91) - %I : 0.2% (n=3) - %I : 1.3% (n=13) #> #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %SI :55.4% (n=247) %SI :85.8% (n=621) %SI :73.7% (n=1319) #> - %S :54.9% (n=245) - %S :85.6% (n=620) - %S :72.5% (n=1297) #> - %I : 0.4% (n=2) - %I : 0.1% (n=1) - %I : 1.2% (n=22) #> #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %SI :72.6% (n=594) %SI :84.5% (n=797) %SI :33.5% (n=607) #> - %S :71.6% (n=586) - %S :84.4% (n=796) - %S :33.5% (n=607) #> - %I : 1.0% (n=8) - %I : 0.1% (n=1) - %I : 0.0% (n=0) #> #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %SI :84.5% (n=797) %SI :75.4% (n=1399) %SI :65.6% (n=886) #> - %S :84.4% (n=796) - %S :74.0% (n=1372) - %S :65.1% (n=879) #> - %I : 0.1% (n=1) - %I : 1.5% (n=27) - %I : 0.5% (n=7) #> #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %SI :36.3% (n=251) %SI : 0.0% (n=0) %SI :61.9% (n=928) #> - %S :36.3% (n=251) - %S : 0.0% (n=0) - %S :61.2% (n=918) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.7% (n=10) #> #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %SI :79.5% (n=1398) %SI :82.9% (n=616) %SI :57.8% (n=203) #> - %S :79.1% (n=1392) - %S :76.0% (n=565) - %S :57.8% (n=203) #> - %I : 0.3% (n=6) - %I : 6.9% (n=51) - %I : 0.0% (n=0) #> #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %SI :30.7% (n=314) %SI :83.8% (n=1181) %SI :66.4% (n=140) #> - %S :30.7% (n=314) - %S :78.9% (n=1112) - %S :64.5% (n=136) #> - %I : 0.0% (n=0) - %I : 4.9% (n=69) - %I : 1.9% (n=4) #> #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %SI :61.7% (n=1149) %SI :24.3% (n=237) %SI :70.3% (n=843) #> - %S :61.7% (n=1149) - %S :24.3% (n=237) - %S :68.3% (n=820) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 1.9% (n=23) #> #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %SI :87.3% (n=697) %SI :72.3% (n=821) %SI :42.8% (n=810) #> - %S :87.3% (n=697) - %S :71.7% (n=814) - %S :42.3% (n=801) #> - %I : 0.0% (n=0) - %I : 0.6% (n=7) - %I : 0.5% (n=9) #> #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %SI :38.8% (n=590) %SI :42.8% (n=810) %SI :93.8% (n=834) #> - %S :38.6% (n=586) - %S :42.3% (n=801) - %S :92.7% (n=824) #> - %I : 0.3% (n=4) - %I : 0.5% (n=9) - %I : 1.1% (n=10) #> #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %SI :94.1% (n=780) %SI :85.3% (n=29) %SI :78.6% (n=121) #> - %S :94.1% (n=780) - %S :85.3% (n=29) - %S :78.6% (n=121) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0) #> #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %SI :18.8% (n=309) %SI :94.1% (n=254) %SI :30.4% (n=305) #> - %S :18.8% (n=309) - %S :93.0% (n=251) - %S :30.2% (n=303) #> - %I : 0.0% (n=0) - %I : 1.1% (n=3) - %I : 0.2% (n=2) #> # For INTERPRETING disk diffusion and MIC values ----------------------- # a whole data set, even with combined MIC values and disk zones df <- data.frame( microorganism = \"Escherichia coli\", AMP = as.mic(8), CIP = as.mic(0.256), GEN = as.disk(18), TOB = as.disk(16), ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #> datetime index ab_input ab_guid…¹ mo_in…² mo_guideline guide…³ #> #> 1 2023-01-24 15:36:22 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… #> 2 2023-01-24 15:36:22 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… #> 3 2023-01-24 15:36:21 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 4 2023-01-24 15:36:21 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 5 2023-01-24 15:36:15 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 6 2023-01-24 15:36:15 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 7 2023-01-24 15:36:15 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 8 2023-01-24 15:36:15 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 9 2023-01-24 15:36:15 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-24 15:36:15 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table , method , #> # input , outcome , breakpoint_S_R , and abbreviated variable #> # names ¹ab_guideline, ²mo_input, ³guideline # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> Note: #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #> pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'sir' #> [1] R # \\donttest{ # the dplyr way if (require(\"dplyr\")) { df %>% mutate_if(is.mic, as.sir) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df %>% mutate(across(where(is.mic), as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir) df %>% mutate(across(AMP:TOB, as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df %>% mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S I R as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages barplot(sir_data) # for frequencies # the dplyr way if (require(\"dplyr\")) { example_isolates %>% mutate_at(vars(PEN:RIF), as.sir) # same: example_isolates %>% as.sir(PEN:RIF) # fastest way to transform all columns with already valid AMR results to class `sir`: example_isolates %>% mutate_if(is_sir_eligible, as.sir) # since dplyr 1.0.0, this can also be: # example_isolates %>% # mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property( atc_code, property, administration = \"O\", url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" ) atc_online_groups(atc_code, ...) atc_online_ddd(atc_code, ...) atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) { # oral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"O\") atc_online_ddd(ab_atc(\"amox\")) # parenteral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"P\") atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\" #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\" #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\" # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_av = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #> av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...) av_cid(x, ...) av_synonyms(x, ...) av_tradenames(x, ...) av_group(x, language = get_AMR_locale(), ...) av_atc(x, ...) av_loinc(x, ...) av_ddd(x, administration = \"oral\", ...) av_ddd_units(x, administration = \"oral\", ...) av_info(x, language = get_AMR_locale(), ...) av_url(x, open = FALSE, ...) av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_tradenames(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #> Aciclovir #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...) # S3 method for bug_drug_combinations format( x, translate_ab = \"name (ab, atc)\", language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark == \",\", \".\", \",\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric decimal point. big.mark character; empty used mark every big.interval decimals (hence big) decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #> mo ab S I R total #> #> 1 (unknown species) PEN 14 0 1 15 #> 2 (unknown species) OXA 0 0 1 1 #> 3 (unknown species) FLC 0 0 0 0 #> 4 (unknown species) AMX 15 0 1 16 #> 5 (unknown species) AMC 15 0 0 15 #> 6 (unknown species) AMP 15 0 1 16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #> #> 1 \"Aminogl… Amik… \"100… \" 0.0… \"100.0… \"\" \"\" \"\" \"\" \"100.0… #> 2 \"\" Gent… \" 13… \" 2.0… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 0.9… \" 21.5… #> 3 \"\" Kana… \"100… \"\" \"100.0… \"\" \"100.0… \"\" \"\" \"100.0… #> 4 \"\" Tobr… \" 78… \" 2.6… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 2.3… \" 49.4… #> 5 \"Ampheni… Chlo… \"\" \"\" \"\" \"\" \"100.0… \"\" \" 0.0… \" 3.1… #> 6 \"Antimyc… Rifa… \"\" \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 0.0… \" 2.7… #> 7 \"Beta-la… Amox… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 8 \"\" Amox… \" 42… \" 13.1… \"\" \" 10.3… \"100.0… \" 2.8… \" 0.4… \" 54.5… #> 9 \"\" Ampi… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 10 \"\" Benz… \" 77… \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` , #> # `S. pneumoniae` , and abbreviated variable names ¹`E. coli`, #> # ²`E. faecalis`, ³`K. pneumoniae`, ⁴`P. aeruginosa`, ⁵`P. mirabilis`, #> # ⁶`S. aureus`, ⁷`S. epidermidis` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 Gram-negative PEN 8 0 717 725 #> 2 Gram-negative OXA 6 0 0 6 #> 3 Gram-negative FLC 6 0 0 6 #> 4 Gram-negative AMX 226 0 405 631 #> 5 Gram-negative AMC 463 89 174 726 #> 6 Gram-negative AMP 226 0 405 631 #> 7 Gram-negative TZP 554 11 76 641 #> 8 Gram-negative CZO 94 2 110 206 #> 9 Gram-negative FEP 470 1 14 485 #> 10 Gram-negative CXM 539 22 142 703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 E. coli PEN 0 0 467 467 #> 2 E. coli OXA 0 0 0 0 #> 3 E. coli FLC 0 0 0 0 #> 4 E. coli AMX 196 0 196 392 #> 5 E. coli AMC 332 74 61 467 #> 6 E. coli AMP 196 0 196 392 #> 7 E. coli TZP 388 5 23 416 #> 8 E. coli CZO 79 1 2 82 #> 9 E. coli FEP 308 0 9 317 #> 10 E. coli CXM 425 15 25 465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #> guideline method site mo rank_…¹ ab ref_tbl disk_…² break…³ #> #> 1 EUCAST 2… MIC NA F_ASPRG_MGTS 2 AMB Aspergi… NA 1 #> 2 EUCAST 2… MIC NA F_ASPRG_NIGR 2 AMB Aspergi… NA 1 #> 3 EUCAST 2… MIC NA F_CANDD_ALBC 2 AMB Candida NA 1 #> 4 EUCAST 2… MIC NA F_CANDD_DBLN 2 AMB Candida NA 1 #> 5 EUCAST 2… MIC NA F_CANDD_GLBR 2 AMB Candida NA 1 #> 6 EUCAST 2… MIC NA F_CANDD_KRUS 2 AMB Candida NA 1 #> 7 EUCAST 2… MIC NA F_CANDD_PRPS 2 AMB Candida NA 1 #> 8 EUCAST 2… MIC NA F_CANDD_TRPC 2 AMB Candida NA 1 #> 9 EUCAST 2… MIC NA F_CRYPT_NFRM 2 AMB Candida NA 1 #> 10 EUCAST 2… DISK NA B_[ORD]_ENTRBCTR 5 AMC Enterob… 20ug/1… 19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R , uti , and #> # abbreviated variable names ¹rank_index, ²disk_dose, ³breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_sir(..., only_all_tested = FALSE) count_df( data, translate_ab = \"name\", language = get_AMR_locale(), combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350 # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804 # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350 # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_sir(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Number of available isolates for a whole antibiotic class # (i.e., in this data set columns GEN, TOB, AMK, KAN) example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), n_sir)) # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #> ward antibiotic interpretation value #> * #> 1 Clinical AMX SI 357 #> 2 Clinical AMX R 487 #> 3 Clinical CIP SI 741 #> 4 Clinical CIP R 128 #> 5 ICU AMX SI 158 #> 6 ICU AMX R 270 #> 7 ICU CIP SI 362 #> 8 ICU CIP R 85 #> 9 Outpatient AMX SI 31 #> 10 Outpatient AMX R 47 #> 11 Outpatient CIP SI 78 #> 12 Outpatient CIP R 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work: rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\", TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"S\" then set to S : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If TZP is \"R\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"), TZP = as.sir(\"R\"), ampi = as.sir(\"S\"), cipro = as.sir(\"S\")) df #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R S S eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\", TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\") eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules( AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_…¹ rule_…² rule_…³ #> #> 1 33 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 2 33 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 3 34 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 4 34 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 5 531 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 6 531 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 7 1485 AMP Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> 8 1485 AMX Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹rule_group, ²rule_name, ³rule_source # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is \"R\" then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #> ab name type dose dose_…¹ admin…² notes origi…³ eucas…⁴ #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 … 12 #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x … 12 #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x … 12 #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1… 12 #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g … 12 #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g … 12 #> 7 AMC Amoxicillin/clavulani… high… 2 g … 3 iv \"\" (2 g a… 12 #> 8 AMC Amoxicillin/clavulani… stan… 1 g … 3 iv \"\" (1 g a… 12 #> 9 AMC Amoxicillin/clavulani… high… 0.87… 3 oral \"\" (0.875… 12 #> 10 AMC Amoxicillin/clavulani… stan… 0.5 … 3 oral \"\" (0.5 g… 12 #> # … with 326 more rows, and abbreviated variable names ¹dose_times, #> # ²administration, ³original_txt, ⁴eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules( x, col_mo = NULL, info = interactive(), rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")), verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, ... ) eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\". EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv. Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylpenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) head(a) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - - - - - S S #> 2 Enterococcus faecalis - - - - - S S #> 3 Escherichia coli - - - - - S S #> 4 Klebsiella pneumoniae - - - - - S S #> 5 Pseudomonas aeruginosa - - - - - S S # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R S S S S #> 2 Enterococcus faecalis - - R R R S R #> 3 Escherichia coli R - - - - R S #> 4 Klebsiella pneumoniae R R - - - R S #> 5 Pseudomonas aeruginosa R R - - R R R # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #> row col mo_fullname old new rule rule_group #> 1 1 AMX Staphylococcus aureus - S Breakpoints #> 2 1 CXM Staphylococcus aureus - S Breakpoints #> 3 1 CAZ Staphylococcus aureus R S Expert Rules #> 4 1 CAZ Staphylococcus aureus - R Expert Rules #> 5 1 COL Staphylococcus aureus - R Expert Rules #> 6 2 CAZ Enterococcus faecalis - R Expert Rules #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # } # Dosage guidelines: eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #>
vignettes/SPSS.Rmd
SPSS.Rmd
R has a huge community.
Many R users just ask questions on websites like StackOverflow.com, the largest -online community for programmers. At the time of writing, 478,396 +online community for programmers. At the time of writing, 480,015 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of diff --git a/articles/WHONET.html b/articles/WHONET.html index 0ab21a9be..4a0690d24 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -71,6 +71,11 @@ Conduct AMR Analysis
vignettes/datasets.Rmd
datasets.Rmd
microorganisms
A data set with 52,141 rows and 22 columns, containing the following +
A data set with 52,142 rows and 22 columns, containing the following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, @@ -201,7 +206,7 @@ column names:mo, fullname, status, kingdomgbif_renamed_to, prevalence and snomed.
This data set is in R available as microorganisms, after you load the AMR package.
AMR
It was last updated on 20 December 2022 15:14:04 UTC. Find more info +
It was last updated on 6 February 2023 10:57:22 UTC. Find more info about the structure of this data set here.
Direct download links:
This base R snippet will work in any version of R since April 2013 (R-3.0).
The AMR package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA).
If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
+antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems()))
antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems()))
In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
+antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain")
antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain")
+ library(AMR) library(dplyr) @@ -388,7 +532,7 @@
library(AMR) library(dplyr) @@ -388,7 +532,7 @@
+ # transform the antibiotic columns to names: out %>% set_ab_names()
# transform the antibiotic columns to names: out %>% set_ab_names()
+ # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = "atc")
# transform the antibiotic column to ATC codes: out %>% set_ab_names(property = "atc")
This package is available here on the official R network (CRAN). Install this package in R from CRAN by using the command:
+ install.packages("AMR") It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install. Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version. @@ -529,13 +673,13 @@ Manually, using: - + install.packages("remotes") # if you haven't already remotes::install_github("msberends/AMR") Automatically, using the rOpenSci R-universe platform, by adding our R-universe address to your list of repositories (‘repos’): - + options(repos = c(getOption("repos"), msberends = "https://msberends.r-universe.dev")) After this, you can install and update this AMR package like any official release (e.g., using install.packages("AMR") or in RStudio via Tools > Check for Package Updates…). diff --git a/news/index.html b/news/index.html index 3e94fffa3..6bf49017e 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -137,39 +142,46 @@ -AMR 1.8.2.9103 +AMR 1.8.2.9111 (this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!) This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below. TL;DR All functions and arguments with ‘rsi’ were replaced with ‘sir’, such as the interpretation of MIC values (now as.sir() instead of as.rsi()) - all old functions still work for now +Many new interesting functions, such as antibiogram() (for generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval() and mean_amr_distance(), and add_custom_microorganisms() to add custom microorganisms to this package +Clinical breakpoints added for EUCAST 2022 and CLSI 2022 Microbiological taxonomy (microorganisms data set) updated to 2022 and now based on LPSN and GBIF Much increased algorithms to translate user input to valid taxonomy, e.g. by using recent scientific work about per-species human pathogenicity -Clinical breakpoints added for EUCAST 2022 and CLSI 2022 20 new antibiotics added and updated all DDDs and ATC codes Extended support for antiviral agents (antivirals data set), with many new functions Now available in 16 languages -Many new interesting functions, such as sir_confidence_interval() and mean_amr_distance(), and add_custom_microorganisms() to add custom microorganisms to this package Many small bug fixes -New +New -SIR vs. RSI +SIR vs. RSI For this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR. While existing functions such as as.rsi(), rsi_df() and ggplot_rsi() still work, their replacements as.sir(), sir_df(), ggplot_sir() are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set rsi_translation is now called clinical_breakpoints to better reflect its content. The ‘RSI functions’ will be removed in a future version, but not before late 2023 / early 2024. -Interpretation of MIC and disk diffusion values +New antibiogram function +Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). +With the new antibiogram() function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). +The help page for antibiogram() extensively elaborates on use cases, and antibiogram() also supports printing in R Markdown and Quarto, with support for 16 languages. +Furthermore, different plotting methods were implemented to allow for graphical visualisations as well. + + +Interpretation of MIC and disk diffusion values The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for as.sir(). EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules() to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline option, such as: options(AMR_guideline = "CLSI 2020"). Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013. -Supported languages +Supported languages We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language. We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 16 languages and according to download stats used in almost all countries in the world! -Microbiological taxonomy +Microbiological taxonomy The microorganisms data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022. We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score() on how their work was incorporated into the prevalence column of the microorganisms data set. Using their results, the as.mo() and all mo_*() functions are now much better capable of converting user input to valid taxonomic records. The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package. @@ -193,7 +205,7 @@ The microorganisms.old data set was removed, and all previously accepted names are now included in the microorganisms data set. A new column status contains "accepted" for currently accepted names and "synonym" for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier. -Antibiotic agents and selectors +Antibiotic agents and selectors The new function add_custom_antimicrobials() allows users to add custom antimicrobial codes and names to the AMR package. The antibiotics data set was greatly updated: The following 20 antibiotics have been added (also includes the new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), and thioacetazone (TAT) @@ -205,14 +217,14 @@ Also, we added support for using antibiotic selectors in scoped dplyr verbs (with or without using vars()), such as in: ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() for examples. -Antiviral agents +Antiviral agents We now added extensive support for antiviral agents! For the first time, the AMR package has extensive support for antiviral drugs and to work with their names, codes and other data in any way. The antivirals data set has been extended with 18 new drugs (also from the new J05AJ ATC group) and now also contains antiviral identifiers and LOINC codes A new data type av (antivirals) has been added, which is functionally similar to ab for antibiotics Functions as.av(), av_name(), av_atc(), av_synonyms(), av_from_text() have all been added as siblings to their ab_*() equivalents -Other new functions +Other new functions Function sir_confidence_interval() to add confidence intervals in AMR calculation. This is now also included in sir_df() and proportion_df(). Function mean_amr_distance() to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. Function sir_interpretation_history() to view the history of previous runs of as.sir() (previously as.rsi()). This returns a ‘logbook’ with the selected guideline, reference table and specific interpretation of each row in a data set on which as.sir() was run. @@ -221,10 +233,10 @@ -Changes +Changes Argument combine_IR has been removed from this package (affecting functions count_df(), proportion_df(), and sir_df() and some plotting functions), since it was replaced with combine_SI three years ago Using units in ab_ddd(..., units = "...") had been deprecated for some time and is now not supported anymore. Use ab_ddd_units() instead. -Support for data.frame-enhancing R packages, more specifically: data.table::data.table, janitor::tabyl, tibble::tibble, and tsibble::tsibble. AMR package functions that have a data set as output (such as sir_df() and bug_drug_combinations()), will now return the same data type as the input. +Support for data.frame-enhancing R packages, more specifically: data.table::data.table, janitor::tabyl, tibble::tibble, and tsibble::tsibble. AMR package functions that have a data set as output (such as sir_df() and bug_drug_combinations()), will now return the same data type as the input. All data sets in this package are now a tibble, instead of base R data.frames. Older R versions are still supported, even if they do not support tibbles. Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info in this article on our website. For as.sir(): @@ -258,9 +270,10 @@ Fix for mo_shortname() in case of higher taxonomic ranks (order, class, phylum) Cleaning columns with as.sir(), as.mic(), or as.disk() will now show the column name in the warning for invalid results +Fix for using g.test() with zeroes in a 2x2 table -Other +Other Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, and Jonas Salm as contributors, to thank them for their valuable input New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions All R and Rmd files in this project are now styled using the styler package diff --git a/pkgdown.yml b/pkgdown.yml index b57efb007..b95f326df 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2023-01-24T15:35Z +last_built: 2023-02-08T15:58Z urls: reference: https://msberends.github.io/AMR/reference article: https://msberends.github.io/AMR/articles diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html index b76bad45a..4c4ab36a1 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/AMR-options.html b/reference/AMR-options.html index ecf0b645d..44f9db297 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/AMR.html b/reference/AMR.html index 67843c0d2..1dcafe8b7 100644 --- a/reference/AMR.html +++ b/reference/AMR.html @@ -18,7 +18,7 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German, AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -50,6 +50,11 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German, Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/Rplot003.png b/reference/Rplot003.png index 1ed92087b..b470d2333 100644 Binary files a/reference/Rplot003.png and b/reference/Rplot003.png differ diff --git a/reference/Rplot005.png b/reference/Rplot005.png index cba65bc28..f7b60774c 100644 Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ diff --git a/reference/Rplot006.png b/reference/Rplot006.png index 0e487a53a..b913b5db2 100644 Binary files a/reference/Rplot006.png and b/reference/Rplot006.png differ diff --git a/reference/Rplot007.png b/reference/Rplot007.png index a9a3af1ea..5d1df79b2 100644 Binary files a/reference/Rplot007.png and b/reference/Rplot007.png differ diff --git a/reference/Rplot008.png b/reference/Rplot008.png index bce895010..4cdf8d40e 100644 Binary files a/reference/Rplot008.png and b/reference/Rplot008.png differ diff --git a/reference/Rplot009.png b/reference/Rplot009.png index 752cb97d5..b4a86c2f6 100644 Binary files a/reference/Rplot009.png and b/reference/Rplot009.png differ diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 68396f6a6..4d1cddd14 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/WHONET.html b/reference/WHONET.html index 4fc1f3bae..d936bc7a6 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 9146817c8..fe6fee3b8 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/ab_property.html b/reference/ab_property.html index 1bee0d0f8..46ca6bddf 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -469,22 +474,18 @@ # \donttest{ if (require("dplyr")) { example_isolates %>% - set_ab_names() %>% - head() + set_ab_names() # this does the same: example_isolates %>% - rename_with(set_ab_names) %>% - head() + rename_with(set_ab_names) # set_ab_names() works with any AB property: example_isolates %>% - set_ab_names(property = "atc") %>% - head() + set_ab_names(property = "atc") example_isolates %>% - set_ab_names(where(is.sir)) %>% - colnames() + set_ab_names(where(is.sir)) example_isolates %>% set_ab_names(NIT:VAN) %>% diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index b7b6f5b2a..54d640604 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 98aec5e8e..15620ca6f 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -260,7 +265,7 @@ #> [1] "Self-added, 2023" #> #> $snomed -#> character(0) +#> [1] NA #> diff --git a/reference/age.html b/reference/age.html index e296e4052..b812429ff 100644 --- a/reference/age.html +++ b/reference/age.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -200,16 +205,16 @@ df #> birth_date age age_exact age_at_y2k -#> 1 1941-04-16 81 81.77534 58 -#> 2 1966-08-03 56 56.47671 33 -#> 3 1980-02-04 42 42.96986 19 -#> 4 1967-03-18 55 55.85479 32 -#> 5 1947-05-25 75 75.66849 52 -#> 6 1948-08-19 74 74.43288 51 -#> 7 1933-07-04 89 89.55890 66 -#> 8 1954-12-03 68 68.14247 45 -#> 9 1956-07-25 66 66.50137 43 -#> 10 1998-12-10 24 24.12329 1 +#> 1 1980-09-24 42 42.37534 19 +#> 2 1957-01-21 66 66.04932 42 +#> 3 1935-11-17 87 87.22740 64 +#> 4 1945-08-15 77 77.48493 54 +#> 5 1986-04-06 36 36.84384 13 +#> 6 1952-04-20 70 70.80548 47 +#> 7 1985-09-08 37 37.41918 14 +#> 8 1993-09-17 29 29.39452 6 +#> 9 1947-07-04 75 75.60000 52 +#> 10 1943-09-17 79 79.39452 56 diff --git a/reference/antibiogram-1.png b/reference/antibiogram-1.png new file mode 100644 index 000000000..d4e50977b Binary files /dev/null and b/reference/antibiogram-1.png differ diff --git a/reference/antibiogram-2.png b/reference/antibiogram-2.png new file mode 100644 index 000000000..a1555e745 Binary files /dev/null and b/reference/antibiogram-2.png differ diff --git a/reference/antibiogram-3.png b/reference/antibiogram-3.png new file mode 100644 index 000000000..4f2284a5f Binary files /dev/null and b/reference/antibiogram-3.png differ diff --git a/reference/antibiogram-4.png b/reference/antibiogram-4.png new file mode 100644 index 000000000..0b1b9be7b Binary files /dev/null and b/reference/antibiogram-4.png differ diff --git a/reference/antibiogram.html b/reference/antibiogram.html new file mode 100644 index 000000000..ffc2d6348 --- /dev/null +++ b/reference/antibiogram.html @@ -0,0 +1,526 @@ + +Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram • AMR (for R) + Skip to contents + + + + + AMR (for R) + + 1.8.2.9111 + + + + + + + + + + + + Home + + + + + + + How to + + + + + + User- Or Team-specific Package Settings + + + + + Conduct AMR Analysis + + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + + + + + Predict Antimicrobial Resistance + + + + + Data Sets for Download / Own Use + + + + + Conduct Principal Component Analysis for AMR + + + + + Determine Multi-Drug Resistance (MDR) + + + + + Work with WHONET Data + + + + + Import Data From SPSS/SAS/Stata + + + + + Apply Eucast Rules + + + + + Get Taxonomy of a Microorganism + + + + + Get Properties of an Antibiotic Drug + + + + + Get Properties of an Antiviral Drug + + + + + + + + Manual + + + + + + + Authors + + + + + + + Changelog + + + + + + + + + + Source Code + + + + + + + + + + Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) + Source: R/antibiogram.R + antibiogram.Rd + + + + Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker et al. (2021, doi:10.1177/20499361211011373 +) and Barbieri et al. (2021, doi:10.1186/s13756-021-00939-2 +), and allow reporting in e.g. R Markdown and Quarto as well. + + + + Usage + antibiogram( + x, + antibiotics = where(is.sir), + mo_transform = "shortname", + ab_transform = NULL, + syndromic_group = NULL, + add_total_n = TRUE, + only_all_tested = FALSE, + digits = 0, + col_mo = NULL, + language = get_AMR_locale(), + minimum = 30, + combine_SI = TRUE, + sep = " + " +) + +# S3 method for antibiogram +plot(x, ...) + +# S3 method for antibiogram +autoplot(object, ...) + +# S3 method for antibiogram +print(x, as_kable = !interactive(), ...) + + + + Source + +Klinker KP et al. (2021). Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms. Therapeutic Advances in Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 +Barbieri E et al. (2021). Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 +M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. + + + Arguments + x +a data.frame containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see as.sir()) + + +antibiotics +vector of column names, or (any combinations of) antibiotic selectors such as aminoglycosides() or carbapenems(). For combination antibiograms, this can also be column names separated with "+", such as "TZP+TOB" given that the data set contains columns "TZP" and "TOB". See Examples. + + +mo_transform +a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed". Can also be NULL to not transform the input. + + +ab_transform +a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units" or "loinc". Can also be NULL to not transform the input. + + +syndromic_group +a column name of x, or values calculated to split rows of x, e.g. by using ifelse() or case_when(). See Examples. + + +add_total_n +a logical to indicate whether total available numbers per pathogen should be added to the table (defaults to TRUE). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for E. coli 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). + + +only_all_tested +(for combination antibiograms): a logical to indicate that isolates must be tested for all antibiotics, see Details + + +digits +number of digits to use for rounding + + +col_mo +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). + + +language +language to translate text, which defaults to the system language (see get_AMR_locale()) + + +minimum +the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. + + +combine_SI +a logical to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (defaults to TRUE) + + +sep +a separating character for antibiotic columns in combination antibiograms + + +... +method extensions + + +object +an antibiogram() object + + +as_kable +a logical to indicate whether the printing should be done using knitr::kable() (which is the default in non-interactive sessions) + + + + Details + This function returns a table with values between 0 and 100 for susceptibility, not resistance. +Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms. +There are four antibiogram types, as proposed by Klinker et al. (2021, doi:10.1177/20499361211011373 +), and they are all supported by antibiogram():Traditional Antibiogram +Case example: Susceptibility of Pseudomonas aeruginosa to piperacillin/tazobactam (TZP) +Code example: +antibiogram(your_data, + antibiotics = "TZP") +Combination Antibiogram +Case example: Additional susceptibility of Pseudomonas aeruginosa to TZP + tobramycin versus TZP alone +Code example: +antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN")) +Syndromic Antibiogram +Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) +Code example: +antibiogram(your_data, + antibiotics = penicillins(), + syndromic_group = "ward") +Weighted-Incidence Syndromic Combination Antibiogram (WISCA) +Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure +Code example: +antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male", + "Group 1", "Group 2")) +All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using ggplot2::autoplot() or base R plot()/barplot()) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. flextable::as_flextable() or gt::gt(). +Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (defaults to FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI: +-------------------------------------------------------------------- + only_all_tested = FALSE only_all_tested = TRUE + ----------------------- ----------------------- + Drug A Drug B include as include as include as include as + numerator denominator numerator denominator +-------- -------- ---------- ----------- ---------- ----------- + S or I S or I X X X X + R S or I X X X X + <NA> S or I X X - - + S or I R X X X X + R R - X - X + <NA> R - - - - + S or I <NA> X X - - + R <NA> - - - - + <NA> <NA> - - - - +-------------------------------------------------------------------- +Printing the antibiogram in non-interactive sessions will be done by knitr::kable(), with support for all their implemented formats, such as "markdown". The knitr format will be automatically determined if printed inside a knitr document (LaTeX, HTML, etc.). + + + + Examples + # example_isolates is a data set available in the AMR package. +# run ?example_isolates for more info. +example_isolates +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +#> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +#> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +#> # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +#> # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +#> # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> + + +# Traditional antibiogram ---------------------------------------------- + +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems())) +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 502 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 10 × 7 +#> `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 CoNS (43-309) 0 0 0 0 0 0 +#> 2 E. coli (0-462) 0 0 0 NA 0 0 +#> 3 E. faecalis (0-39) 0 0 0 0 NA 0 +#> 4 K. pneumoniae (0-58) NA 0 0 NA 0 0 +#> 5 P. aeruginosa (17-30) NA 0 NA 0 NA 0 +#> 6 P. mirabilis (0-34) NA 0 0 NA NA 0 +#> 7 S. aureus (2-233) NA 0 NA NA NA 0 +#> 8 S. epidermidis (8-163) 0 0 NA 0 NA 0 +#> 9 S. hominis (3-80) NA 0 NA NA NA 0 +#> 10 S. pneumoniae (11-117) 0 0 NA 0 NA 0 + +antibiogram(example_isolates, + antibiotics = aminoglycosides(), + ab_transform = "atc", + mo_transform = "gramstain") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Pathogen (N min-max)` J01GB01 J01GB03 J01GB04 J01GB06 +#> * <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 Gram-negative (35-686) 0 0 0 0 +#> 2 Gram-positive (436-1170) 0 0 0 0 + +antibiogram(example_isolates, + antibiotics = carbapenems(), + ab_transform = "name", + mo_transform = "name") +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 172 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 5 × 3 +#> `Pathogen (N min-max)` Imipenem Meropenem +#> * <chr> <dbl> <dbl> +#> 1 Coagulase-negative Staphylococcus (CoNS) (48-48) 2 2 +#> 2 Enterococcus faecalis (0-38) 3 NA +#> 3 Escherichia coli (418-422) 0 0 +#> 4 Klebsiella pneumoniae (51-53) 2 2 +#> 5 Proteus mirabilis (27-32) 3 NA + + +# Combined antibiogram ------------------------------------------------- + +# combined antibiotics yield higher empiric coverage +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain") +#> ℹ 3 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 4 +#> `Pathogen (N min-max)` TZP `TZP + GEN` `TZP + TOB` +#> * <chr> <dbl> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 2 +#> 2 Gram-positive (345-1044) 4 1 3 + +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB"), + mo_transform = "gramstain", + ab_transform = "name", + sep = " & ") +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 3 +#> `Pathogen (N min-max)` `Piperacillin/tazobactam` Piperacillin/tazobactam & …¹ +#> * <chr> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 +#> 2 Gram-positive (345-550) 4 3 +#> # … with abbreviated variable name ¹`Piperacillin/tazobactam & Tobramycin` + + +# Syndromic antibiogram ------------------------------------------------ + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems()), + syndromic_group = "ward") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 897 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 14 × 8 +#> `Syndromic Group` `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 Clinical CoNS (23-205) NA 0 0 NA 0 0 +#> 2 ICU CoNS (10-73) NA 0 NA NA NA NA +#> 3 Outpatient CoNS (3-31) NA 0 NA NA NA NA +#> 4 Clinical E. coli (0-299) 0 0 0 NA 0 0 +#> 5 ICU E. coli (0-137) 0 0 0 NA 0 0 +#> 6 Clinical K. pneumoniae (0-51) NA 0 0 NA 0 0 +#> 7 Clinical P. mirabilis (0-30) NA 0 NA NA NA 0 +#> 8 Clinical S. aureus (2-150) NA 0 NA NA NA 0 +#> 9 ICU S. aureus (0-66) NA 0 NA NA NA NA +#> 10 Clinical S. epidermidis (4-79) NA 0 NA NA NA 0 +#> 11 ICU S. epidermidis (4-75) NA 0 NA NA NA 0 +#> 12 Clinical S. hominis (1-45) NA 0 NA NA NA 0 +#> 13 Clinical S. pneumoniae (5-78) 0 0 NA 0 NA 0 +#> 14 ICU S. pneumoniae (5-30) 0 0 NA 0 NA 0 + +# with a custom language, though this will be determined automatically +# (i.e., this table will be in Spanish on Spanish systems) +ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ] +#> ℹ Using column 'mo' as input for mo_genus() +antibiogram(ex1, + antibiotics = aminoglycosides(), + ab_transform = "name", + syndromic_group = ifelse(ex1$ward == "ICU", + "UCI", "No UCI"), + language = "es") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina +#> * <chr> <chr> <dbl> <dbl> <dbl> +#> 1 No UCI E. coli (0-325) 100 37 37 +#> 2 UCI E. coli (0-137) 229 87 87 + + +# Weighted-incidence syndromic combination antibiogram (WISCA) --------- + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + minimum = 10, # this should be >= 30, but now just as example + syndromic_group = ifelse(example_isolates$age >= 65 & + example_isolates$gender == "M", + "WISCA Group 1", "WISCA Group 2")) +#> ℹ 8 combinations had less than minimum = 10 results and were ignored +#> # A tibble: 4 × 6 +#> `Syndromic Group` `Pathogen (N min-max)` AMC `AMC + CIP` TZP `TZP + TOB` +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 WISCA Group 1 Gram-negative (261-285) 3 3 3 3 +#> 2 WISCA Group 2 Gram-negative (380-442) 2 2 2 2 +#> 3 WISCA Group 1 Gram-positive (123-406) 2 2 7 5 +#> 4 WISCA Group 2 Gram-positive (222-732) 1 1 4 3 + + +# Generate plots with ggplot2 or base R -------------------------------- + +ab1 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain") +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +ab2 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + syndromic_group = "ward") +#> ℹ 16 combinations had less than minimum = 30 results and were ignored + +plot(ab1) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab1) +} + + +plot(ab2) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab2) +} + + + + + + + + + + + + + + diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html index ba1f7d1d3..221ddafc9 100644 --- a/reference/antibiotic_class_selectors.html +++ b/reference/antibiotic_class_selectors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -232,7 +237,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). version_expertrules @@ -596,7 +601,11 @@ } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) -#> Warning: Introducing NA: only 23 results available for KAN in group: ward = +#> Warning: There was 1 warning in `summarise()`. +#> ℹ In argument: `across(aminoglycosides(), resistance)`. +#> ℹ In group 3: `ward = "Outpatient"`. +#> Caused by warning: +#> ! Introducing NA: only 23 results available for KAN in group: ward = #> "Outpatient" (minimum = 30). #> # A tibble: 3 × 5 #> ward GEN TOB AMK KAN @@ -648,110 +657,14 @@ #> (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #> (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #> (azithromycin) and 'RIF' (rifampicin) -#> Warning: Introducing NA: no results available for OXA in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 25 results available for AMX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for AMC in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for AMP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 24 results available for TZP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CZO in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 10 results available for CZO in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 20 results available for FEP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CXM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 22 results available for FOX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for CTX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CAZ in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for CRO in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for GEN in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TOB in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for AMK in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 22 results available for TMP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for SXT in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for NIT in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for FOS in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for CIP in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for MFX in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 4 results available for MFX in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for TCY in group: ward = -#> "Clinical" (minimum = 30). -#> Warning: Introducing NA: no results available for TCY in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for TCY in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 13 results available for TGC in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TGC in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 24 results available for IPM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: ward = -#> "Clinical" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: ward = "Outpatient" -#> (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: ward = -#> "Outpatient" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "Clinical" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "ICU" -#> (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: ward = "Outpatient" +#> Warning: There were 52 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. +#> ℹ In group 1: `ward = "Clinical"`. +#> Caused by warning: +#> ! Introducing NA: no results available for OXA in group: ward = "Clinical" #> (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # A tibble: 3 × 33 #> ward OXA FLC AMX AMC AMP TZP CZO FEP CXM FOX #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> @@ -769,15 +682,14 @@ group_by(ward) %>% summarise(across(ab_selector(name %like% "trim"), susceptibility)) } -#> Including isolates from ICU. #> ℹ For ab_selector(name %like% "trim") using columns 'TMP' (trimethoprim) #> and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #> ward TMP SXT #> <chr> <dbl> <dbl> -#> 1 Clinical 0.627 0.808 -#> 2 ICU 0.549 0.778 -#> 3 Outpatient 0.667 0.821 +#> 1 Clinical 0.633 0.793 +#> 2 ICU 0.578 0.792 +#> 3 Outpatient 0.705 0.835 if (require("dplyr")) { # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>% diff --git a/reference/antibiotics.html b/reference/antibiotics.html index 324da1df7..fafc9e935 100644 --- a/reference/antibiotics.html +++ b/reference/antibiotics.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.ab.html b/reference/as.ab.html index f017e9f7e..9cfee9280 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -272,7 +277,7 @@ if (require("dplyr")) { # you can quickly rename 'sir' columns using set_ab_names() with dplyr: example_isolates %>% - set_ab_names(where(is.sir), property = "atc") + set_ab_names(where(is.sir), property = "atc") } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo J01CE01 J01CF04 J01CF05 diff --git a/reference/as.av.html b/reference/as.av.html index c63e2c381..124c6b828 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.disk.html b/reference/as.disk.html index c4c3730df..cd2f2a6ad 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png index e43a9606c..f1b1cd1d5 100644 Binary files a/reference/as.mic-4.png and b/reference/as.mic-4.png differ diff --git a/reference/as.mic-5.png b/reference/as.mic-5.png index 26b403a78..62e5a977b 100644 Binary files a/reference/as.mic-5.png and b/reference/as.mic-5.png differ diff --git a/reference/as.mic.html b/reference/as.mic.html index e0122fd31..e917b07a6 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.mo.html b/reference/as.mo.html index 4a0b657fd..4c798188d 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/as.sir.html b/reference/as.sir.html index 3d9291806..5014ae6f1 100644 --- a/reference/as.sir.html +++ b/reference/as.sir.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,7 +256,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). clean @@ -312,7 +317,7 @@ Machine-Readable Interpretation Guidelines -The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18,308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed. +The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18 308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
install.packages("AMR")
It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.
Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
Manually, using:
+ install.packages("remotes") # if you haven't already remotes::install_github("msberends/AMR")
install.packages("remotes") # if you haven't already remotes::install_github("msberends/AMR")
Automatically, using the rOpenSci R-universe platform, by adding our R-universe address to your list of repositories (‘repos’):
+ options(repos = c(getOption("repos"), msberends = "https://msberends.r-universe.dev")) After this, you can install and update this AMR package like any official release (e.g., using install.packages("AMR") or in RStudio via Tools > Check for Package Updates…). diff --git a/news/index.html b/news/index.html index 3e94fffa3..6bf49017e 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -137,39 +142,46 @@
options(repos = c(getOption("repos"), msberends = "https://msberends.r-universe.dev"))
After this, you can install and update this AMR package like any official release (e.g., using install.packages("AMR") or in RStudio via Tools > Check for Package Updates…).
(this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!)
This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.
TL;DR
as.sir()
as.rsi()
antibiogram()
sir_confidence_interval()
mean_amr_distance()
add_custom_microorganisms()
antivirals
For this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR.
While existing functions such as as.rsi(), rsi_df() and ggplot_rsi() still work, their replacements as.sir(), sir_df(), ggplot_sir() are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set rsi_translation is now called clinical_breakpoints to better reflect its content.
rsi_df()
ggplot_rsi()
sir_df()
ggplot_sir()
rsi_translation
clinical_breakpoints
The ‘RSI functions’ will be removed in a future version, but not before late 2023 / early 2024.
Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2).
With the new antibiogram() function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2).
The help page for antibiogram() extensively elaborates on use cases, and antibiogram() also supports printing in R Markdown and Quarto, with support for 16 languages.
Furthermore, different plotting methods were implemented to allow for graphical visualisations as well.
The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for as.sir(). EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules() to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline option, such as: options(AMR_guideline = "CLSI 2020").
eucast_rules()
AMR_guideline
options(AMR_guideline = "CLSI 2020")
Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.
We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language.
We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 16 languages and according to download stats used in almost all countries in the world!
The microorganisms data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022.
We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score() on how their work was incorporated into the prevalence column of the microorganisms data set. Using their results, the as.mo() and all mo_*() functions are now much better capable of converting user input to valid taxonomic records.
mo_matching_score()
prevalence
as.mo()
mo_*()
The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package.
microorganisms.old
status
"accepted"
"synonym"
The new function add_custom_antimicrobials() allows users to add custom antimicrobial codes and names to the AMR package.
add_custom_antimicrobials()
The antibiotics data set was greatly updated:
antibiotics
Also, we added support for using antibiotic selectors in scoped dplyr verbs (with or without using vars()), such as in: ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() for examples.
vars()
... %>% summarise_at(aminoglycosides(), resistance)
resistance()
We now added extensive support for antiviral agents! For the first time, the AMR package has extensive support for antiviral drugs and to work with their names, codes and other data in any way.
av
ab
as.av()
av_name()
av_atc()
av_synonyms()
av_from_text()
ab_*()
proportion_df()
sir_interpretation_history()
combine_IR
count_df()
combine_SI
units
ab_ddd(..., units = "...")
ab_ddd_units()
data.frame
data.table::data.table
janitor::tabyl
tibble::tibble
tsibble::tsibble
bug_drug_combinations()
tibble
mo_shortname()
as.mic()
as.disk()
g.test()
styler
R/antibiogram.R
antibiogram.Rd
Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker et al. (2021, doi:10.1177/20499361211011373 +) and Barbieri et al. (2021, doi:10.1186/s13756-021-00939-2 +), and allow reporting in e.g. R Markdown and Quarto as well.
antibiogram( + x, + antibiotics = where(is.sir), + mo_transform = "shortname", + ab_transform = NULL, + syndromic_group = NULL, + add_total_n = TRUE, + only_all_tested = FALSE, + digits = 0, + col_mo = NULL, + language = get_AMR_locale(), + minimum = 30, + combine_SI = TRUE, + sep = " + " +) + +# S3 method for antibiogram +plot(x, ...) + +# S3 method for antibiogram +autoplot(object, ...) + +# S3 method for antibiogram +print(x, as_kable = !interactive(), ...)
Klinker KP et al. (2021). Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms. Therapeutic Advances in Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373
Barbieri E et al. (2021). Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2
M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
a data.frame containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see as.sir())
vector of column names, or (any combinations of) antibiotic selectors such as aminoglycosides() or carbapenems(). For combination antibiograms, this can also be column names separated with "+", such as "TZP+TOB" given that the data set contains columns "TZP" and "TOB". See Examples.
aminoglycosides()
carbapenems()
"+"
a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed". Can also be NULL to not transform the input.
NULL
a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units" or "loinc". Can also be NULL to not transform the input.
a column name of x, or values calculated to split rows of x, e.g. by using ifelse() or case_when(). See Examples.
x
ifelse()
case_when()
a logical to indicate whether total available numbers per pathogen should be added to the table (defaults to TRUE). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for E. coli 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").
TRUE
(for combination antibiograms): a logical to indicate that isolates must be tested for all antibiotics, see Details
number of digits to use for rounding
column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
mo
language to translate text, which defaults to the system language (see get_AMR_locale())
get_AMR_locale()
the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
minimum
NA
30
a logical to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (defaults to TRUE)
a separating character for antibiotic columns in combination antibiograms
method extensions
an antibiogram() object
a logical to indicate whether the printing should be done using knitr::kable() (which is the default in non-interactive sessions)
knitr::kable()
This function returns a table with values between 0 and 100 for susceptibility, not resistance.
Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms.
first_isolate()
There are four antibiogram types, as proposed by Klinker et al. (2021, doi:10.1177/20499361211011373 +), and they are all supported by antibiogram():
Traditional Antibiogram
Case example: Susceptibility of Pseudomonas aeruginosa to piperacillin/tazobactam (TZP)
Code example:
antibiogram(your_data, + antibiotics = "TZP")
Combination Antibiogram
Case example: Additional susceptibility of Pseudomonas aeruginosa to TZP + tobramycin versus TZP alone
antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
Syndromic Antibiogram
Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only)
antibiogram(your_data, + antibiotics = penicillins(), + syndromic_group = "ward")
Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure
antibiogram(your_data, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male", + "Group 1", "Group 2"))
All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using ggplot2::autoplot() or base R plot()/barplot()) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. flextable::as_flextable() or gt::gt().
ggplot2::autoplot()
plot()
barplot()
flextable::as_flextable()
gt::gt()
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (defaults to FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI:
only_all_tested
FALSE
-------------------------------------------------------------------- + only_all_tested = FALSE only_all_tested = TRUE + ----------------------- ----------------------- + Drug A Drug B include as include as include as include as + numerator denominator numerator denominator +-------- -------- ---------- ----------- ---------- ----------- + S or I S or I X X X X + R S or I X X X X + <NA> S or I X X - - + S or I R X X X X + R R - X - X + <NA> R - - - - + S or I <NA> X X - - + R <NA> - - - - + <NA> <NA> - - - - +--------------------------------------------------------------------
Printing the antibiogram in non-interactive sessions will be done by knitr::kable(), with support for all their implemented formats, such as "markdown". The knitr format will be automatically determined if printed inside a knitr document (LaTeX, HTML, etc.).
# example_isolates is a data set available in the AMR package. +# run ?example_isolates for more info. +example_isolates +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +#> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +#> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +#> # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +#> # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +#> # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> + + +# Traditional antibiogram ---------------------------------------------- + +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems())) +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 502 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 10 × 7 +#> `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 CoNS (43-309) 0 0 0 0 0 0 +#> 2 E. coli (0-462) 0 0 0 NA 0 0 +#> 3 E. faecalis (0-39) 0 0 0 0 NA 0 +#> 4 K. pneumoniae (0-58) NA 0 0 NA 0 0 +#> 5 P. aeruginosa (17-30) NA 0 NA 0 NA 0 +#> 6 P. mirabilis (0-34) NA 0 0 NA NA 0 +#> 7 S. aureus (2-233) NA 0 NA NA NA 0 +#> 8 S. epidermidis (8-163) 0 0 NA 0 NA 0 +#> 9 S. hominis (3-80) NA 0 NA NA NA 0 +#> 10 S. pneumoniae (11-117) 0 0 NA 0 NA 0 + +antibiogram(example_isolates, + antibiotics = aminoglycosides(), + ab_transform = "atc", + mo_transform = "gramstain") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Pathogen (N min-max)` J01GB01 J01GB03 J01GB04 J01GB06 +#> * <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 Gram-negative (35-686) 0 0 0 0 +#> 2 Gram-positive (436-1170) 0 0 0 0 + +antibiogram(example_isolates, + antibiotics = carbapenems(), + ab_transform = "name", + mo_transform = "name") +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 172 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 5 × 3 +#> `Pathogen (N min-max)` Imipenem Meropenem +#> * <chr> <dbl> <dbl> +#> 1 Coagulase-negative Staphylococcus (CoNS) (48-48) 2 2 +#> 2 Enterococcus faecalis (0-38) 3 NA +#> 3 Escherichia coli (418-422) 0 0 +#> 4 Klebsiella pneumoniae (51-53) 2 2 +#> 5 Proteus mirabilis (27-32) 3 NA + + +# Combined antibiogram ------------------------------------------------- + +# combined antibiotics yield higher empiric coverage +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), + mo_transform = "gramstain") +#> ℹ 3 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 4 +#> `Pathogen (N min-max)` TZP `TZP + GEN` `TZP + TOB` +#> * <chr> <dbl> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 2 +#> 2 Gram-positive (345-1044) 4 1 3 + +antibiogram(example_isolates, + antibiotics = c("TZP", "TZP+TOB"), + mo_transform = "gramstain", + ab_transform = "name", + sep = " & ") +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 3 +#> `Pathogen (N min-max)` `Piperacillin/tazobactam` Piperacillin/tazobactam & …¹ +#> * <chr> <dbl> <dbl> +#> 1 Gram-negative (641-693) 2 2 +#> 2 Gram-positive (345-550) 4 3 +#> # … with abbreviated variable name ¹`Piperacillin/tazobactam & Tobramycin` + + +# Syndromic antibiogram ------------------------------------------------ + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c(aminoglycosides(), carbapenems()), + syndromic_group = "ward") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) +#> ℹ 897 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 14 × 8 +#> `Syndromic Group` `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 Clinical CoNS (23-205) NA 0 0 NA 0 0 +#> 2 ICU CoNS (10-73) NA 0 NA NA NA NA +#> 3 Outpatient CoNS (3-31) NA 0 NA NA NA NA +#> 4 Clinical E. coli (0-299) 0 0 0 NA 0 0 +#> 5 ICU E. coli (0-137) 0 0 0 NA 0 0 +#> 6 Clinical K. pneumoniae (0-51) NA 0 0 NA 0 0 +#> 7 Clinical P. mirabilis (0-30) NA 0 NA NA NA 0 +#> 8 Clinical S. aureus (2-150) NA 0 NA NA NA 0 +#> 9 ICU S. aureus (0-66) NA 0 NA NA NA NA +#> 10 Clinical S. epidermidis (4-79) NA 0 NA NA NA 0 +#> 11 ICU S. epidermidis (4-75) NA 0 NA NA NA 0 +#> 12 Clinical S. hominis (1-45) NA 0 NA NA NA 0 +#> 13 Clinical S. pneumoniae (5-78) 0 0 NA 0 NA 0 +#> 14 ICU S. pneumoniae (5-30) 0 0 NA 0 NA 0 + +# with a custom language, though this will be determined automatically +# (i.e., this table will be in Spanish on Spanish systems) +ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ] +#> ℹ Using column 'mo' as input for mo_genus() +antibiogram(ex1, + antibiotics = aminoglycosides(), + ab_transform = "name", + syndromic_group = ifelse(ex1$ward == "ICU", + "UCI", "No UCI"), + language = "es") +#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' +#> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) +#> ℹ 2 combinations had less than minimum = 30 results and were ignored +#> # A tibble: 2 × 5 +#> `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina +#> * <chr> <chr> <dbl> <dbl> <dbl> +#> 1 No UCI E. coli (0-325) 100 37 37 +#> 2 UCI E. coli (0-137) 229 87 87 + + +# Weighted-incidence syndromic combination antibiogram (WISCA) --------- + +# the data set could contain a filter for e.g. respiratory specimens +antibiogram(example_isolates, + antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + minimum = 10, # this should be >= 30, but now just as example + syndromic_group = ifelse(example_isolates$age >= 65 & + example_isolates$gender == "M", + "WISCA Group 1", "WISCA Group 2")) +#> ℹ 8 combinations had less than minimum = 10 results and were ignored +#> # A tibble: 4 × 6 +#> `Syndromic Group` `Pathogen (N min-max)` AMC `AMC + CIP` TZP `TZP + TOB` +#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> +#> 1 WISCA Group 1 Gram-negative (261-285) 3 3 3 3 +#> 2 WISCA Group 2 Gram-negative (380-442) 2 2 2 2 +#> 3 WISCA Group 1 Gram-positive (123-406) 2 2 7 5 +#> 4 WISCA Group 2 Gram-positive (222-732) 1 1 4 3 + + +# Generate plots with ggplot2 or base R -------------------------------- + +ab1 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain") +#> ℹ 4 combinations had less than minimum = 30 results and were ignored +ab2 <- antibiogram(example_isolates, + antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), + mo_transform = "gramstain", + syndromic_group = "ward") +#> ℹ 16 combinations had less than minimum = 30 results and were ignored + +plot(ab1) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab1) +} + + +plot(ab2) + + +if (requireNamespace("ggplot2")) { + ggplot2::autoplot(ab2) +} + +
column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18,308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18 308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
# \donttest{ +#' # example_isolates is a data set available in the AMR package. +# run ?example_isolates for more info. +example_isolates +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +#> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +#> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +#> # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +#> # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +#> # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> + x <- bug_drug_combinations(example_isolates) -head(x) -#> # A tibble: 6 × 6 -#> mo ab S I R total -#> <chr> <chr> <int> <int> <int> <int> -#> 1 (unknown species) PEN 14 0 1 15 -#> 2 (unknown species) OXA 0 0 1 1 -#> 3 (unknown species) FLC 0 0 0 0 -#> 4 (unknown species) AMX 15 0 1 16 -#> 5 (unknown species) AMC 15 0 0 15 -#> 6 (unknown species) AMP 15 0 1 16 -#> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = "name (atc)") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ @@ -262,20 +280,20 @@ bug_drug_combinations(example_isolates, FUN = mo_gramstain ) -#> # A tibble: 80 × 6 +#> # A tibble: 120 × 6 #> mo ab S I R total #> * <chr> <chr> <int> <int> <int> <int> -#> 1 Gram-negative PEN 8 0 717 725 -#> 2 Gram-negative OXA 6 0 0 6 -#> 3 Gram-negative FLC 6 0 0 6 +#> 1 Gram-negative AMC 463 89 174 726 +#> 2 Gram-negative AMK 251 0 5 256 +#> 3 Gram-negative AMP 226 0 405 631 #> 4 Gram-negative AMX 226 0 405 631 -#> 5 Gram-negative AMC 463 89 174 726 -#> 6 Gram-negative AMP 226 0 405 631 -#> 7 Gram-negative TZP 554 11 76 641 -#> 8 Gram-negative CZO 94 2 110 206 -#> 9 Gram-negative FEP 470 1 14 485 -#> 10 Gram-negative CXM 539 22 142 703 -#> # … with 70 more rows +#> 5 Gram-negative AZM 1 2 696 699 +#> 6 Gram-negative CAZ 607 0 27 634 +#> 7 Gram-negative CHL 1 0 30 31 +#> 8 Gram-negative CIP 610 11 63 684 +#> 9 Gram-negative CLI 18 1 709 728 +#> 10 Gram-negative COL 309 0 78 387 +#> # … with 110 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, @@ -289,16 +307,16 @@ #> # A tibble: 80 × 6 #> mo ab S I R total #> * <chr> <chr> <int> <int> <int> <int> -#> 1 E. coli PEN 0 0 467 467 -#> 2 E. coli OXA 0 0 0 0 -#> 3 E. coli FLC 0 0 0 0 +#> 1 E. coli AMC 332 74 61 467 +#> 2 E. coli AMK 171 0 0 171 +#> 3 E. coli AMP 196 0 196 392 #> 4 E. coli AMX 196 0 196 392 -#> 5 E. coli AMC 332 74 61 467 -#> 6 E. coli AMP 196 0 196 392 -#> 7 E. coli TZP 388 5 23 416 -#> 8 E. coli CZO 79 1 2 82 -#> 9 E. coli FEP 308 0 9 317 -#> 10 E. coli CXM 425 15 25 465 +#> 5 E. coli AZM 0 0 467 467 +#> 6 E. coli CAZ 449 0 11 460 +#> 7 E. coli CHL 0 0 0 0 +#> 8 E. coli CIP 398 1 57 456 +#> 9 E. coli CLI 0 0 467 467 +#> 10 E. coli COL 240 0 0 240 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # } diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 1d2f80733..44866f505 100644 --- a/reference/clinical_breakpoints.html +++ b/reference/clinical_breakpoints.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -148,7 +153,7 @@ Format - A tibble with 18,308 observations and 11 variables:guideline Name of the guideline + A tibble with 18 308 observations and 11 variables:guideline Name of the guideline method Either "DISK" or "MIC" site Body site, e.g. "Oral" or "Respiratory" mo Microbial ID, see as.mo() diff --git a/reference/count.html b/reference/count.html index 8b8386344..2398ed973 100644 --- a/reference/count.html +++ b/reference/count.html @@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible( AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ count_resistant() should be used to count resistant isolates, count_susceptible( Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index 71e6eaa97..7a3ba6f0f 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/dosage.html b/reference/dosage.html index 874eac63e..6c11a77e2 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index ebcab1318..88715447f 100644 --- a/reference/eucast_rules.html +++ b/reference/eucast_rules.html @@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -183,7 +188,7 @@ Leclercq et al. EUCAST expert rules in antimicrobial susceptibility test col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). info diff --git a/reference/example_isolates.html b/reference/example_isolates.html index e00bd1843..831df3efc 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -1,5 +1,5 @@ -Data Set with 2,000 Example Isolates — example_isolates • AMR (for R)Data Set with 2 000 Example Isolates — example_isolates • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,13 +137,13 @@ - Data Set with 2,000 Example Isolates + Data Set with 2 000 Example Isolates Source: R/data.R example_isolates.Rd - A data set containing 2,000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website. + A data set containing 2 000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website. @@ -148,7 +153,7 @@ Format - A tibble with 2,000 observations and 46 variables:date Date of receipt at the laboratory + A tibble with 2 000 observations and 46 variables:date Date of receipt at the laboratory patient ID of the patient age Age of the patient gender Gender of the patient, either "F" or "M" diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index 53ee9e342..a7da9f22d 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -1,5 +1,5 @@ -Data Set with Unclean Data — example_isolates_unclean • AMR (for R)Data Set with Unclean Data — example_isolates_unclean • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -138,7 +143,7 @@ - A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice. + A data set containing 3 000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice. @@ -148,7 +153,7 @@ Format - A tibble with 3,000 observations and 8 variables:patient_id ID of the patient + A tibble with 3 000 observations and 8 variables:patient_id ID of the patient date date of receipt at the laboratory hospital ID of the hospital, from A to C bacteria info about microorganism that can be transformed with as.mo(), see also microorganisms diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 49d4931d6..3936a4dca 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -12,7 +12,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -200,7 +205,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). col_testcode @@ -284,7 +289,7 @@ According to Hindler et al. (2007, doi:10.1086/511864 ), there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies). -All mentioned methods are covered in the first_isolate() function:MethodFunction to applyIsolate-basedfirst_isolate(x, method = "isolate-based")(= all isolates)Patient-basedfirst_isolate(x, method = "patient-based")(= first isolate per patient)Episode-basedfirst_isolate(x, method = "episode-based"), or:(= first isolate per episode)- 7-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 7)- 30-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 30)Phenotype-basedfirst_isolate(x, method = "phenotype-based"), or:(= first isolate per phenotype)- Major difference in any antimicrobial result- first_isolate(x, type = "points")- Any difference in key antimicrobial results- first_isolate(x, type = "keyantimicrobials") +All mentioned methods are covered in the first_isolate() function:MethodFunction to applyIsolate-basedfirst_isolate(x, method = "isolate-based")(= all isolates)Patient-basedfirst_isolate(x, method = "patient-based")(= first isolate per patient)Episode-basedfirst_isolate(x, method = "episode-based"), or:(= first isolate per episode)- 7-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 7)- 30-Day interval from initial isolate- first_isolate(x, method = "e", episode_days = 30)Phenotype-basedfirst_isolate(x, method = "phenotype-based"), or:(= first isolate per phenotype)- Major difference in any antimicrobial result- first_isolate(x, type = "points")- Any difference in key antimicrobial results- first_isolate(x, type = "keyantimicrobials") Isolate-based @@ -332,22 +337,30 @@ # `example_isolates` is a data set available in the AMR package. # See ?example_isolates. -example_isolates[first_isolate(), ] +example_isolates[first_isolate(info = TRUE), ] +#> Determining first isolates using an episode length of 365 days +#> ℹ Using column 'date' as input for col_date. +#> ℹ Using column 'patient' as input for col_patient_id. +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -357,22 +370,21 @@ # \donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] -#> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() -#> # A tibble: 437 × 46 +#> # A tibble: 731 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R -#> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R -#> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R -#> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R -#> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA -#> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA -#> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 4 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 5 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R +#> 6 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 7 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 8 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 9 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 10 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> # … with 721 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> # CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> # CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> # SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, @@ -383,23 +395,29 @@ if (require("dplyr")) { # filter on first isolates using dplyr: example_isolates %>% - filter(first_isolate()) + filter(first_isolate(info = TRUE)) } +#> Determining first isolates using an episode length of 365 days +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -411,21 +429,20 @@ example_isolates %>% filter_first_isolate(info = FALSE) } -#> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -436,25 +453,22 @@ # flag the first isolates per group: example_isolates %>% group_by(ward) %>% - mutate(first = first_isolate()) %>% + mutate(first = first_isolate(info = FALSE)) %>% select(ward, date, patient, mo, first) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> <chr> <date> <chr> <mo> <lgl> #> 1 Clinical 2002-01-02 A77334 B_ESCHR_COLI TRUE -#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI FALSE +#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI TRUE #> 3 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE -#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR FALSE -#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE -#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE +#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE +#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE +#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE #> 7 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE -#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS FALSE -#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR FALSE +#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE +#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR TRUE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # … with 1,990 more rows # } diff --git a/reference/g.test.html b/reference/g.test.html index 6d54325f6..f4c7119ad 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/get_episode.html b/reference/get_episode.html index da0f76bd6..b24cf3e76 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -1,5 +1,5 @@ -Determine (New) Episodes for Patients — get_episode • AMR (for R)Determine (New) Episodes for Patients — get_episode • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -133,12 +138,12 @@ Determine (New) Episodes for Patients - Source: R/episode.R + Source: R/get_episode.R get_episode.Rd - These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode. + These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE for where get_episode() returns 1, and is thus equal to get_episode(...) == 1. @@ -173,7 +178,7 @@ Details Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored. The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods. -The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). +The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). See also @@ -184,47 +189,32 @@ Examples # `example_isolates` is a data set available in the AMR package. # See ?example_isolates -df <- example_isolates[sample(seq_len(2000), size = 200), ] +df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices -#> [1] 14 30 51 29 53 28 49 36 9 7 29 9 43 30 20 51 5 2 5 9 27 6 21 25 14 -#> [26] 20 29 22 42 51 56 39 31 47 1 10 56 7 54 13 39 20 44 10 37 45 26 42 53 3 -#> [51] 2 7 13 51 48 52 31 9 38 50 54 15 41 53 38 23 16 9 26 12 5 26 4 6 31 -#> [76] 7 40 27 55 57 22 52 27 36 26 16 10 48 23 36 49 47 6 6 56 22 51 48 44 24 -#> [101] 58 33 47 49 58 32 8 6 21 3 14 33 57 43 14 50 6 3 53 6 18 15 52 31 22 -#> [126] 7 47 6 42 39 45 1 49 23 53 13 18 35 8 57 12 11 17 3 55 16 12 54 55 14 -#> [151] 9 46 51 55 19 7 36 8 57 4 14 45 45 59 38 55 25 23 60 57 39 38 54 16 31 -#> [176] 3 16 39 50 37 40 14 53 60 6 34 32 14 3 30 52 24 23 1 59 52 11 41 7 26 +#> [1] 24 25 6 9 9 40 28 6 15 42 34 3 1 21 39 3 5 22 30 13 14 31 12 41 36 +#> [26] 34 38 46 18 25 45 43 2 5 17 44 23 43 8 37 17 28 31 43 26 47 43 6 1 28 +#> [51] 18 12 30 22 20 29 31 34 18 13 48 12 31 15 4 3 16 2 7 35 6 19 29 11 24 +#> [76] 16 12 28 14 32 1 23 13 4 6 9 46 38 37 10 13 17 12 40 33 30 7 27 48 38 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE -#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE -#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE -#> [25] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE -#> [37] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE -#> [49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE -#> [61] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE -#> [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE -#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [97] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE -#> [109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE -#> [121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE -#> [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE -#> [157] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE -#> [169] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [181] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE -#> [193] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE +#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE +#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [97] FALSE FALSE FALSE FALSE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] -#> # A tibble: 6 × 46 -#> date patient age gender ward mo PEN OXA FLC AMX -#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> -#> 1 2002-11-07 430011 82 F Clinical B_STPHY_CONS R NA R R -#> 2 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 3 2002-11-14 058917 76 F ICU B_STPHY_HMNS R NA S NA -#> 4 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 5 2002-11-18 956065 89 F Clinical B_ESCHR_COLI R NA NA NA -#> 6 2002-10-14 FCC668 54 F ICU B_ACNTB R NA NA NA +#> # A tibble: 3 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2003-02-26 869648 64 M Outpatie… B_STPHY_AURS R NA R R +#> 2 2003-03-22 E44854 60 F ICU B_STRPT_PNMN S NA NA S +#> 3 2003-01-25 088256 73 F ICU B_STPHY_HMNS R NA R R #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, @@ -234,13 +224,9 @@ #> # MUP <sir>, RIF <sir> # the functions also work for less than a day, e.g. to include one per hour: -get_episode( - c( - Sys.time(), - Sys.time() + 60 * 60 - ), - episode_days = 1 / 24 -) +get_episode(c(Sys.time(), + Sys.time() + 60 * 60), + episode_days = 1 / 24) #> [1] 1 2 # \donttest{ @@ -257,45 +243,36 @@ mutate(new_episode = is_new_episode(date, 365)) %>% select(patient, date, condition, new_episode) } -#> # A tibble: 200 × 4 -#> # Groups: condition [3] -#> patient date condition new_episode -#> <chr> <date> <chr> <lgl> -#> 1 277241 2005-08-31 A FALSE -#> 2 A86006 2009-08-09 C FALSE -#> 3 16DC39 2015-11-19 A FALSE -#> 4 F68330 2009-04-09 A FALSE -#> 5 A79917 2016-05-21 B FALSE -#> 6 A80D37 2009-01-19 A FALSE -#> 7 335263 2015-04-30 B FALSE -#> 8 257844 2011-05-22 B FALSE -#> 9 B8F499 2004-08-22 B FALSE -#> 10 F35553 2004-02-28 A FALSE -#> # … with 190 more rows +#> Error in mutate(., condition = sample(x = c("A", "B", "C"), size = 200, replace = TRUE)): ℹ In argument: `condition = sample(x = c("A", "B", "C"), size = 200, +#> replace = TRUE)`. +#> Caused by error: +#> ! `condition` must be size 100 or 1, not 200. + if (require("dplyr")) { df %>% group_by(ward, patient) %>% - transmute(date, + transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) } -#> # A tibble: 200 × 5 -#> # Groups: ward, patient [183] +#> # A tibble: 100 × 5 +#> # Groups: ward, patient [96] #> ward date patient new_index new_logical #> <chr> <date> <chr> <dbl> <lgl> -#> 1 ICU 2005-08-31 277241 1 TRUE -#> 2 Clinical 2009-08-09 A86006 1 TRUE -#> 3 ICU 2015-11-19 16DC39 1 TRUE -#> 4 Clinical 2009-04-09 F68330 1 TRUE -#> 5 Clinical 2016-05-21 A79917 1 TRUE -#> 6 Clinical 2009-01-19 A80D37 1 TRUE -#> 7 Clinical 2015-04-30 335263 1 TRUE -#> 8 Clinical 2011-05-22 257844 1 TRUE -#> 9 Clinical 2004-08-22 B8F499 1 TRUE -#> 10 ICU 2004-02-28 F35553 2 TRUE -#> # … with 190 more rows +#> 1 Clinical 2009-12-30 6D2377 1 TRUE +#> 2 Clinical 2010-04-05 192353 1 TRUE +#> 3 Clinical 2004-02-02 136315 1 TRUE +#> 4 ICU 2004-09-22 F35553 1 TRUE +#> 5 ICU 2004-11-03 D65308 1 TRUE +#> 6 Clinical 2015-08-14 A84726 1 TRUE +#> 7 Clinical 2011-04-25 023456 1 TRUE +#> 8 Clinical 2004-03-03 1435C8 1 TRUE +#> 9 Clinical 2006-05-26 54890C 1 TRUE +#> 10 ICU 2016-01-28 845227 1 TRUE +#> # … with 90 more rows + if (require("dplyr")) { df %>% group_by(ward) %>% @@ -309,50 +286,33 @@ #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> <chr> <int> <int> <int> <int> -#> 1 Clinical 114 15 53 73 -#> 2 ICU 62 12 38 46 -#> 3 Outpatient 7 6 7 7 -if (require("dplyr")) { - # grouping on patients and microorganisms leads to the same - # results as first_isolate() when using 'episode-based': - x <- df %>% - filter_first_isolate( - include_unknown = TRUE, - method = "episode-based" - ) +#> 1 Clinical 61 2 1 1 +#> 2 ICU 31 6 2 1 +#> 3 Outpatient 4 2 1 1 - y <- df %>% - group_by(patient, mo) %>% - filter(is_new_episode(date, 365)) %>% - ungroup() - - identical(x, y) -} -#> Including isolates from ICU. -#> [1] FALSE if (require("dplyr")) { - # but is_new_episode() has a lot more flexibility than first_isolate(), - # since you can now group on anything that seems relevant: + # is_new_episode() has a lot more flexibility than first_isolate(), + # since you can group on anything that seems relevant: df %>% group_by(patient, mo, ward) %>% mutate(flag_episode = is_new_episode(date, 365)) %>% select(group_vars(.), flag_episode) } -#> # A tibble: 200 × 4 -#> # Groups: patient, mo, ward [189] -#> patient mo ward flag_episode -#> <chr> <mo> <chr> <lgl> -#> 1 277241 B_STPHY_AURS ICU TRUE -#> 2 A86006 B_STPHY_CONS Clinical TRUE -#> 3 16DC39 B_STPHY_HMLY ICU TRUE -#> 4 F68330 B_STRPT_PNMN Clinical TRUE -#> 5 A79917 B_ENTRC_FACM Clinical TRUE -#> 6 A80D37 B_ESCHR_COLI Clinical TRUE -#> 7 335263 B_ENTRBC_CLOC Clinical TRUE -#> 8 257844 B_STPHY_CONS Clinical TRUE -#> 9 B8F499 B_STPHY_CONS Clinical TRUE -#> 10 F35553 B_STPHY_AURS ICU TRUE -#> # … with 190 more rows +#> # A tibble: 100 × 4 +#> # Groups: patient, mo, ward [98] +#> patient mo ward flag_episode +#> <chr> <mo> <chr> <lgl> +#> 1 6D2377 B_FSBCT Clinical TRUE +#> 2 192353 B_STRPT_PNMN Clinical TRUE +#> 3 136315 B_STRPT Clinical TRUE +#> 4 F35553 B_STRPT_ORLS ICU TRUE +#> 5 D65308 B_STPHY_EPDR ICU TRUE +#> 6 A84726 B_STPHY_HMNS Clinical TRUE +#> 7 023456 B_PROTS_MRBL Clinical TRUE +#> 8 1435C8 B_ESCHR_COLI Clinical TRUE +#> 9 54890C B_ESCHR_COLI Clinical TRUE +#> 10 845227 B_STRPT_PNMN ICU TRUE +#> # … with 90 more rows # } diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 428654615..839d2ba6f 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -318,152 +323,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/ggplot_sir-8.png b/reference/ggplot_sir-8.png index 8daa19d00..5b10bb883 100644 Binary files a/reference/ggplot_sir-8.png and b/reference/ggplot_sir-8.png differ diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 3674610f4..667fa2887 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -391,9 +396,8 @@ select(age_group, CIP) %>% ggplot_sir(x = "age_group") } -#> Including isolates from ICU. -#> Warning: Removed 6 rows containing missing values (`position_stack()`). -#> Warning: Removed 6 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). if (require("ggplot2") && require("dplyr")) { # a shorter version which also adjusts data label colours: diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 80103ab93..bddb62312 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/index.html b/reference/index.html index 3a20b446b..ac5d0b75c 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -241,7 +246,7 @@ Analysing data: antimicrobial resistance - Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). + Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. With antibiogram(), you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination antibiogram(WISCA). This function also comes with support for R Markdown and Quarto. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). @@ -251,9 +256,14 @@ + antibiogram() plot(<antibiogram>) autoplot(<antibiogram>) print(<antibiogram>) + + Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) + + resistance() susceptibility() sir_confidence_interval() proportion_R() proportion_IR() proportion_I() proportion_SI() proportion_S() proportion_df() sir_df() - Calculate Microbial Resistance + Calculate Antimicrobial Resistance count_resistant() count_susceptible() count_R() count_IR() count_I() count_SI() count_S() count_all() n_sir() count_df() @@ -376,17 +386,17 @@ example_isolates - Data Set with 2,000 Example Isolates + Data Set with 2 000 Example Isolates microorganisms - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms microorganisms.codes - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes antibiotics antivirals diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index e783f702a..fef9e64a3 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -148,7 +153,7 @@ Format - A tibble with 134,634 observations and 2 variables:mo Microorganism ID + A tibble with 134 634 observations and 2 variables:mo Microorganism ID ab Antibiotic ID diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index f064f2185..9cee16b62 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/join.html b/reference/join.html index 25a516dfd..4dcd176e4 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index ed0d93bad..2168637aa 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,7 +182,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). universal @@ -289,9 +294,7 @@ sum(my_patients$first_regular, na.rm = TRUE) sum(my_patients$first_weighted, na.rm = TRUE) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> [1] 1395 +#> [1] 1984 # } diff --git a/reference/kurtosis.html b/reference/kurtosis.html index e20a8d946..36bf8d83b 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,9 +182,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 2.880473 +#> [1] 2.938161 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.007374585 +#> [1] 0.0159802 + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mdro.html b/reference/mdro.html index 1629593e0..6a9222333 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -183,7 +188,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). info @@ -347,7 +352,10 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> (16 isolates had no test results) -#> Warning: in mdro(): NA introduced for isolates where the available percentage of +#> Warning: There was 1 warning in `mutate()`. +#> ℹ In argument: `MDRO = mdro()`. +#> Caused by warning: +#> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 385f62672..6b9a76860 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -196,31 +201,31 @@ sir <- random_sir(10) sir #> Class 'sir' -#> [1] S I R I S R R I R S +#> [1] R S R R S R I S I R mean_amr_distance(sir) -#> [1] -0.7745967 -0.7745967 1.1618950 -0.7745967 -0.7745967 1.1618950 -#> [7] 1.1618950 -0.7745967 1.1618950 -0.7745967 +#> [1] 0.9486833 -0.9486833 0.9486833 0.9486833 -0.9486833 0.9486833 +#> [7] -0.9486833 -0.9486833 -0.9486833 0.9486833 mic <- random_mic(10) mic #> Class 'mic' -#> [1] 0.5 0.005 4 0.005 4 16 8 >=128 <=0.002 -#> [10] 0.125 +#> [1] <=0.001 8 2 <=0.001 16 <=0.001 0.25 128 0.125 +#> [10] <=0.001 mean_amr_distance(mic) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 disk <- random_disk(10) disk #> Class 'disk' -#> [1] 8 32 43 15 44 37 16 14 29 29 +#> [1] 38 7 36 29 48 15 25 50 50 39 mean_amr_distance(disk) -#> [1] -1.4621577 0.4144083 1.2745011 -0.9148259 1.3526913 0.8053596 -#> [7] -0.8366357 -0.9930162 0.1798376 0.1798376 +#> [1] 0.2922699 -1.8147922 0.1563304 -0.3194578 0.9719673 -1.2710342 +#> [7] -0.5913368 1.1079068 1.1079068 0.3602396 y <- data.frame( id = LETTERS[1:10], @@ -230,36 +235,26 @@ tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") ) y -#> id amox cipr gent tobr -#> 1 A I 25 16 0.25 -#> 2 B I 30 16 0.25 -#> 3 C I 28 0.5 8 -#> 4 D I 26 16 0.25 -#> 5 E S 26 8 0.5 -#> 6 F R 21 16 0.25 -#> 7 G S 19 2 2 -#> 8 H I 27 8 1 -#> 9 I R 19 8 1 -#> 10 J R 28 8 4 +#> id amox cipr gent tobr +#> 1 A S 26 <=0.5 0.5 +#> 2 B I 20 >=16 2 +#> 3 C R 22 1 0.5 +#> 4 D S 25 >=16 >=8 +#> 5 E I 18 >=16 0.5 +#> 6 F S 18 <=0.5 <=0.25 +#> 7 G S 19 2 <=0.25 +#> 8 H S 21 8 0.5 +#> 9 I R 25 1 <=0.25 +#> 10 J S 25 <=0.5 <=0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> [1] -0.19598774 0.12451337 -0.09209741 -0.13188752 -0.14956959 0.06516053 -#> [7] -0.63363528 0.05123363 0.05598101 0.90628900 -y$amr_distance <- mean_amr_distance(y, where(is.mic)) -#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr" +#> [1] -0.08308833 0.31310698 0.30459906 1.01959980 -0.15475222 -0.87382971 +#> [7] -0.56840820 -0.02905708 0.38884763 -0.31701792 +y$amr_distance <- mean_amr_distance(y, where(is.mic)) +#> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] -#> id amox cipr gent tobr amr_distance -#> 3 C I 28 0.5 8 -0.27108669 -#> 7 G S 19 2 2 -0.20035843 -#> 5 E S 26 8 0.5 -0.12963016 -#> 1 A I 25 16 0.25 -0.09426603 -#> 2 B I 30 16 0.25 -0.09426603 -#> 4 D I 26 16 0.25 -0.09426603 -#> 6 F R 21 16 0.25 -0.09426603 -#> 8 H I 27 8 1 0.14377582 -#> 9 I R 19 8 1 0.14377582 -#> 10 J R 28 8 4 0.69058778 +#> Error in order(y$amr_distance): argument 1 is not a vector if (require("dplyr")) { y %>% @@ -271,17 +266,17 @@ } #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> id amox cipr gent tobr amr_distance check_id_C -#> 1 C I 28 0.5 8 -0.09209741 0.00000000 -#> 2 D I 26 16 0.25 -0.13188752 0.03979011 -#> 3 E S 26 8 0.5 -0.14956959 0.05747218 -#> 4 A I 25 16 0.25 -0.19598774 0.10389034 -#> 5 H I 27 8 1 0.05123363 0.14333103 -#> 6 I R 19 8 1 0.05598101 0.14807842 -#> 7 F R 21 16 0.25 0.06516053 0.15725794 -#> 8 B I 30 16 0.25 0.12451337 0.21661078 -#> 9 G S 19 2 2 -0.63363528 0.54153787 -#> 10 J R 28 8 4 0.90628900 0.99838641 +#> id amox cipr gent tobr amr_distance check_id_C +#> 1 C R 22 1 0.5 0.30459906 0.000000000 +#> 2 B I 20 >=16 2 0.31310698 0.008507921 +#> 3 I R 25 1 <=0.25 0.38884763 0.084248568 +#> 4 H S 21 8 0.5 -0.02905708 0.333656139 +#> 5 A S 26 <=0.5 0.5 -0.08308833 0.387687383 +#> 6 E I 18 >=16 0.5 -0.15475222 0.459351276 +#> 7 J S 25 <=0.5 <=0.25 -0.31701792 0.621616981 +#> 8 D S 25 >=16 >=8 1.01959980 0.715000742 +#> 9 G S 19 2 <=0.25 -0.56840820 0.873007255 +#> 10 F S 18 <=0.5 <=0.25 -0.87382971 1.178428772 if (require("dplyr")) { # support for groups example_isolates %>% diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 944c454cc..e71b9798c 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -1,5 +1,5 @@ -Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5 910 Common Microorganism Codes — microorganisms.codes • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes Source: R/data.R microorganisms.codes.Rd @@ -148,7 +153,7 @@ Format - A tibble with 5,910 observations and 2 variables:code Commonly used code of a microorganism + A tibble with 5 910 observations and 2 variables:code Commonly used code of a microorganism mo ID of the microorganism in the microorganisms data set diff --git a/reference/microorganisms.html b/reference/microorganisms.html index aea7321a9..ce5728cf0 100644 --- a/reference/microorganisms.html +++ b/reference/microorganisms.html @@ -1,5 +1,5 @@ -Data Set with 52,141 Microorganisms — microorganisms • AMR (for R)Data Set with 52 142 Microorganisms — microorganisms • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms Source: R/data.R microorganisms.Rd @@ -148,7 +153,7 @@ Format - A tibble with 52,141 observations and 22 variables:mo ID of microorganism as used by this package + A tibble with 52 142 observations and 22 variables:mo ID of microorganism as used by this package fullname Full name, like "Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. status Status of the taxon, either "accepted" or "synonym" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank of the microorganism @@ -231,20 +236,20 @@ Examples microorganisms -#> # A tibble: 52,141 × 22 +#> # A tibble: 52,142 × 22 #> mo fullname status kingdom phylum class order family genus #> <mo> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… -#> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 1 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 2 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 3 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 4 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… "(un… "(un… "(unk… "(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "Abdi… "" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "" "" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… "Abd… "" "" "" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "" "" "" "" -#> # … with 52,131 more rows, and 13 more variables: species <chr>, +#> # … with 52,132 more rows, and 13 more variables: species <chr>, #> # subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> # lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> # gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 6f8cccad9..f9d933796 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mo_property.html b/reference/mo_property.html index 19a8773cb..c2a62e79c 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -400,7 +405,7 @@ property -one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname" +one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed" @@ -506,8 +511,10 @@ mo_gramstain("Klebsiella pneumoniae") #> [1] "Gram-negative" mo_snomed("Klebsiella pneumoniae") -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [[1]] +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" +#> mo_type("Klebsiella pneumoniae") #> [1] "Bacteria" mo_rank("Klebsiella pneumoniae") @@ -748,8 +755,8 @@ #> [1] "Trevisan, 1887" #> #> $snomed -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" #> # } diff --git a/reference/mo_source.html b/reference/mo_source.html index 7f67d7ff8..5dae97f8f 100644 --- a/reference/mo_source.html +++ b/reference/mo_source.html @@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ This is the fastest way to have your organisation (or analysis) specific codes p Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/pca.html b/reference/pca.html index ab16b25c1..f67028628 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,152 +256,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/plot-1.png b/reference/plot-1.png index c7f0a0e23..7b06b86c3 100644 Binary files a/reference/plot-1.png and b/reference/plot-1.png differ diff --git a/reference/plot-2.png b/reference/plot-2.png index 4da7bb826..05d760db2 100644 Binary files a/reference/plot-2.png and b/reference/plot-2.png differ diff --git a/reference/plot-3.png b/reference/plot-3.png index 9dc828c91..8f7ea9a35 100644 Binary files a/reference/plot-3.png and b/reference/plot-3.png differ diff --git a/reference/plot-4.png b/reference/plot-4.png index 76abf039b..3c8519ae8 100644 Binary files a/reference/plot-4.png and b/reference/plot-4.png differ diff --git a/reference/plot-5.png b/reference/plot-5.png index be91a6a92..9d0b334fd 100644 Binary files a/reference/plot-5.png and b/reference/plot-5.png differ diff --git a/reference/plot-6.png b/reference/plot-6.png index 59a1b63fb..7de420901 100644 Binary files a/reference/plot-6.png and b/reference/plot-6.png differ diff --git a/reference/plot-7.png b/reference/plot-7.png index ea5d1e4fc..9fc466395 100644 Binary files a/reference/plot-7.png and b/reference/plot-7.png differ diff --git a/reference/plot-8.png b/reference/plot-8.png index 94cc6370a..eac02c0ad 100644 Binary files a/reference/plot-8.png and b/reference/plot-8.png differ diff --git a/reference/plot-9.png b/reference/plot-9.png index acefed1df..991774f96 100644 Binary files a/reference/plot-9.png and b/reference/plot-9.png differ diff --git a/reference/plot.html b/reference/plot.html index 4aa05522a..d3c4b1f2f 100644 --- a/reference/plot.html +++ b/reference/plot.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/proportion.html b/reference/proportion.html index a3578b281..ee2c1affd 100644 --- a/reference/proportion.html +++ b/reference/proportion.html @@ -1,6 +1,6 @@ Calculate Microbial Resistance — proportion • AMR (for R)Calculate Antimicrobial Resistance — proportion • AMR (for R) subspecies, a 3-5 letter acronym | | \\----> species, a 3-6 letter acronym | \\----> genus, a 4-8 letter acronym \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #> 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #> will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...) NA_sir_ is.sir(x) is_sir_eligible(x, threshold = 0.05) # S3 method for mic as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for disk as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for data.frame as.sir( x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) ) sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. Can also set option AMR_include_PKPD. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF summary(example_isolates) # see all SIR results at a glance #> date patient age gender #> Min. :2002-01-02 Length:2000 Min. : 0.00 Length:2000 #> 1st Qu.:2005-07-31 Class :character 1st Qu.:63.00 Class :character #> Median :2009-07-31 Mode :character Median :74.00 Mode :character #> Mean :2009-11-20 Mean :70.69 #> 3rd Qu.:2014-05-30 3rd Qu.:82.00 #> Max. :2017-12-28 Max. :97.00 #> ward mo PEN #> Length:2000 Class :mo Class:sir #> Class :character :0 %R :73.7% (n=1201) #> Mode :character Unique:90 %SI :26.3% (n=428) #> #1 :B_ESCHR_COLI - %S :25.6% (n=417) #> #2 :B_STPHY_CONS - %I : 0.7% (n=11) #> #3 :B_STPHY_AURS #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %SI :68.8% (n=251) %SI :70.5% (n=665) %SI :40.4% (n=546) #> - %S :68.8% (n=251) - %S :70.5% (n=665) - %S :40.2% (n=543) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.2% (n=3) #> #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %SI :76.3% (n=1433) %SI :40.4% (n=546) %SI :87.4% (n=875) #> - %S :71.4% (n=1342) - %S :40.2% (n=543) - %S :86.1% (n=862) #> - %I : 4.8% (n=91) - %I : 0.2% (n=3) - %I : 1.3% (n=13) #> #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %SI :55.4% (n=247) %SI :85.8% (n=621) %SI :73.7% (n=1319) #> - %S :54.9% (n=245) - %S :85.6% (n=620) - %S :72.5% (n=1297) #> - %I : 0.4% (n=2) - %I : 0.1% (n=1) - %I : 1.2% (n=22) #> #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %SI :72.6% (n=594) %SI :84.5% (n=797) %SI :33.5% (n=607) #> - %S :71.6% (n=586) - %S :84.4% (n=796) - %S :33.5% (n=607) #> - %I : 1.0% (n=8) - %I : 0.1% (n=1) - %I : 0.0% (n=0) #> #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %SI :84.5% (n=797) %SI :75.4% (n=1399) %SI :65.6% (n=886) #> - %S :84.4% (n=796) - %S :74.0% (n=1372) - %S :65.1% (n=879) #> - %I : 0.1% (n=1) - %I : 1.5% (n=27) - %I : 0.5% (n=7) #> #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %SI :36.3% (n=251) %SI : 0.0% (n=0) %SI :61.9% (n=928) #> - %S :36.3% (n=251) - %S : 0.0% (n=0) - %S :61.2% (n=918) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.7% (n=10) #> #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %SI :79.5% (n=1398) %SI :82.9% (n=616) %SI :57.8% (n=203) #> - %S :79.1% (n=1392) - %S :76.0% (n=565) - %S :57.8% (n=203) #> - %I : 0.3% (n=6) - %I : 6.9% (n=51) - %I : 0.0% (n=0) #> #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %SI :30.7% (n=314) %SI :83.8% (n=1181) %SI :66.4% (n=140) #> - %S :30.7% (n=314) - %S :78.9% (n=1112) - %S :64.5% (n=136) #> - %I : 0.0% (n=0) - %I : 4.9% (n=69) - %I : 1.9% (n=4) #> #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %SI :61.7% (n=1149) %SI :24.3% (n=237) %SI :70.3% (n=843) #> - %S :61.7% (n=1149) - %S :24.3% (n=237) - %S :68.3% (n=820) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 1.9% (n=23) #> #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %SI :87.3% (n=697) %SI :72.3% (n=821) %SI :42.8% (n=810) #> - %S :87.3% (n=697) - %S :71.7% (n=814) - %S :42.3% (n=801) #> - %I : 0.0% (n=0) - %I : 0.6% (n=7) - %I : 0.5% (n=9) #> #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %SI :38.8% (n=590) %SI :42.8% (n=810) %SI :93.8% (n=834) #> - %S :38.6% (n=586) - %S :42.3% (n=801) - %S :92.7% (n=824) #> - %I : 0.3% (n=4) - %I : 0.5% (n=9) - %I : 1.1% (n=10) #> #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %SI :94.1% (n=780) %SI :85.3% (n=29) %SI :78.6% (n=121) #> - %S :94.1% (n=780) - %S :85.3% (n=29) - %S :78.6% (n=121) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0) #> #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %SI :18.8% (n=309) %SI :94.1% (n=254) %SI :30.4% (n=305) #> - %S :18.8% (n=309) - %S :93.0% (n=251) - %S :30.2% (n=303) #> - %I : 0.0% (n=0) - %I : 1.1% (n=3) - %I : 0.2% (n=2) #> # For INTERPRETING disk diffusion and MIC values ----------------------- # a whole data set, even with combined MIC values and disk zones df <- data.frame( microorganism = \"Escherichia coli\", AMP = as.mic(8), CIP = as.mic(0.256), GEN = as.disk(18), TOB = as.disk(16), ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #> datetime index ab_input ab_guid…¹ mo_in…² mo_guideline guide…³ #> #> 1 2023-01-24 15:36:22 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… #> 2 2023-01-24 15:36:22 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… #> 3 2023-01-24 15:36:21 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 4 2023-01-24 15:36:21 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 5 2023-01-24 15:36:15 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 6 2023-01-24 15:36:15 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 7 2023-01-24 15:36:15 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 8 2023-01-24 15:36:15 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 9 2023-01-24 15:36:15 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-24 15:36:15 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table , method , #> # input , outcome , breakpoint_S_R , and abbreviated variable #> # names ¹ab_guideline, ²mo_input, ³guideline # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> Note: #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #> pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'sir' #> [1] R # \\donttest{ # the dplyr way if (require(\"dplyr\")) { df %>% mutate_if(is.mic, as.sir) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df %>% mutate(across(where(is.mic), as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir) df %>% mutate(across(AMP:TOB, as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df %>% mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S I R as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages barplot(sir_data) # for frequencies # the dplyr way if (require(\"dplyr\")) { example_isolates %>% mutate_at(vars(PEN:RIF), as.sir) # same: example_isolates %>% as.sir(PEN:RIF) # fastest way to transform all columns with already valid AMR results to class `sir`: example_isolates %>% mutate_if(is_sir_eligible, as.sir) # since dplyr 1.0.0, this can also be: # example_isolates %>% # mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property( atc_code, property, administration = \"O\", url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" ) atc_online_groups(atc_code, ...) atc_online_ddd(atc_code, ...) atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) { # oral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"O\") atc_online_ddd(ab_atc(\"amox\")) # parenteral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"P\") atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\" #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\" #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\" # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_av = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #> av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...) av_cid(x, ...) av_synonyms(x, ...) av_tradenames(x, ...) av_group(x, language = get_AMR_locale(), ...) av_atc(x, ...) av_loinc(x, ...) av_ddd(x, administration = \"oral\", ...) av_ddd_units(x, administration = \"oral\", ...) av_info(x, language = get_AMR_locale(), ...) av_url(x, open = FALSE, ...) av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_tradenames(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #> Aciclovir #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...) # S3 method for bug_drug_combinations format( x, translate_ab = \"name (ab, atc)\", language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark == \",\", \".\", \",\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric decimal point. big.mark character; empty used mark every big.interval decimals (hence big) decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #> mo ab S I R total #> #> 1 (unknown species) PEN 14 0 1 15 #> 2 (unknown species) OXA 0 0 1 1 #> 3 (unknown species) FLC 0 0 0 0 #> 4 (unknown species) AMX 15 0 1 16 #> 5 (unknown species) AMC 15 0 0 15 #> 6 (unknown species) AMP 15 0 1 16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #> #> 1 \"Aminogl… Amik… \"100… \" 0.0… \"100.0… \"\" \"\" \"\" \"\" \"100.0… #> 2 \"\" Gent… \" 13… \" 2.0… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 0.9… \" 21.5… #> 3 \"\" Kana… \"100… \"\" \"100.0… \"\" \"100.0… \"\" \"\" \"100.0… #> 4 \"\" Tobr… \" 78… \" 2.6… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 2.3… \" 49.4… #> 5 \"Ampheni… Chlo… \"\" \"\" \"\" \"\" \"100.0… \"\" \" 0.0… \" 3.1… #> 6 \"Antimyc… Rifa… \"\" \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 0.0… \" 2.7… #> 7 \"Beta-la… Amox… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 8 \"\" Amox… \" 42… \" 13.1… \"\" \" 10.3… \"100.0… \" 2.8… \" 0.4… \" 54.5… #> 9 \"\" Ampi… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 10 \"\" Benz… \" 77… \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` , #> # `S. pneumoniae` , and abbreviated variable names ¹`E. coli`, #> # ²`E. faecalis`, ³`K. pneumoniae`, ⁴`P. aeruginosa`, ⁵`P. mirabilis`, #> # ⁶`S. aureus`, ⁷`S. epidermidis` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 Gram-negative PEN 8 0 717 725 #> 2 Gram-negative OXA 6 0 0 6 #> 3 Gram-negative FLC 6 0 0 6 #> 4 Gram-negative AMX 226 0 405 631 #> 5 Gram-negative AMC 463 89 174 726 #> 6 Gram-negative AMP 226 0 405 631 #> 7 Gram-negative TZP 554 11 76 641 #> 8 Gram-negative CZO 94 2 110 206 #> 9 Gram-negative FEP 470 1 14 485 #> 10 Gram-negative CXM 539 22 142 703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 E. coli PEN 0 0 467 467 #> 2 E. coli OXA 0 0 0 0 #> 3 E. coli FLC 0 0 0 0 #> 4 E. coli AMX 196 0 196 392 #> 5 E. coli AMC 332 74 61 467 #> 6 E. coli AMP 196 0 196 392 #> 7 E. coli TZP 388 5 23 416 #> 8 E. coli CZO 79 1 2 82 #> 9 E. coli FEP 308 0 9 317 #> 10 E. coli CXM 425 15 25 465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #> guideline method site mo rank_…¹ ab ref_tbl disk_…² break…³ #> #> 1 EUCAST 2… MIC NA F_ASPRG_MGTS 2 AMB Aspergi… NA 1 #> 2 EUCAST 2… MIC NA F_ASPRG_NIGR 2 AMB Aspergi… NA 1 #> 3 EUCAST 2… MIC NA F_CANDD_ALBC 2 AMB Candida NA 1 #> 4 EUCAST 2… MIC NA F_CANDD_DBLN 2 AMB Candida NA 1 #> 5 EUCAST 2… MIC NA F_CANDD_GLBR 2 AMB Candida NA 1 #> 6 EUCAST 2… MIC NA F_CANDD_KRUS 2 AMB Candida NA 1 #> 7 EUCAST 2… MIC NA F_CANDD_PRPS 2 AMB Candida NA 1 #> 8 EUCAST 2… MIC NA F_CANDD_TRPC 2 AMB Candida NA 1 #> 9 EUCAST 2… MIC NA F_CRYPT_NFRM 2 AMB Candida NA 1 #> 10 EUCAST 2… DISK NA B_[ORD]_ENTRBCTR 5 AMC Enterob… 20ug/1… 19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R , uti , and #> # abbreviated variable names ¹rank_index, ²disk_dose, ³breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_sir(..., only_all_tested = FALSE) count_df( data, translate_ab = \"name\", language = get_AMR_locale(), combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350 # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804 # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350 # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_sir(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Number of available isolates for a whole antibiotic class # (i.e., in this data set columns GEN, TOB, AMK, KAN) example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), n_sir)) # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #> ward antibiotic interpretation value #> * #> 1 Clinical AMX SI 357 #> 2 Clinical AMX R 487 #> 3 Clinical CIP SI 741 #> 4 Clinical CIP R 128 #> 5 ICU AMX SI 158 #> 6 ICU AMX R 270 #> 7 ICU CIP SI 362 #> 8 ICU CIP R 85 #> 9 Outpatient AMX SI 31 #> 10 Outpatient AMX R 47 #> 11 Outpatient CIP SI 78 #> 12 Outpatient CIP R 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work: rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\", TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"S\" then set to S : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If TZP is \"R\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"), TZP = as.sir(\"R\"), ampi = as.sir(\"S\"), cipro = as.sir(\"S\")) df #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R S S eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\", TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\") eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules( AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_…¹ rule_…² rule_…³ #> #> 1 33 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 2 33 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 3 34 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 4 34 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 5 531 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 6 531 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 7 1485 AMP Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> 8 1485 AMX Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹rule_group, ²rule_name, ³rule_source # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is \"R\" then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #> ab name type dose dose_…¹ admin…² notes origi…³ eucas…⁴ #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 … 12 #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x … 12 #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x … 12 #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1… 12 #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g … 12 #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g … 12 #> 7 AMC Amoxicillin/clavulani… high… 2 g … 3 iv \"\" (2 g a… 12 #> 8 AMC Amoxicillin/clavulani… stan… 1 g … 3 iv \"\" (1 g a… 12 #> 9 AMC Amoxicillin/clavulani… high… 0.87… 3 oral \"\" (0.875… 12 #> 10 AMC Amoxicillin/clavulani… stan… 0.5 … 3 oral \"\" (0.5 g… 12 #> # … with 326 more rows, and abbreviated variable names ¹dose_times, #> # ²administration, ³original_txt, ⁴eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules( x, col_mo = NULL, info = interactive(), rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")), verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, ... ) eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\". EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv. Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylpenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) head(a) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - - - - - S S #> 2 Enterococcus faecalis - - - - - S S #> 3 Escherichia coli - - - - - S S #> 4 Klebsiella pneumoniae - - - - - S S #> 5 Pseudomonas aeruginosa - - - - - S S # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R S S S S #> 2 Enterococcus faecalis - - R R R S R #> 3 Escherichia coli R - - - - R S #> 4 Klebsiella pneumoniae R R - - - R S #> 5 Pseudomonas aeruginosa R R - - R R R # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #> row col mo_fullname old new rule rule_group #> 1 1 AMX Staphylococcus aureus - S Breakpoints #> 2 1 CXM Staphylococcus aureus - S Breakpoints #> 3 1 CAZ Staphylococcus aureus R S Expert Rules #> 4 1 CAZ Staphylococcus aureus - R Expert Rules #> 5 1 COL Staphylococcus aureus - R Expert Rules #> 6 2 CAZ Enterococcus faecalis - R Expert Rules #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # } # Dosage guidelines: eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #>
A tibble with 18,308 observations and 11 variables:
guideline Name of the guideline
guideline
A tibble with 18 308 observations and 11 variables:
method Either "DISK" or "MIC"
method
site Body site, e.g. "Oral" or "Respiratory"
site
mo Microbial ID, see as.mo()
R/data.R
example_isolates.Rd
A data set containing 2,000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website.
A data set containing 2 000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read the tutorial on our website.
A tibble with 2,000 observations and 46 variables:
date Date of receipt at the laboratory
date
A tibble with 2 000 observations and 46 variables:
patient ID of the patient
patient
age Age of the patient
age
gender Gender of the patient, either "F" or "M"
gender
A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
A data set containing 3 000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
A tibble with 3,000 observations and 8 variables:
patient_id ID of the patient
patient_id
A tibble with 3 000 observations and 8 variables:
date date of receipt at the laboratory
hospital ID of the hospital, from A to C
hospital
bacteria info about microorganism that can be transformed with as.mo(), see also microorganisms
bacteria
According to Hindler et al. (2007, doi:10.1086/511864 ), there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies).
All mentioned methods are covered in the first_isolate() function:
first_isolate(x, method = "isolate-based")
first_isolate(x, method = "patient-based")
first_isolate(x, method = "episode-based")
first_isolate(x, method = "e", episode_days = 7)
first_isolate(x, method = "e", episode_days = 30)
first_isolate(x, method = "phenotype-based")
first_isolate(x, type = "points")
first_isolate(x, type = "keyantimicrobials")
# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. -example_isolates[first_isolate(), ] +example_isolates[first_isolate(info = TRUE), ] +#> Determining first isolates using an episode length of 365 days +#> ℹ Using column 'date' as input for col_date. +#> ℹ Using column 'patient' as input for col_patient_id. +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -357,22 +370,21 @@ # \donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] -#> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() -#> # A tibble: 437 × 46 +#> # A tibble: 731 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R -#> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R -#> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R -#> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R -#> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA -#> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA -#> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 4 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA +#> 5 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R +#> 6 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 7 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA +#> 8 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 9 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> 10 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R +#> # … with 721 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> # CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> # CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> # SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, @@ -383,23 +395,29 @@ if (require("dplyr")) { # filter on first isolates using dplyr: example_isolates %>% - filter(first_isolate()) + filter(first_isolate(info = TRUE)) } +#> Determining first isolates using an episode length of 365 days +#> Basing inclusion on all antimicrobial results, using a points threshold of +#> 2 #> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') +#> => Found 1,984 'phenotype-based' first isolates (99.2% of total where a +#> microbial ID was available) +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -411,21 +429,20 @@ example_isolates %>% filter_first_isolate(info = FALSE) } -#> Including isolates from ICU. -#> # A tibble: 1,379 × 46 +#> # A tibble: 1,984 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA -#> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA -#> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R -#> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA -#> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA -#> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA -#> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R -#> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA -#> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA -#> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA -#> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # … with 1,974 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, @@ -436,25 +453,22 @@ # flag the first isolates per group: example_isolates %>% group_by(ward) %>% - mutate(first = first_isolate()) %>% + mutate(first = first_isolate(info = FALSE)) %>% select(ward, date, patient, mo, first) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> <chr> <date> <chr> <mo> <lgl> #> 1 Clinical 2002-01-02 A77334 B_ESCHR_COLI TRUE -#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI FALSE +#> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI TRUE #> 3 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE -#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR FALSE -#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE -#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE +#> 4 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE +#> 5 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE +#> 6 ICU 2002-01-13 067927 B_STPHY_EPDR TRUE #> 7 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE -#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS FALSE -#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR FALSE +#> 8 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE +#> 9 ICU 2002-01-16 067927 B_STPHY_EPDR TRUE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # … with 1,990 more rows # } diff --git a/reference/g.test.html b/reference/g.test.html index 6d54325f6..f4c7119ad 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/get_episode.html b/reference/get_episode.html index da0f76bd6..b24cf3e76 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -1,5 +1,5 @@ -Determine (New) Episodes for Patients — get_episode • AMR (for R)Determine (New) Episodes for Patients — get_episode • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -133,12 +138,12 @@ Determine (New) Episodes for Patients - Source: R/episode.R + Source: R/get_episode.R get_episode.Rd - These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode. + These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE for where get_episode() returns 1, and is thus equal to get_episode(...) == 1. @@ -173,7 +178,7 @@ Details Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored. The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods. -The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). +The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise(). See also @@ -184,47 +189,32 @@ Examples # `example_isolates` is a data set available in the AMR package. # See ?example_isolates -df <- example_isolates[sample(seq_len(2000), size = 200), ] +df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices -#> [1] 14 30 51 29 53 28 49 36 9 7 29 9 43 30 20 51 5 2 5 9 27 6 21 25 14 -#> [26] 20 29 22 42 51 56 39 31 47 1 10 56 7 54 13 39 20 44 10 37 45 26 42 53 3 -#> [51] 2 7 13 51 48 52 31 9 38 50 54 15 41 53 38 23 16 9 26 12 5 26 4 6 31 -#> [76] 7 40 27 55 57 22 52 27 36 26 16 10 48 23 36 49 47 6 6 56 22 51 48 44 24 -#> [101] 58 33 47 49 58 32 8 6 21 3 14 33 57 43 14 50 6 3 53 6 18 15 52 31 22 -#> [126] 7 47 6 42 39 45 1 49 23 53 13 18 35 8 57 12 11 17 3 55 16 12 54 55 14 -#> [151] 9 46 51 55 19 7 36 8 57 4 14 45 45 59 38 55 25 23 60 57 39 38 54 16 31 -#> [176] 3 16 39 50 37 40 14 53 60 6 34 32 14 3 30 52 24 23 1 59 52 11 41 7 26 +#> [1] 24 25 6 9 9 40 28 6 15 42 34 3 1 21 39 3 5 22 30 13 14 31 12 41 36 +#> [26] 34 38 46 18 25 45 43 2 5 17 44 23 43 8 37 17 28 31 43 26 47 43 6 1 28 +#> [51] 18 12 30 22 20 29 31 34 18 13 48 12 31 15 4 3 16 2 7 35 6 19 29 11 24 +#> [76] 16 12 28 14 32 1 23 13 4 6 9 46 38 37 10 13 17 12 40 33 30 7 27 48 38 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE -#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE -#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE -#> [25] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE -#> [37] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE -#> [49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE -#> [61] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE -#> [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE -#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [97] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE -#> [109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE -#> [121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE -#> [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE -#> [157] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE -#> [169] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [181] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE -#> [193] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE +#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE +#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [97] FALSE FALSE FALSE FALSE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] -#> # A tibble: 6 × 46 -#> date patient age gender ward mo PEN OXA FLC AMX -#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> -#> 1 2002-11-07 430011 82 F Clinical B_STPHY_CONS R NA R R -#> 2 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 3 2002-11-14 058917 76 F ICU B_STPHY_HMNS R NA S NA -#> 4 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 5 2002-11-18 956065 89 F Clinical B_ESCHR_COLI R NA NA NA -#> 6 2002-10-14 FCC668 54 F ICU B_ACNTB R NA NA NA +#> # A tibble: 3 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2003-02-26 869648 64 M Outpatie… B_STPHY_AURS R NA R R +#> 2 2003-03-22 E44854 60 F ICU B_STRPT_PNMN S NA NA S +#> 3 2003-01-25 088256 73 F ICU B_STPHY_HMNS R NA R R #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, @@ -234,13 +224,9 @@ #> # MUP <sir>, RIF <sir> # the functions also work for less than a day, e.g. to include one per hour: -get_episode( - c( - Sys.time(), - Sys.time() + 60 * 60 - ), - episode_days = 1 / 24 -) +get_episode(c(Sys.time(), + Sys.time() + 60 * 60), + episode_days = 1 / 24) #> [1] 1 2 # \donttest{ @@ -257,45 +243,36 @@ mutate(new_episode = is_new_episode(date, 365)) %>% select(patient, date, condition, new_episode) } -#> # A tibble: 200 × 4 -#> # Groups: condition [3] -#> patient date condition new_episode -#> <chr> <date> <chr> <lgl> -#> 1 277241 2005-08-31 A FALSE -#> 2 A86006 2009-08-09 C FALSE -#> 3 16DC39 2015-11-19 A FALSE -#> 4 F68330 2009-04-09 A FALSE -#> 5 A79917 2016-05-21 B FALSE -#> 6 A80D37 2009-01-19 A FALSE -#> 7 335263 2015-04-30 B FALSE -#> 8 257844 2011-05-22 B FALSE -#> 9 B8F499 2004-08-22 B FALSE -#> 10 F35553 2004-02-28 A FALSE -#> # … with 190 more rows +#> Error in mutate(., condition = sample(x = c("A", "B", "C"), size = 200, replace = TRUE)): ℹ In argument: `condition = sample(x = c("A", "B", "C"), size = 200, +#> replace = TRUE)`. +#> Caused by error: +#> ! `condition` must be size 100 or 1, not 200. + if (require("dplyr")) { df %>% group_by(ward, patient) %>% - transmute(date, + transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) } -#> # A tibble: 200 × 5 -#> # Groups: ward, patient [183] +#> # A tibble: 100 × 5 +#> # Groups: ward, patient [96] #> ward date patient new_index new_logical #> <chr> <date> <chr> <dbl> <lgl> -#> 1 ICU 2005-08-31 277241 1 TRUE -#> 2 Clinical 2009-08-09 A86006 1 TRUE -#> 3 ICU 2015-11-19 16DC39 1 TRUE -#> 4 Clinical 2009-04-09 F68330 1 TRUE -#> 5 Clinical 2016-05-21 A79917 1 TRUE -#> 6 Clinical 2009-01-19 A80D37 1 TRUE -#> 7 Clinical 2015-04-30 335263 1 TRUE -#> 8 Clinical 2011-05-22 257844 1 TRUE -#> 9 Clinical 2004-08-22 B8F499 1 TRUE -#> 10 ICU 2004-02-28 F35553 2 TRUE -#> # … with 190 more rows +#> 1 Clinical 2009-12-30 6D2377 1 TRUE +#> 2 Clinical 2010-04-05 192353 1 TRUE +#> 3 Clinical 2004-02-02 136315 1 TRUE +#> 4 ICU 2004-09-22 F35553 1 TRUE +#> 5 ICU 2004-11-03 D65308 1 TRUE +#> 6 Clinical 2015-08-14 A84726 1 TRUE +#> 7 Clinical 2011-04-25 023456 1 TRUE +#> 8 Clinical 2004-03-03 1435C8 1 TRUE +#> 9 Clinical 2006-05-26 54890C 1 TRUE +#> 10 ICU 2016-01-28 845227 1 TRUE +#> # … with 90 more rows + if (require("dplyr")) { df %>% group_by(ward) %>% @@ -309,50 +286,33 @@ #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> <chr> <int> <int> <int> <int> -#> 1 Clinical 114 15 53 73 -#> 2 ICU 62 12 38 46 -#> 3 Outpatient 7 6 7 7 -if (require("dplyr")) { - # grouping on patients and microorganisms leads to the same - # results as first_isolate() when using 'episode-based': - x <- df %>% - filter_first_isolate( - include_unknown = TRUE, - method = "episode-based" - ) +#> 1 Clinical 61 2 1 1 +#> 2 ICU 31 6 2 1 +#> 3 Outpatient 4 2 1 1 - y <- df %>% - group_by(patient, mo) %>% - filter(is_new_episode(date, 365)) %>% - ungroup() - - identical(x, y) -} -#> Including isolates from ICU. -#> [1] FALSE if (require("dplyr")) { - # but is_new_episode() has a lot more flexibility than first_isolate(), - # since you can now group on anything that seems relevant: + # is_new_episode() has a lot more flexibility than first_isolate(), + # since you can group on anything that seems relevant: df %>% group_by(patient, mo, ward) %>% mutate(flag_episode = is_new_episode(date, 365)) %>% select(group_vars(.), flag_episode) } -#> # A tibble: 200 × 4 -#> # Groups: patient, mo, ward [189] -#> patient mo ward flag_episode -#> <chr> <mo> <chr> <lgl> -#> 1 277241 B_STPHY_AURS ICU TRUE -#> 2 A86006 B_STPHY_CONS Clinical TRUE -#> 3 16DC39 B_STPHY_HMLY ICU TRUE -#> 4 F68330 B_STRPT_PNMN Clinical TRUE -#> 5 A79917 B_ENTRC_FACM Clinical TRUE -#> 6 A80D37 B_ESCHR_COLI Clinical TRUE -#> 7 335263 B_ENTRBC_CLOC Clinical TRUE -#> 8 257844 B_STPHY_CONS Clinical TRUE -#> 9 B8F499 B_STPHY_CONS Clinical TRUE -#> 10 F35553 B_STPHY_AURS ICU TRUE -#> # … with 190 more rows +#> # A tibble: 100 × 4 +#> # Groups: patient, mo, ward [98] +#> patient mo ward flag_episode +#> <chr> <mo> <chr> <lgl> +#> 1 6D2377 B_FSBCT Clinical TRUE +#> 2 192353 B_STRPT_PNMN Clinical TRUE +#> 3 136315 B_STRPT Clinical TRUE +#> 4 F35553 B_STRPT_ORLS ICU TRUE +#> 5 D65308 B_STPHY_EPDR ICU TRUE +#> 6 A84726 B_STPHY_HMNS Clinical TRUE +#> 7 023456 B_PROTS_MRBL Clinical TRUE +#> 8 1435C8 B_ESCHR_COLI Clinical TRUE +#> 9 54890C B_ESCHR_COLI Clinical TRUE +#> 10 845227 B_STRPT_PNMN ICU TRUE +#> # … with 90 more rows # } diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 428654615..839d2ba6f 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -318,152 +323,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/ggplot_sir-8.png b/reference/ggplot_sir-8.png index 8daa19d00..5b10bb883 100644 Binary files a/reference/ggplot_sir-8.png and b/reference/ggplot_sir-8.png differ diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 3674610f4..667fa2887 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -391,9 +396,8 @@ select(age_group, CIP) %>% ggplot_sir(x = "age_group") } -#> Including isolates from ICU. -#> Warning: Removed 6 rows containing missing values (`position_stack()`). -#> Warning: Removed 6 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). +#> Warning: Removed 4 rows containing missing values (`position_stack()`). if (require("ggplot2") && require("dplyr")) { # a shorter version which also adjusts data label colours: diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 80103ab93..bddb62312 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/index.html b/reference/index.html index 3a20b446b..ac5d0b75c 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -241,7 +246,7 @@ Analysing data: antimicrobial resistance - Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). + Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. With antibiogram(), you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination antibiogram(WISCA). This function also comes with support for R Markdown and Quarto. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()). @@ -251,9 +256,14 @@ + antibiogram() plot(<antibiogram>) autoplot(<antibiogram>) print(<antibiogram>) + + Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) + + resistance() susceptibility() sir_confidence_interval() proportion_R() proportion_IR() proportion_I() proportion_SI() proportion_S() proportion_df() sir_df() - Calculate Microbial Resistance + Calculate Antimicrobial Resistance count_resistant() count_susceptible() count_R() count_IR() count_I() count_SI() count_S() count_all() n_sir() count_df() @@ -376,17 +386,17 @@ example_isolates - Data Set with 2,000 Example Isolates + Data Set with 2 000 Example Isolates microorganisms - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms microorganisms.codes - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes antibiotics antivirals diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index e783f702a..fef9e64a3 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -148,7 +153,7 @@ Format - A tibble with 134,634 observations and 2 variables:mo Microorganism ID + A tibble with 134 634 observations and 2 variables:mo Microorganism ID ab Antibiotic ID diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index f064f2185..9cee16b62 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/join.html b/reference/join.html index 25a516dfd..4dcd176e4 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index ed0d93bad..2168637aa 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,7 +182,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). universal @@ -289,9 +294,7 @@ sum(my_patients$first_regular, na.rm = TRUE) sum(my_patients$first_weighted, na.rm = TRUE) } -#> Including isolates from ICU. -#> Including isolates from ICU. -#> [1] 1395 +#> [1] 1984 # } diff --git a/reference/kurtosis.html b/reference/kurtosis.html index e20a8d946..36bf8d83b 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -177,9 +182,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 2.880473 +#> [1] 2.938161 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.007374585 +#> [1] 0.0159802 + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mdro.html b/reference/mdro.html index 1629593e0..6a9222333 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -183,7 +188,7 @@ col_mo -column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). +column name of the names or codes of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo(). info @@ -347,7 +352,10 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> (16 isolates had no test results) -#> Warning: in mdro(): NA introduced for isolates where the available percentage of +#> Warning: There was 1 warning in `mutate()`. +#> ℹ In argument: `MDRO = mdro()`. +#> Caused by warning: +#> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 385f62672..6b9a76860 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -196,31 +201,31 @@ sir <- random_sir(10) sir #> Class 'sir' -#> [1] S I R I S R R I R S +#> [1] R S R R S R I S I R mean_amr_distance(sir) -#> [1] -0.7745967 -0.7745967 1.1618950 -0.7745967 -0.7745967 1.1618950 -#> [7] 1.1618950 -0.7745967 1.1618950 -0.7745967 +#> [1] 0.9486833 -0.9486833 0.9486833 0.9486833 -0.9486833 0.9486833 +#> [7] -0.9486833 -0.9486833 -0.9486833 0.9486833 mic <- random_mic(10) mic #> Class 'mic' -#> [1] 0.5 0.005 4 0.005 4 16 8 >=128 <=0.002 -#> [10] 0.125 +#> [1] <=0.001 8 2 <=0.001 16 <=0.001 0.25 128 0.125 +#> [10] <=0.001 mean_amr_distance(mic) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 disk <- random_disk(10) disk #> Class 'disk' -#> [1] 8 32 43 15 44 37 16 14 29 29 +#> [1] 38 7 36 29 48 15 25 50 50 39 mean_amr_distance(disk) -#> [1] -1.4621577 0.4144083 1.2745011 -0.9148259 1.3526913 0.8053596 -#> [7] -0.8366357 -0.9930162 0.1798376 0.1798376 +#> [1] 0.2922699 -1.8147922 0.1563304 -0.3194578 0.9719673 -1.2710342 +#> [7] -0.5913368 1.1079068 1.1079068 0.3602396 y <- data.frame( id = LETTERS[1:10], @@ -230,36 +235,26 @@ tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") ) y -#> id amox cipr gent tobr -#> 1 A I 25 16 0.25 -#> 2 B I 30 16 0.25 -#> 3 C I 28 0.5 8 -#> 4 D I 26 16 0.25 -#> 5 E S 26 8 0.5 -#> 6 F R 21 16 0.25 -#> 7 G S 19 2 2 -#> 8 H I 27 8 1 -#> 9 I R 19 8 1 -#> 10 J R 28 8 4 +#> id amox cipr gent tobr +#> 1 A S 26 <=0.5 0.5 +#> 2 B I 20 >=16 2 +#> 3 C R 22 1 0.5 +#> 4 D S 25 >=16 >=8 +#> 5 E I 18 >=16 0.5 +#> 6 F S 18 <=0.5 <=0.25 +#> 7 G S 19 2 <=0.25 +#> 8 H S 21 8 0.5 +#> 9 I R 25 1 <=0.25 +#> 10 J S 25 <=0.5 <=0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> [1] -0.19598774 0.12451337 -0.09209741 -0.13188752 -0.14956959 0.06516053 -#> [7] -0.63363528 0.05123363 0.05598101 0.90628900 -y$amr_distance <- mean_amr_distance(y, where(is.mic)) -#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr" +#> [1] -0.08308833 0.31310698 0.30459906 1.01959980 -0.15475222 -0.87382971 +#> [7] -0.56840820 -0.02905708 0.38884763 -0.31701792 +y$amr_distance <- mean_amr_distance(y, where(is.mic)) +#> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] -#> id amox cipr gent tobr amr_distance -#> 3 C I 28 0.5 8 -0.27108669 -#> 7 G S 19 2 2 -0.20035843 -#> 5 E S 26 8 0.5 -0.12963016 -#> 1 A I 25 16 0.25 -0.09426603 -#> 2 B I 30 16 0.25 -0.09426603 -#> 4 D I 26 16 0.25 -0.09426603 -#> 6 F R 21 16 0.25 -0.09426603 -#> 8 H I 27 8 1 0.14377582 -#> 9 I R 19 8 1 0.14377582 -#> 10 J R 28 8 4 0.69058778 +#> Error in order(y$amr_distance): argument 1 is not a vector if (require("dplyr")) { y %>% @@ -271,17 +266,17 @@ } #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> id amox cipr gent tobr amr_distance check_id_C -#> 1 C I 28 0.5 8 -0.09209741 0.00000000 -#> 2 D I 26 16 0.25 -0.13188752 0.03979011 -#> 3 E S 26 8 0.5 -0.14956959 0.05747218 -#> 4 A I 25 16 0.25 -0.19598774 0.10389034 -#> 5 H I 27 8 1 0.05123363 0.14333103 -#> 6 I R 19 8 1 0.05598101 0.14807842 -#> 7 F R 21 16 0.25 0.06516053 0.15725794 -#> 8 B I 30 16 0.25 0.12451337 0.21661078 -#> 9 G S 19 2 2 -0.63363528 0.54153787 -#> 10 J R 28 8 4 0.90628900 0.99838641 +#> id amox cipr gent tobr amr_distance check_id_C +#> 1 C R 22 1 0.5 0.30459906 0.000000000 +#> 2 B I 20 >=16 2 0.31310698 0.008507921 +#> 3 I R 25 1 <=0.25 0.38884763 0.084248568 +#> 4 H S 21 8 0.5 -0.02905708 0.333656139 +#> 5 A S 26 <=0.5 0.5 -0.08308833 0.387687383 +#> 6 E I 18 >=16 0.5 -0.15475222 0.459351276 +#> 7 J S 25 <=0.5 <=0.25 -0.31701792 0.621616981 +#> 8 D S 25 >=16 >=8 1.01959980 0.715000742 +#> 9 G S 19 2 <=0.25 -0.56840820 0.873007255 +#> 10 F S 18 <=0.5 <=0.25 -0.87382971 1.178428772 if (require("dplyr")) { # support for groups example_isolates %>% diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 944c454cc..e71b9798c 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -1,5 +1,5 @@ -Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5 910 Common Microorganism Codes — microorganisms.codes • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes Source: R/data.R microorganisms.codes.Rd @@ -148,7 +153,7 @@ Format - A tibble with 5,910 observations and 2 variables:code Commonly used code of a microorganism + A tibble with 5 910 observations and 2 variables:code Commonly used code of a microorganism mo ID of the microorganism in the microorganisms data set diff --git a/reference/microorganisms.html b/reference/microorganisms.html index aea7321a9..ce5728cf0 100644 --- a/reference/microorganisms.html +++ b/reference/microorganisms.html @@ -1,5 +1,5 @@ -Data Set with 52,141 Microorganisms — microorganisms • AMR (for R)Data Set with 52 142 Microorganisms — microorganisms • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms Source: R/data.R microorganisms.Rd @@ -148,7 +153,7 @@ Format - A tibble with 52,141 observations and 22 variables:mo ID of microorganism as used by this package + A tibble with 52 142 observations and 22 variables:mo ID of microorganism as used by this package fullname Full name, like "Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. status Status of the taxon, either "accepted" or "synonym" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank of the microorganism @@ -231,20 +236,20 @@ Examples microorganisms -#> # A tibble: 52,141 × 22 +#> # A tibble: 52,142 × 22 #> mo fullname status kingdom phylum class order family genus #> <mo> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… -#> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 1 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 2 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 3 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 4 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… "(un… "(un… "(unk… "(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "Abdi… "" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "" "" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… "Abd… "" "" "" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "" "" "" "" -#> # … with 52,131 more rows, and 13 more variables: species <chr>, +#> # … with 52,132 more rows, and 13 more variables: species <chr>, #> # subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> # lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> # gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 6f8cccad9..f9d933796 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mo_property.html b/reference/mo_property.html index 19a8773cb..c2a62e79c 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -400,7 +405,7 @@ property -one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname" +one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed" @@ -506,8 +511,10 @@ mo_gramstain("Klebsiella pneumoniae") #> [1] "Gram-negative" mo_snomed("Klebsiella pneumoniae") -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [[1]] +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" +#> mo_type("Klebsiella pneumoniae") #> [1] "Bacteria" mo_rank("Klebsiella pneumoniae") @@ -748,8 +755,8 @@ #> [1] "Trevisan, 1887" #> #> $snomed -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" #> # } diff --git a/reference/mo_source.html b/reference/mo_source.html index 7f67d7ff8..5dae97f8f 100644 --- a/reference/mo_source.html +++ b/reference/mo_source.html @@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ This is the fastest way to have your organisation (or analysis) specific codes p Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/pca.html b/reference/pca.html index ab16b25c1..f67028628 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,152 +256,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/plot-1.png b/reference/plot-1.png index c7f0a0e23..7b06b86c3 100644 Binary files a/reference/plot-1.png and b/reference/plot-1.png differ diff --git a/reference/plot-2.png b/reference/plot-2.png index 4da7bb826..05d760db2 100644 Binary files a/reference/plot-2.png and b/reference/plot-2.png differ diff --git a/reference/plot-3.png b/reference/plot-3.png index 9dc828c91..8f7ea9a35 100644 Binary files a/reference/plot-3.png and b/reference/plot-3.png differ diff --git a/reference/plot-4.png b/reference/plot-4.png index 76abf039b..3c8519ae8 100644 Binary files a/reference/plot-4.png and b/reference/plot-4.png differ diff --git a/reference/plot-5.png b/reference/plot-5.png index be91a6a92..9d0b334fd 100644 Binary files a/reference/plot-5.png and b/reference/plot-5.png differ diff --git a/reference/plot-6.png b/reference/plot-6.png index 59a1b63fb..7de420901 100644 Binary files a/reference/plot-6.png and b/reference/plot-6.png differ diff --git a/reference/plot-7.png b/reference/plot-7.png index ea5d1e4fc..9fc466395 100644 Binary files a/reference/plot-7.png and b/reference/plot-7.png differ diff --git a/reference/plot-8.png b/reference/plot-8.png index 94cc6370a..eac02c0ad 100644 Binary files a/reference/plot-8.png and b/reference/plot-8.png differ diff --git a/reference/plot-9.png b/reference/plot-9.png index acefed1df..991774f96 100644 Binary files a/reference/plot-9.png and b/reference/plot-9.png differ diff --git a/reference/plot.html b/reference/plot.html index 4aa05522a..d3c4b1f2f 100644 --- a/reference/plot.html +++ b/reference/plot.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/proportion.html b/reference/proportion.html index a3578b281..ee2c1affd 100644 --- a/reference/proportion.html +++ b/reference/proportion.html @@ -1,6 +1,6 @@ Calculate Microbial Resistance — proportion • AMR (for R)Calculate Antimicrobial Resistance — proportion • AMR (for R) subspecies, a 3-5 letter acronym | | \\----> species, a 3-6 letter acronym | \\----> genus, a 4-8 letter acronym \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #> 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #> will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...) NA_sir_ is.sir(x) is_sir_eligible(x, threshold = 0.05) # S3 method for mic as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for disk as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for data.frame as.sir( x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) ) sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. Can also set option AMR_include_PKPD. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF summary(example_isolates) # see all SIR results at a glance #> date patient age gender #> Min. :2002-01-02 Length:2000 Min. : 0.00 Length:2000 #> 1st Qu.:2005-07-31 Class :character 1st Qu.:63.00 Class :character #> Median :2009-07-31 Mode :character Median :74.00 Mode :character #> Mean :2009-11-20 Mean :70.69 #> 3rd Qu.:2014-05-30 3rd Qu.:82.00 #> Max. :2017-12-28 Max. :97.00 #> ward mo PEN #> Length:2000 Class :mo Class:sir #> Class :character :0 %R :73.7% (n=1201) #> Mode :character Unique:90 %SI :26.3% (n=428) #> #1 :B_ESCHR_COLI - %S :25.6% (n=417) #> #2 :B_STPHY_CONS - %I : 0.7% (n=11) #> #3 :B_STPHY_AURS #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %SI :68.8% (n=251) %SI :70.5% (n=665) %SI :40.4% (n=546) #> - %S :68.8% (n=251) - %S :70.5% (n=665) - %S :40.2% (n=543) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.2% (n=3) #> #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %SI :76.3% (n=1433) %SI :40.4% (n=546) %SI :87.4% (n=875) #> - %S :71.4% (n=1342) - %S :40.2% (n=543) - %S :86.1% (n=862) #> - %I : 4.8% (n=91) - %I : 0.2% (n=3) - %I : 1.3% (n=13) #> #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %SI :55.4% (n=247) %SI :85.8% (n=621) %SI :73.7% (n=1319) #> - %S :54.9% (n=245) - %S :85.6% (n=620) - %S :72.5% (n=1297) #> - %I : 0.4% (n=2) - %I : 0.1% (n=1) - %I : 1.2% (n=22) #> #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %SI :72.6% (n=594) %SI :84.5% (n=797) %SI :33.5% (n=607) #> - %S :71.6% (n=586) - %S :84.4% (n=796) - %S :33.5% (n=607) #> - %I : 1.0% (n=8) - %I : 0.1% (n=1) - %I : 0.0% (n=0) #> #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %SI :84.5% (n=797) %SI :75.4% (n=1399) %SI :65.6% (n=886) #> - %S :84.4% (n=796) - %S :74.0% (n=1372) - %S :65.1% (n=879) #> - %I : 0.1% (n=1) - %I : 1.5% (n=27) - %I : 0.5% (n=7) #> #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %SI :36.3% (n=251) %SI : 0.0% (n=0) %SI :61.9% (n=928) #> - %S :36.3% (n=251) - %S : 0.0% (n=0) - %S :61.2% (n=918) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.7% (n=10) #> #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %SI :79.5% (n=1398) %SI :82.9% (n=616) %SI :57.8% (n=203) #> - %S :79.1% (n=1392) - %S :76.0% (n=565) - %S :57.8% (n=203) #> - %I : 0.3% (n=6) - %I : 6.9% (n=51) - %I : 0.0% (n=0) #> #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %SI :30.7% (n=314) %SI :83.8% (n=1181) %SI :66.4% (n=140) #> - %S :30.7% (n=314) - %S :78.9% (n=1112) - %S :64.5% (n=136) #> - %I : 0.0% (n=0) - %I : 4.9% (n=69) - %I : 1.9% (n=4) #> #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %SI :61.7% (n=1149) %SI :24.3% (n=237) %SI :70.3% (n=843) #> - %S :61.7% (n=1149) - %S :24.3% (n=237) - %S :68.3% (n=820) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 1.9% (n=23) #> #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %SI :87.3% (n=697) %SI :72.3% (n=821) %SI :42.8% (n=810) #> - %S :87.3% (n=697) - %S :71.7% (n=814) - %S :42.3% (n=801) #> - %I : 0.0% (n=0) - %I : 0.6% (n=7) - %I : 0.5% (n=9) #> #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %SI :38.8% (n=590) %SI :42.8% (n=810) %SI :93.8% (n=834) #> - %S :38.6% (n=586) - %S :42.3% (n=801) - %S :92.7% (n=824) #> - %I : 0.3% (n=4) - %I : 0.5% (n=9) - %I : 1.1% (n=10) #> #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %SI :94.1% (n=780) %SI :85.3% (n=29) %SI :78.6% (n=121) #> - %S :94.1% (n=780) - %S :85.3% (n=29) - %S :78.6% (n=121) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0) #> #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %SI :18.8% (n=309) %SI :94.1% (n=254) %SI :30.4% (n=305) #> - %S :18.8% (n=309) - %S :93.0% (n=251) - %S :30.2% (n=303) #> - %I : 0.0% (n=0) - %I : 1.1% (n=3) - %I : 0.2% (n=2) #> # For INTERPRETING disk diffusion and MIC values ----------------------- # a whole data set, even with combined MIC values and disk zones df <- data.frame( microorganism = \"Escherichia coli\", AMP = as.mic(8), CIP = as.mic(0.256), GEN = as.disk(18), TOB = as.disk(16), ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #> datetime index ab_input ab_guid…¹ mo_in…² mo_guideline guide…³ #> #> 1 2023-01-24 15:36:22 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… #> 2 2023-01-24 15:36:22 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… #> 3 2023-01-24 15:36:21 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 4 2023-01-24 15:36:21 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 5 2023-01-24 15:36:15 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 6 2023-01-24 15:36:15 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 7 2023-01-24 15:36:15 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 8 2023-01-24 15:36:15 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 9 2023-01-24 15:36:15 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-24 15:36:15 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table , method , #> # input , outcome , breakpoint_S_R , and abbreviated variable #> # names ¹ab_guideline, ²mo_input, ³guideline # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> Note: #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #> pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'sir' #> [1] R # \\donttest{ # the dplyr way if (require(\"dplyr\")) { df %>% mutate_if(is.mic, as.sir) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df %>% mutate(across(where(is.mic), as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir) df %>% mutate(across(AMP:TOB, as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df %>% mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S I R as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages barplot(sir_data) # for frequencies # the dplyr way if (require(\"dplyr\")) { example_isolates %>% mutate_at(vars(PEN:RIF), as.sir) # same: example_isolates %>% as.sir(PEN:RIF) # fastest way to transform all columns with already valid AMR results to class `sir`: example_isolates %>% mutate_if(is_sir_eligible, as.sir) # since dplyr 1.0.0, this can also be: # example_isolates %>% # mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property( atc_code, property, administration = \"O\", url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" ) atc_online_groups(atc_code, ...) atc_online_ddd(atc_code, ...) atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) { # oral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"O\") atc_online_ddd(ab_atc(\"amox\")) # parenteral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"P\") atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\" #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\" #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\" # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_av = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #> av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...) av_cid(x, ...) av_synonyms(x, ...) av_tradenames(x, ...) av_group(x, language = get_AMR_locale(), ...) av_atc(x, ...) av_loinc(x, ...) av_ddd(x, administration = \"oral\", ...) av_ddd_units(x, administration = \"oral\", ...) av_info(x, language = get_AMR_locale(), ...) av_url(x, open = FALSE, ...) av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_tradenames(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #> Aciclovir #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...) # S3 method for bug_drug_combinations format( x, translate_ab = \"name (ab, atc)\", language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark == \",\", \".\", \",\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric decimal point. big.mark character; empty used mark every big.interval decimals (hence big) decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #> mo ab S I R total #> #> 1 (unknown species) PEN 14 0 1 15 #> 2 (unknown species) OXA 0 0 1 1 #> 3 (unknown species) FLC 0 0 0 0 #> 4 (unknown species) AMX 15 0 1 16 #> 5 (unknown species) AMC 15 0 0 15 #> 6 (unknown species) AMP 15 0 1 16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #> #> 1 \"Aminogl… Amik… \"100… \" 0.0… \"100.0… \"\" \"\" \"\" \"\" \"100.0… #> 2 \"\" Gent… \" 13… \" 2.0… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 0.9… \" 21.5… #> 3 \"\" Kana… \"100… \"\" \"100.0… \"\" \"100.0… \"\" \"\" \"100.0… #> 4 \"\" Tobr… \" 78… \" 2.6… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 2.3… \" 49.4… #> 5 \"Ampheni… Chlo… \"\" \"\" \"\" \"\" \"100.0… \"\" \" 0.0… \" 3.1… #> 6 \"Antimyc… Rifa… \"\" \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 0.0… \" 2.7… #> 7 \"Beta-la… Amox… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 8 \"\" Amox… \" 42… \" 13.1… \"\" \" 10.3… \"100.0… \" 2.8… \" 0.4… \" 54.5… #> 9 \"\" Ampi… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 10 \"\" Benz… \" 77… \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` , #> # `S. pneumoniae` , and abbreviated variable names ¹`E. coli`, #> # ²`E. faecalis`, ³`K. pneumoniae`, ⁴`P. aeruginosa`, ⁵`P. mirabilis`, #> # ⁶`S. aureus`, ⁷`S. epidermidis` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 Gram-negative PEN 8 0 717 725 #> 2 Gram-negative OXA 6 0 0 6 #> 3 Gram-negative FLC 6 0 0 6 #> 4 Gram-negative AMX 226 0 405 631 #> 5 Gram-negative AMC 463 89 174 726 #> 6 Gram-negative AMP 226 0 405 631 #> 7 Gram-negative TZP 554 11 76 641 #> 8 Gram-negative CZO 94 2 110 206 #> 9 Gram-negative FEP 470 1 14 485 #> 10 Gram-negative CXM 539 22 142 703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 E. coli PEN 0 0 467 467 #> 2 E. coli OXA 0 0 0 0 #> 3 E. coli FLC 0 0 0 0 #> 4 E. coli AMX 196 0 196 392 #> 5 E. coli AMC 332 74 61 467 #> 6 E. coli AMP 196 0 196 392 #> 7 E. coli TZP 388 5 23 416 #> 8 E. coli CZO 79 1 2 82 #> 9 E. coli FEP 308 0 9 317 #> 10 E. coli CXM 425 15 25 465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #> guideline method site mo rank_…¹ ab ref_tbl disk_…² break…³ #> #> 1 EUCAST 2… MIC NA F_ASPRG_MGTS 2 AMB Aspergi… NA 1 #> 2 EUCAST 2… MIC NA F_ASPRG_NIGR 2 AMB Aspergi… NA 1 #> 3 EUCAST 2… MIC NA F_CANDD_ALBC 2 AMB Candida NA 1 #> 4 EUCAST 2… MIC NA F_CANDD_DBLN 2 AMB Candida NA 1 #> 5 EUCAST 2… MIC NA F_CANDD_GLBR 2 AMB Candida NA 1 #> 6 EUCAST 2… MIC NA F_CANDD_KRUS 2 AMB Candida NA 1 #> 7 EUCAST 2… MIC NA F_CANDD_PRPS 2 AMB Candida NA 1 #> 8 EUCAST 2… MIC NA F_CANDD_TRPC 2 AMB Candida NA 1 #> 9 EUCAST 2… MIC NA F_CRYPT_NFRM 2 AMB Candida NA 1 #> 10 EUCAST 2… DISK NA B_[ORD]_ENTRBCTR 5 AMC Enterob… 20ug/1… 19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R , uti , and #> # abbreviated variable names ¹rank_index, ²disk_dose, ³breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_sir(..., only_all_tested = FALSE) count_df( data, translate_ab = \"name\", language = get_AMR_locale(), combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350 # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804 # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350 # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_sir(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Number of available isolates for a whole antibiotic class # (i.e., in this data set columns GEN, TOB, AMK, KAN) example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), n_sir)) # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #> ward antibiotic interpretation value #> * #> 1 Clinical AMX SI 357 #> 2 Clinical AMX R 487 #> 3 Clinical CIP SI 741 #> 4 Clinical CIP R 128 #> 5 ICU AMX SI 158 #> 6 ICU AMX R 270 #> 7 ICU CIP SI 362 #> 8 ICU CIP R 85 #> 9 Outpatient AMX SI 31 #> 10 Outpatient AMX R 47 #> 11 Outpatient CIP SI 78 #> 12 Outpatient CIP R 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work: rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\", TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"S\" then set to S : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If TZP is \"R\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"), TZP = as.sir(\"R\"), ampi = as.sir(\"S\"), cipro = as.sir(\"S\")) df #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R S S eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\", TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\") eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules( AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_…¹ rule_…² rule_…³ #> #> 1 33 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 2 33 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 3 34 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 4 34 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 5 531 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 6 531 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 7 1485 AMP Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> 8 1485 AMX Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹rule_group, ²rule_name, ³rule_source # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is \"R\" then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #> ab name type dose dose_…¹ admin…² notes origi…³ eucas…⁴ #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 … 12 #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x … 12 #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x … 12 #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1… 12 #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g … 12 #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g … 12 #> 7 AMC Amoxicillin/clavulani… high… 2 g … 3 iv \"\" (2 g a… 12 #> 8 AMC Amoxicillin/clavulani… stan… 1 g … 3 iv \"\" (1 g a… 12 #> 9 AMC Amoxicillin/clavulani… high… 0.87… 3 oral \"\" (0.875… 12 #> 10 AMC Amoxicillin/clavulani… stan… 0.5 … 3 oral \"\" (0.5 g… 12 #> # … with 326 more rows, and abbreviated variable names ¹dose_times, #> # ²administration, ³original_txt, ⁴eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules( x, col_mo = NULL, info = interactive(), rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")), verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, ... ) eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\". EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv. Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylpenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) head(a) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - - - - - S S #> 2 Enterococcus faecalis - - - - - S S #> 3 Escherichia coli - - - - - S S #> 4 Klebsiella pneumoniae - - - - - S S #> 5 Pseudomonas aeruginosa - - - - - S S # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R S S S S #> 2 Enterococcus faecalis - - R R R S R #> 3 Escherichia coli R - - - - R S #> 4 Klebsiella pneumoniae R R - - - R S #> 5 Pseudomonas aeruginosa R R - - R R R # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #> row col mo_fullname old new rule rule_group #> 1 1 AMX Staphylococcus aureus - S Breakpoints #> 2 1 CXM Staphylococcus aureus - S Breakpoints #> 3 1 CAZ Staphylococcus aureus R S Expert Rules #> 4 1 CAZ Staphylococcus aureus - R Expert Rules #> 5 1 COL Staphylococcus aureus - R Expert Rules #> 6 2 CAZ Enterococcus faecalis - R Expert Rules #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # } # Dosage guidelines: eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #>
R/episode.R
R/get_episode.R
get_episode.Rd
These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode.
episode_days
get_episode()
is_new_episode()
These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE for where get_episode() returns 1, and is thus equal to get_episode(...) == 1.
get_episode(...) == 1
Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.
The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().
mutate()
The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().
# `example_isolates` is a data set available in the AMR package. # See ?example_isolates -df <- example_isolates[sample(seq_len(2000), size = 200), ] +df <- example_isolates[sample(seq_len(2000), size = 100), ] get_episode(df$date, episode_days = 60) # indices -#> [1] 14 30 51 29 53 28 49 36 9 7 29 9 43 30 20 51 5 2 5 9 27 6 21 25 14 -#> [26] 20 29 22 42 51 56 39 31 47 1 10 56 7 54 13 39 20 44 10 37 45 26 42 53 3 -#> [51] 2 7 13 51 48 52 31 9 38 50 54 15 41 53 38 23 16 9 26 12 5 26 4 6 31 -#> [76] 7 40 27 55 57 22 52 27 36 26 16 10 48 23 36 49 47 6 6 56 22 51 48 44 24 -#> [101] 58 33 47 49 58 32 8 6 21 3 14 33 57 43 14 50 6 3 53 6 18 15 52 31 22 -#> [126] 7 47 6 42 39 45 1 49 23 53 13 18 35 8 57 12 11 17 3 55 16 12 54 55 14 -#> [151] 9 46 51 55 19 7 36 8 57 4 14 45 45 59 38 55 25 23 60 57 39 38 54 16 31 -#> [176] 3 16 39 50 37 40 14 53 60 6 34 32 14 3 30 52 24 23 1 59 52 11 41 7 26 +#> [1] 24 25 6 9 9 40 28 6 15 42 34 3 1 21 39 3 5 22 30 13 14 31 12 41 36 +#> [26] 34 38 46 18 25 45 43 2 5 17 44 23 43 8 37 17 28 31 43 26 47 43 6 1 28 +#> [51] 18 12 30 22 20 29 31 34 18 13 48 12 31 15 4 3 16 2 7 35 6 19 29 11 24 +#> [76] 16 12 28 14 32 1 23 13 4 6 9 46 38 37 10 13 17 12 40 33 30 7 27 48 38 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE -#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE -#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE -#> [25] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE -#> [37] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE -#> [49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE -#> [61] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE -#> [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE -#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [97] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE -#> [109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE -#> [121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE -#> [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE -#> [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE -#> [157] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE -#> [169] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [181] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE -#> [193] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE +#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE +#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE +#> [97] FALSE FALSE FALSE FALSE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] -#> # A tibble: 6 × 46 -#> date patient age gender ward mo PEN OXA FLC AMX -#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> -#> 1 2002-11-07 430011 82 F Clinical B_STPHY_CONS R NA R R -#> 2 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 3 2002-11-14 058917 76 F ICU B_STPHY_HMNS R NA S NA -#> 4 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R -#> 5 2002-11-18 956065 89 F Clinical B_ESCHR_COLI R NA NA NA -#> 6 2002-10-14 FCC668 54 F ICU B_ACNTB R NA NA NA +#> # A tibble: 3 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2003-02-26 869648 64 M Outpatie… B_STPHY_AURS R NA R R +#> 2 2003-03-22 E44854 60 F ICU B_STRPT_PNMN S NA NA S +#> 3 2003-01-25 088256 73 F ICU B_STPHY_HMNS R NA R R #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, @@ -234,13 +224,9 @@ #> # MUP <sir>, RIF <sir> # the functions also work for less than a day, e.g. to include one per hour: -get_episode( - c( - Sys.time(), - Sys.time() + 60 * 60 - ), - episode_days = 1 / 24 -) +get_episode(c(Sys.time(), + Sys.time() + 60 * 60), + episode_days = 1 / 24) #> [1] 1 2 # \donttest{ @@ -257,45 +243,36 @@ mutate(new_episode = is_new_episode(date, 365)) %>% select(patient, date, condition, new_episode) } -#> # A tibble: 200 × 4 -#> # Groups: condition [3] -#> patient date condition new_episode -#> <chr> <date> <chr> <lgl> -#> 1 277241 2005-08-31 A FALSE -#> 2 A86006 2009-08-09 C FALSE -#> 3 16DC39 2015-11-19 A FALSE -#> 4 F68330 2009-04-09 A FALSE -#> 5 A79917 2016-05-21 B FALSE -#> 6 A80D37 2009-01-19 A FALSE -#> 7 335263 2015-04-30 B FALSE -#> 8 257844 2011-05-22 B FALSE -#> 9 B8F499 2004-08-22 B FALSE -#> 10 F35553 2004-02-28 A FALSE -#> # … with 190 more rows +#> Error in mutate(., condition = sample(x = c("A", "B", "C"), size = 200, replace = TRUE)): ℹ In argument: `condition = sample(x = c("A", "B", "C"), size = 200, +#> replace = TRUE)`. +#> Caused by error: +#> ! `condition` must be size 100 or 1, not 200. + if (require("dplyr")) { df %>% group_by(ward, patient) %>% - transmute(date, + transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) } -#> # A tibble: 200 × 5 -#> # Groups: ward, patient [183] +#> # A tibble: 100 × 5 +#> # Groups: ward, patient [96] #> ward date patient new_index new_logical #> <chr> <date> <chr> <dbl> <lgl> -#> 1 ICU 2005-08-31 277241 1 TRUE -#> 2 Clinical 2009-08-09 A86006 1 TRUE -#> 3 ICU 2015-11-19 16DC39 1 TRUE -#> 4 Clinical 2009-04-09 F68330 1 TRUE -#> 5 Clinical 2016-05-21 A79917 1 TRUE -#> 6 Clinical 2009-01-19 A80D37 1 TRUE -#> 7 Clinical 2015-04-30 335263 1 TRUE -#> 8 Clinical 2011-05-22 257844 1 TRUE -#> 9 Clinical 2004-08-22 B8F499 1 TRUE -#> 10 ICU 2004-02-28 F35553 2 TRUE -#> # … with 190 more rows +#> 1 Clinical 2009-12-30 6D2377 1 TRUE +#> 2 Clinical 2010-04-05 192353 1 TRUE +#> 3 Clinical 2004-02-02 136315 1 TRUE +#> 4 ICU 2004-09-22 F35553 1 TRUE +#> 5 ICU 2004-11-03 D65308 1 TRUE +#> 6 Clinical 2015-08-14 A84726 1 TRUE +#> 7 Clinical 2011-04-25 023456 1 TRUE +#> 8 Clinical 2004-03-03 1435C8 1 TRUE +#> 9 Clinical 2006-05-26 54890C 1 TRUE +#> 10 ICU 2016-01-28 845227 1 TRUE +#> # … with 90 more rows + if (require("dplyr")) { df %>% group_by(ward) %>% @@ -309,50 +286,33 @@ #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> <chr> <int> <int> <int> <int> -#> 1 Clinical 114 15 53 73 -#> 2 ICU 62 12 38 46 -#> 3 Outpatient 7 6 7 7 -if (require("dplyr")) { - # grouping on patients and microorganisms leads to the same - # results as first_isolate() when using 'episode-based': - x <- df %>% - filter_first_isolate( - include_unknown = TRUE, - method = "episode-based" - ) +#> 1 Clinical 61 2 1 1 +#> 2 ICU 31 6 2 1 +#> 3 Outpatient 4 2 1 1 - y <- df %>% - group_by(patient, mo) %>% - filter(is_new_episode(date, 365)) %>% - ungroup() - - identical(x, y) -} -#> Including isolates from ICU. -#> [1] FALSE if (require("dplyr")) { - # but is_new_episode() has a lot more flexibility than first_isolate(), - # since you can now group on anything that seems relevant: + # is_new_episode() has a lot more flexibility than first_isolate(), + # since you can group on anything that seems relevant: df %>% group_by(patient, mo, ward) %>% mutate(flag_episode = is_new_episode(date, 365)) %>% select(group_vars(.), flag_episode) } -#> # A tibble: 200 × 4 -#> # Groups: patient, mo, ward [189] -#> patient mo ward flag_episode -#> <chr> <mo> <chr> <lgl> -#> 1 277241 B_STPHY_AURS ICU TRUE -#> 2 A86006 B_STPHY_CONS Clinical TRUE -#> 3 16DC39 B_STPHY_HMLY ICU TRUE -#> 4 F68330 B_STRPT_PNMN Clinical TRUE -#> 5 A79917 B_ENTRC_FACM Clinical TRUE -#> 6 A80D37 B_ESCHR_COLI Clinical TRUE -#> 7 335263 B_ENTRBC_CLOC Clinical TRUE -#> 8 257844 B_STPHY_CONS Clinical TRUE -#> 9 B8F499 B_STPHY_CONS Clinical TRUE -#> 10 F35553 B_STPHY_AURS ICU TRUE -#> # … with 190 more rows +#> # A tibble: 100 × 4 +#> # Groups: patient, mo, ward [98] +#> patient mo ward flag_episode +#> <chr> <mo> <chr> <lgl> +#> 1 6D2377 B_FSBCT Clinical TRUE +#> 2 192353 B_STRPT_PNMN Clinical TRUE +#> 3 136315 B_STRPT Clinical TRUE +#> 4 F35553 B_STRPT_ORLS ICU TRUE +#> 5 D65308 B_STPHY_EPDR ICU TRUE +#> 6 A84726 B_STPHY_HMNS Clinical TRUE +#> 7 023456 B_PROTS_MRBL Clinical TRUE +#> 8 1435C8 B_ESCHR_COLI Clinical TRUE +#> 9 54890C B_ESCHR_COLI Clinical TRUE +#> 10 845227 B_STRPT_PNMN ICU TRUE +#> # … with 90 more rows # }
Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()).
susceptibility()
mdro()
Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. With antibiogram(), you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination antibiogram(WISCA). This function also comes with support for R Markdown and Quarto. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()).
plot(<antibiogram>)
autoplot(<antibiogram>)
print(<antibiogram>)
proportion_R()
proportion_IR()
proportion_I()
proportion_S()
count_resistant()
count_susceptible()
count_R()
count_IR()
count_I()
count_SI()
count_S()
count_all()
n_sir()
example_isolates
microorganisms.codes
A tibble with 134,634 observations and 2 variables:
mo Microorganism ID
A tibble with 134 634 observations and 2 variables:
ab Antibiotic ID
kurtosis(rnorm(10000)) -#> [1] 2.880473 +#> [1] 2.938161 kurtosis(rnorm(10000), excess = TRUE) -#> [1] 0.007374585 +#> [1] 0.0159802
sir <- random_sir(10) sir #> Class 'sir' -#> [1] S I R I S R R I R S +#> [1] R S R R S R I S I R mean_amr_distance(sir) -#> [1] -0.7745967 -0.7745967 1.1618950 -0.7745967 -0.7745967 1.1618950 -#> [7] 1.1618950 -0.7745967 1.1618950 -0.7745967 +#> [1] 0.9486833 -0.9486833 0.9486833 0.9486833 -0.9486833 0.9486833 +#> [7] -0.9486833 -0.9486833 -0.9486833 0.9486833 mic <- random_mic(10) mic #> Class 'mic' -#> [1] 0.5 0.005 4 0.005 4 16 8 >=128 <=0.002 -#> [10] 0.125 +#> [1] <=0.001 8 2 <=0.001 16 <=0.001 0.25 128 0.125 +#> [10] <=0.001 mean_amr_distance(mic) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) -#> [1] 0.004555148 -1.189296722 0.543632982 -1.189296722 0.543632982 -#> [6] 0.903018205 0.723325594 1.442096040 -1.426837433 -0.354830075 +#> [1] -1.052079574 0.902371938 0.600893605 -1.052079574 1.053111104 +#> [6] -1.052079574 0.148676107 1.505328602 -0.002063059 -1.052079574 disk <- random_disk(10) disk #> Class 'disk' -#> [1] 8 32 43 15 44 37 16 14 29 29 +#> [1] 38 7 36 29 48 15 25 50 50 39 mean_amr_distance(disk) -#> [1] -1.4621577 0.4144083 1.2745011 -0.9148259 1.3526913 0.8053596 -#> [7] -0.8366357 -0.9930162 0.1798376 0.1798376 +#> [1] 0.2922699 -1.8147922 0.1563304 -0.3194578 0.9719673 -1.2710342 +#> [7] -0.5913368 1.1079068 1.1079068 0.3602396 y <- data.frame( id = LETTERS[1:10], @@ -230,36 +235,26 @@ tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") ) y -#> id amox cipr gent tobr -#> 1 A I 25 16 0.25 -#> 2 B I 30 16 0.25 -#> 3 C I 28 0.5 8 -#> 4 D I 26 16 0.25 -#> 5 E S 26 8 0.5 -#> 6 F R 21 16 0.25 -#> 7 G S 19 2 2 -#> 8 H I 27 8 1 -#> 9 I R 19 8 1 -#> 10 J R 28 8 4 +#> id amox cipr gent tobr +#> 1 A S 26 <=0.5 0.5 +#> 2 B I 20 >=16 2 +#> 3 C R 22 1 0.5 +#> 4 D S 25 >=16 >=8 +#> 5 E I 18 >=16 0.5 +#> 6 F S 18 <=0.5 <=0.25 +#> 7 G S 19 2 <=0.25 +#> 8 H S 21 8 0.5 +#> 9 I R 25 1 <=0.25 +#> 10 J S 25 <=0.5 <=0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> [1] -0.19598774 0.12451337 -0.09209741 -0.13188752 -0.14956959 0.06516053 -#> [7] -0.63363528 0.05123363 0.05598101 0.90628900 -y$amr_distance <- mean_amr_distance(y, where(is.mic)) -#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr" +#> [1] -0.08308833 0.31310698 0.30459906 1.01959980 -0.15475222 -0.87382971 +#> [7] -0.56840820 -0.02905708 0.38884763 -0.31701792 +y$amr_distance <- mean_amr_distance(y, where(is.mic)) +#> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] -#> id amox cipr gent tobr amr_distance -#> 3 C I 28 0.5 8 -0.27108669 -#> 7 G S 19 2 2 -0.20035843 -#> 5 E S 26 8 0.5 -0.12963016 -#> 1 A I 25 16 0.25 -0.09426603 -#> 2 B I 30 16 0.25 -0.09426603 -#> 4 D I 26 16 0.25 -0.09426603 -#> 6 F R 21 16 0.25 -0.09426603 -#> 8 H I 27 8 1 0.14377582 -#> 9 I R 19 8 1 0.14377582 -#> 10 J R 28 8 4 0.69058778 +#> Error in order(y$amr_distance): argument 1 is not a vector if (require("dplyr")) { y %>% @@ -271,17 +266,17 @@ } #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and #> "tobr" -#> id amox cipr gent tobr amr_distance check_id_C -#> 1 C I 28 0.5 8 -0.09209741 0.00000000 -#> 2 D I 26 16 0.25 -0.13188752 0.03979011 -#> 3 E S 26 8 0.5 -0.14956959 0.05747218 -#> 4 A I 25 16 0.25 -0.19598774 0.10389034 -#> 5 H I 27 8 1 0.05123363 0.14333103 -#> 6 I R 19 8 1 0.05598101 0.14807842 -#> 7 F R 21 16 0.25 0.06516053 0.15725794 -#> 8 B I 30 16 0.25 0.12451337 0.21661078 -#> 9 G S 19 2 2 -0.63363528 0.54153787 -#> 10 J R 28 8 4 0.90628900 0.99838641 +#> id amox cipr gent tobr amr_distance check_id_C +#> 1 C R 22 1 0.5 0.30459906 0.000000000 +#> 2 B I 20 >=16 2 0.31310698 0.008507921 +#> 3 I R 25 1 <=0.25 0.38884763 0.084248568 +#> 4 H S 21 8 0.5 -0.02905708 0.333656139 +#> 5 A S 26 <=0.5 0.5 -0.08308833 0.387687383 +#> 6 E I 18 >=16 0.5 -0.15475222 0.459351276 +#> 7 J S 25 <=0.5 <=0.25 -0.31701792 0.621616981 +#> 8 D S 25 >=16 >=8 1.01959980 0.715000742 +#> 9 G S 19 2 <=0.25 -0.56840820 0.873007255 +#> 10 F S 18 <=0.5 <=0.25 -0.87382971 1.178428772 if (require("dplyr")) { # support for groups example_isolates %>% diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 944c454cc..e71b9798c 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -1,5 +1,5 @@ -Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5 910 Common Microorganism Codes — microorganisms.codes • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 5,910 Common Microorganism Codes + Data Set with 5 910 Common Microorganism Codes Source: R/data.R microorganisms.codes.Rd @@ -148,7 +153,7 @@ Format - A tibble with 5,910 observations and 2 variables:code Commonly used code of a microorganism + A tibble with 5 910 observations and 2 variables:code Commonly used code of a microorganism mo ID of the microorganism in the microorganisms data set diff --git a/reference/microorganisms.html b/reference/microorganisms.html index aea7321a9..ce5728cf0 100644 --- a/reference/microorganisms.html +++ b/reference/microorganisms.html @@ -1,5 +1,5 @@ -Data Set with 52,141 Microorganisms — microorganisms • AMR (for R)Data Set with 52 142 Microorganisms — microorganisms • AMR (for R) @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -132,7 +137,7 @@ - Data Set with 52,141 Microorganisms + Data Set with 52 142 Microorganisms Source: R/data.R microorganisms.Rd @@ -148,7 +153,7 @@ Format - A tibble with 52,141 observations and 22 variables:mo ID of microorganism as used by this package + A tibble with 52 142 observations and 22 variables:mo ID of microorganism as used by this package fullname Full name, like "Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. status Status of the taxon, either "accepted" or "synonym" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank of the microorganism @@ -231,20 +236,20 @@ Examples microorganisms -#> # A tibble: 52,141 × 22 +#> # A tibble: 52,142 × 22 #> mo fullname status kingdom phylum class order family genus #> <mo> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… -#> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 1 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 2 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 3 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 4 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… "(un… "(un… "(unk… "(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "Abdi… "" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "" "" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… "Abd… "" "" "" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "" "" "" "" -#> # … with 52,131 more rows, and 13 more variables: species <chr>, +#> # … with 52,132 more rows, and 13 more variables: species <chr>, #> # subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> # lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> # gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 6f8cccad9..f9d933796 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mo_property.html b/reference/mo_property.html index 19a8773cb..c2a62e79c 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -400,7 +405,7 @@ property -one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname" +one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed" @@ -506,8 +511,10 @@ mo_gramstain("Klebsiella pneumoniae") #> [1] "Gram-negative" mo_snomed("Klebsiella pneumoniae") -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [[1]] +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" +#> mo_type("Klebsiella pneumoniae") #> [1] "Bacteria" mo_rank("Klebsiella pneumoniae") @@ -748,8 +755,8 @@ #> [1] "Trevisan, 1887" #> #> $snomed -#> [1] "1098101000112102" "1098201000112108" "409801009" "446870005" -#> [5] "56415008" "713926009" "714315002" +#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009" +#> [5] "56415008" "714315002" "713926009" #> # } diff --git a/reference/mo_source.html b/reference/mo_source.html index 7f67d7ff8..5dae97f8f 100644 --- a/reference/mo_source.html +++ b/reference/mo_source.html @@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -44,6 +44,11 @@ This is the fastest way to have your organisation (or analysis) specific codes p Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/pca.html b/reference/pca.html index ab16b25c1..f67028628 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -251,152 +256,15 @@ labs(title = "Title here") } } -#> Warning: Introducing NA: only 14 results available for PEN in group: order = +#> Warning: There were 73 warnings in `summarise()`. +#> The first warning was: +#> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, +#> only_all_tested = FALSE) ...`. +#> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`. +#> Caused by warning: +#> ! Introducing NA: only 14 results available for PEN in group: order = #> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for OXA in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 15 results available for OXA in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for OXA in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 13 results available for FLC in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FLC in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for TZP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 12 results available for CZO in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CZO in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for FEP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for FEP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 29 results available for FOX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for AMK in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for AMK in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for AMK in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for KAN in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for NIT in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 17 results available for NIT in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 8 results available for FOS in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for FOS in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for FOS in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for LNZ in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 5 results available for CIP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 23 results available for CIP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MFX in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for MFX in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MFX in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 6 results available for TEC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 3 results available for TCY in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for TCY in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 18 results available for TGC in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 7 results available for TGC in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for DOX in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for DOX in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 27 results available for IPM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: only 25 results available for MEM in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: only 26 results available for MEM in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Caryophanales", genus = "Staphylococcus" (minimum = 30). -#> Warning: Introducing NA: only 2 results available for MTR in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MTR in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for CHL in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: only 9 results available for COL in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Escherichia" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Klebsiella" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Enterobacterales", genus = "Proteus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for MUP in group: order = -#> "Pseudomonadales", genus = "Pseudomonas" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Enterococcus" (minimum = 30). -#> Warning: Introducing NA: no results available for RIF in group: order = -#> "Lactobacillales", genus = "Streptococcus" (minimum = 30). +#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP" #> and "TOB". Total observations available: 7. #> Groups (n=4, named as 'order'): diff --git a/reference/plot-1.png b/reference/plot-1.png index c7f0a0e23..7b06b86c3 100644 Binary files a/reference/plot-1.png and b/reference/plot-1.png differ diff --git a/reference/plot-2.png b/reference/plot-2.png index 4da7bb826..05d760db2 100644 Binary files a/reference/plot-2.png and b/reference/plot-2.png differ diff --git a/reference/plot-3.png b/reference/plot-3.png index 9dc828c91..8f7ea9a35 100644 Binary files a/reference/plot-3.png and b/reference/plot-3.png differ diff --git a/reference/plot-4.png b/reference/plot-4.png index 76abf039b..3c8519ae8 100644 Binary files a/reference/plot-4.png and b/reference/plot-4.png differ diff --git a/reference/plot-5.png b/reference/plot-5.png index be91a6a92..9d0b334fd 100644 Binary files a/reference/plot-5.png and b/reference/plot-5.png differ diff --git a/reference/plot-6.png b/reference/plot-6.png index 59a1b63fb..7de420901 100644 Binary files a/reference/plot-6.png and b/reference/plot-6.png differ diff --git a/reference/plot-7.png b/reference/plot-7.png index ea5d1e4fc..9fc466395 100644 Binary files a/reference/plot-7.png and b/reference/plot-7.png differ diff --git a/reference/plot-8.png b/reference/plot-8.png index 94cc6370a..eac02c0ad 100644 Binary files a/reference/plot-8.png and b/reference/plot-8.png differ diff --git a/reference/plot-9.png b/reference/plot-9.png index acefed1df..991774f96 100644 Binary files a/reference/plot-9.png and b/reference/plot-9.png differ diff --git a/reference/plot.html b/reference/plot.html index 4aa05522a..d3c4b1f2f 100644 --- a/reference/plot.html +++ b/reference/plot.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/proportion.html b/reference/proportion.html index a3578b281..ee2c1affd 100644 --- a/reference/proportion.html +++ b/reference/proportion.html @@ -1,6 +1,6 @@ Calculate Microbial Resistance — proportion • AMR (for R)Calculate Antimicrobial Resistance — proportion • AMR (for R) subspecies, a 3-5 letter acronym | | \\----> species, a 3-6 letter acronym | \\----> genus, a 4-8 letter acronym \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #> 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #> will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...) NA_sir_ is.sir(x) is_sir_eligible(x, threshold = 0.05) # S3 method for mic as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for disk as.sir( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE), ... ) # S3 method for data.frame as.sir( x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::clinical_breakpoints, include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) ) sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. Can also set option AMR_include_PKPD. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either: interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF summary(example_isolates) # see all SIR results at a glance #> date patient age gender #> Min. :2002-01-02 Length:2000 Min. : 0.00 Length:2000 #> 1st Qu.:2005-07-31 Class :character 1st Qu.:63.00 Class :character #> Median :2009-07-31 Mode :character Median :74.00 Mode :character #> Mean :2009-11-20 Mean :70.69 #> 3rd Qu.:2014-05-30 3rd Qu.:82.00 #> Max. :2017-12-28 Max. :97.00 #> ward mo PEN #> Length:2000 Class :mo Class:sir #> Class :character :0 %R :73.7% (n=1201) #> Mode :character Unique:90 %SI :26.3% (n=428) #> #1 :B_ESCHR_COLI - %S :25.6% (n=417) #> #2 :B_STPHY_CONS - %I : 0.7% (n=11) #> #3 :B_STPHY_AURS #> OXA FLC AMX #> Class:sir Class:sir Class:sir #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %SI :68.8% (n=251) %SI :70.5% (n=665) %SI :40.4% (n=546) #> - %S :68.8% (n=251) - %S :70.5% (n=665) - %S :40.2% (n=543) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.2% (n=3) #> #> AMC AMP TZP #> Class:sir Class:sir Class:sir #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %SI :76.3% (n=1433) %SI :40.4% (n=546) %SI :87.4% (n=875) #> - %S :71.4% (n=1342) - %S :40.2% (n=543) - %S :86.1% (n=862) #> - %I : 4.8% (n=91) - %I : 0.2% (n=3) - %I : 1.3% (n=13) #> #> CZO FEP CXM #> Class:sir Class:sir Class:sir #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %SI :55.4% (n=247) %SI :85.8% (n=621) %SI :73.7% (n=1319) #> - %S :54.9% (n=245) - %S :85.6% (n=620) - %S :72.5% (n=1297) #> - %I : 0.4% (n=2) - %I : 0.1% (n=1) - %I : 1.2% (n=22) #> #> FOX CTX CAZ #> Class:sir Class:sir Class:sir #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %SI :72.6% (n=594) %SI :84.5% (n=797) %SI :33.5% (n=607) #> - %S :71.6% (n=586) - %S :84.4% (n=796) - %S :33.5% (n=607) #> - %I : 1.0% (n=8) - %I : 0.1% (n=1) - %I : 0.0% (n=0) #> #> CRO GEN TOB #> Class:sir Class:sir Class:sir #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %SI :84.5% (n=797) %SI :75.4% (n=1399) %SI :65.6% (n=886) #> - %S :84.4% (n=796) - %S :74.0% (n=1372) - %S :65.1% (n=879) #> - %I : 0.1% (n=1) - %I : 1.5% (n=27) - %I : 0.5% (n=7) #> #> AMK KAN TMP #> Class:sir Class:sir Class:sir #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %SI :36.3% (n=251) %SI : 0.0% (n=0) %SI :61.9% (n=928) #> - %S :36.3% (n=251) - %S : 0.0% (n=0) - %S :61.2% (n=918) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.7% (n=10) #> #> SXT NIT FOS #> Class:sir Class:sir Class:sir #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %SI :79.5% (n=1398) %SI :82.9% (n=616) %SI :57.8% (n=203) #> - %S :79.1% (n=1392) - %S :76.0% (n=565) - %S :57.8% (n=203) #> - %I : 0.3% (n=6) - %I : 6.9% (n=51) - %I : 0.0% (n=0) #> #> LNZ CIP MFX #> Class:sir Class:sir Class:sir #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %SI :30.7% (n=314) %SI :83.8% (n=1181) %SI :66.4% (n=140) #> - %S :30.7% (n=314) - %S :78.9% (n=1112) - %S :64.5% (n=136) #> - %I : 0.0% (n=0) - %I : 4.9% (n=69) - %I : 1.9% (n=4) #> #> VAN TEC TCY #> Class:sir Class:sir Class:sir #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %SI :61.7% (n=1149) %SI :24.3% (n=237) %SI :70.3% (n=843) #> - %S :61.7% (n=1149) - %S :24.3% (n=237) - %S :68.3% (n=820) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 1.9% (n=23) #> #> TGC DOX ERY #> Class:sir Class:sir Class:sir #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %SI :87.3% (n=697) %SI :72.3% (n=821) %SI :42.8% (n=810) #> - %S :87.3% (n=697) - %S :71.7% (n=814) - %S :42.3% (n=801) #> - %I : 0.0% (n=0) - %I : 0.6% (n=7) - %I : 0.5% (n=9) #> #> CLI AZM IPM #> Class:sir Class:sir Class:sir #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %SI :38.8% (n=590) %SI :42.8% (n=810) %SI :93.8% (n=834) #> - %S :38.6% (n=586) - %S :42.3% (n=801) - %S :92.7% (n=824) #> - %I : 0.3% (n=4) - %I : 0.5% (n=9) - %I : 1.1% (n=10) #> #> MEM MTR CHL #> Class:sir Class:sir Class:sir #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %SI :94.1% (n=780) %SI :85.3% (n=29) %SI :78.6% (n=121) #> - %S :94.1% (n=780) - %S :85.3% (n=29) - %S :78.6% (n=121) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0) #> #> COL MUP RIF #> Class:sir Class:sir Class:sir #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %SI :18.8% (n=309) %SI :94.1% (n=254) %SI :30.4% (n=305) #> - %S :18.8% (n=309) - %S :93.0% (n=251) - %S :30.2% (n=303) #> - %I : 0.0% (n=0) - %I : 1.1% (n=3) - %I : 0.2% (n=2) #> # For INTERPRETING disk diffusion and MIC values ----------------------- # a whole data set, even with combined MIC values and disk zones df <- data.frame( microorganism = \"Escherichia coli\", AMP = as.mic(8), CIP = as.mic(0.256), GEN = as.disk(18), TOB = as.disk(16), ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #> datetime index ab_input ab_guid…¹ mo_in…² mo_guideline guide…³ #> #> 1 2023-01-24 15:36:22 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… #> 2 2023-01-24 15:36:22 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… #> 3 2023-01-24 15:36:21 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 4 2023-01-24 15:36:21 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 5 2023-01-24 15:36:15 1 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 6 2023-01-24 15:36:15 2 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 7 2023-01-24 15:36:15 3 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 8 2023-01-24 15:36:15 4 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 9 2023-01-24 15:36:15 5 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-24 15:36:15 6 CIP CIP B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table , method , #> # input , outcome , breakpoint_S_R , and abbreviated variable #> # names ¹ab_guideline, ²mo_input, ³guideline # for single values as.sir( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> Note: #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #> pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R as.sir( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'sir' #> [1] R # \\donttest{ # the dplyr way if (require(\"dplyr\")) { df %>% mutate_if(is.mic, as.sir) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df %>% mutate(across(where(is.mic), as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir) df %>% mutate(across(AMP:TOB, as.sir)) df %>% mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.sir(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.sir() # automatically determines urine isolates df %>% mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> Note: #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #> * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # For CLEANING existing SIR values ------------------------------------ as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S I R as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages barplot(sir_data) # for frequencies # the dplyr way if (require(\"dplyr\")) { example_isolates %>% mutate_at(vars(PEN:RIF), as.sir) # same: example_isolates %>% as.sir(PEN:RIF) # fastest way to transform all columns with already valid AMR results to class `sir`: example_isolates %>% mutate_if(is_sir_eligible, as.sir) # since dplyr 1.0.0, this can also be: # example_isolates %>% # mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property( atc_code, property, administration = \"O\", url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" ) atc_online_groups(atc_code, ...) atc_online_ddd(atc_code, ...) atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) { # oral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"O\") atc_online_ddd(ab_atc(\"amox\")) # parenteral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"P\") atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\" #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\" #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\" # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_av = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #> av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...) av_cid(x, ...) av_synonyms(x, ...) av_tradenames(x, ...) av_group(x, language = get_AMR_locale(), ...) av_atc(x, ...) av_loinc(x, ...) av_ddd(x, administration = \"oral\", ...) av_ddd_units(x, administration = \"oral\", ...) av_info(x, language = get_AMR_locale(), ...) av_url(x, open = FALSE, ...) av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_tradenames(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #> Aciclovir #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.sir) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...) # S3 method for bug_drug_combinations format( x, translate_ab = \"name (ab, atc)\", language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark == \",\", \".\", \",\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric decimal point. big.mark character; empty used mark every big.interval decimals (hence big) decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #> mo ab S I R total #> #> 1 (unknown species) PEN 14 0 1 15 #> 2 (unknown species) OXA 0 0 1 1 #> 3 (unknown species) FLC 0 0 0 0 #> 4 (unknown species) AMX 15 0 1 16 #> 5 (unknown species) AMC 15 0 0 15 #> 6 (unknown species) AMP 15 0 1 16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #> #> 1 \"Aminogl… Amik… \"100… \" 0.0… \"100.0… \"\" \"\" \"\" \"\" \"100.0… #> 2 \"\" Gent… \" 13… \" 2.0… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 0.9… \" 21.5… #> 3 \"\" Kana… \"100… \"\" \"100.0… \"\" \"100.0… \"\" \"\" \"100.0… #> 4 \"\" Tobr… \" 78… \" 2.6… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 2.3… \" 49.4… #> 5 \"Ampheni… Chlo… \"\" \"\" \"\" \"\" \"100.0… \"\" \" 0.0… \" 3.1… #> 6 \"Antimyc… Rifa… \"\" \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 0.0… \" 2.7… #> 7 \"Beta-la… Amox… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 8 \"\" Amox… \" 42… \" 13.1… \"\" \" 10.3… \"100.0… \" 2.8… \" 0.4… \" 54.5… #> 9 \"\" Ampi… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 10 \"\" Benz… \" 77… \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` , #> # `S. pneumoniae` , and abbreviated variable names ¹`E. coli`, #> # ²`E. faecalis`, ³`K. pneumoniae`, ⁴`P. aeruginosa`, ⁵`P. mirabilis`, #> # ⁶`S. aureus`, ⁷`S. epidermidis` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 Gram-negative PEN 8 0 717 725 #> 2 Gram-negative OXA 6 0 0 6 #> 3 Gram-negative FLC 6 0 0 6 #> 4 Gram-negative AMX 226 0 405 631 #> 5 Gram-negative AMC 463 89 174 726 #> 6 Gram-negative AMP 226 0 405 631 #> 7 Gram-negative TZP 554 11 76 641 #> 8 Gram-negative CZO 94 2 110 206 #> 9 Gram-negative FEP 470 1 14 485 #> 10 Gram-negative CXM 539 22 142 703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 E. coli PEN 0 0 467 467 #> 2 E. coli OXA 0 0 0 0 #> 3 E. coli FLC 0 0 0 0 #> 4 E. coli AMX 196 0 196 392 #> 5 E. coli AMC 332 74 61 467 #> 6 E. coli AMP 196 0 196 392 #> 7 E. coli TZP 388 5 23 416 #> 8 E. coli CZO 79 1 2 82 #> 9 E. coli FEP 308 0 9 317 #> 10 E. coli CXM 425 15 25 465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #> guideline method site mo rank_…¹ ab ref_tbl disk_…² break…³ #> #> 1 EUCAST 2… MIC NA F_ASPRG_MGTS 2 AMB Aspergi… NA 1 #> 2 EUCAST 2… MIC NA F_ASPRG_NIGR 2 AMB Aspergi… NA 1 #> 3 EUCAST 2… MIC NA F_CANDD_ALBC 2 AMB Candida NA 1 #> 4 EUCAST 2… MIC NA F_CANDD_DBLN 2 AMB Candida NA 1 #> 5 EUCAST 2… MIC NA F_CANDD_GLBR 2 AMB Candida NA 1 #> 6 EUCAST 2… MIC NA F_CANDD_KRUS 2 AMB Candida NA 1 #> 7 EUCAST 2… MIC NA F_CANDD_PRPS 2 AMB Candida NA 1 #> 8 EUCAST 2… MIC NA F_CANDD_TRPC 2 AMB Candida NA 1 #> 9 EUCAST 2… MIC NA F_CRYPT_NFRM 2 AMB Candida NA 1 #> 10 EUCAST 2… DISK NA B_[ORD]_ENTRBCTR 5 AMC Enterob… 20ug/1… 19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R , uti , and #> # abbreviated variable names ¹rank_index, ²disk_dose, ³breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_sir(..., only_all_tested = FALSE) count_df( data, translate_ab = \"name\", language = get_AMR_locale(), combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350 # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804 # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350 # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_sir(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Number of available isolates for a whole antibiotic class # (i.e., in this data set columns GEN, TOB, AMK, KAN) example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), n_sir)) # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #> ward antibiotic interpretation value #> * #> 1 Clinical AMX SI 357 #> 2 Clinical AMX R 487 #> 3 Clinical CIP SI 741 #> 4 Clinical CIP R 128 #> 5 ICU AMX SI 158 #> 6 ICU AMX R 270 #> 7 ICU CIP SI 362 #> 8 ICU CIP R 85 #> 9 Outpatient AMX SI 31 #> 10 Outpatient AMX R 47 #> 11 Outpatient CIP SI 78 #> 12 Outpatient CIP R 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work: rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\", TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"S\" then set to S : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If TZP is \"R\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"), TZP = as.sir(\"R\"), ampi = as.sir(\"S\"), cipro = as.sir(\"S\")) df #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R S S eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\", TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\") eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules( AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_…¹ rule_…² rule_…³ #> #> 1 33 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 2 33 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 3 34 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 4 34 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 5 531 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 6 531 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 7 1485 AMP Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> 8 1485 AMX Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹rule_group, ²rule_name, ³rule_source # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is \"R\" then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #> ab name type dose dose_…¹ admin…² notes origi…³ eucas…⁴ #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 … 12 #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x … 12 #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x … 12 #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1… 12 #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g … 12 #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g … 12 #> 7 AMC Amoxicillin/clavulani… high… 2 g … 3 iv \"\" (2 g a… 12 #> 8 AMC Amoxicillin/clavulani… stan… 1 g … 3 iv \"\" (1 g a… 12 #> 9 AMC Amoxicillin/clavulani… high… 0.87… 3 oral \"\" (0.875… 12 #> 10 AMC Amoxicillin/clavulani… stan… 0.5 … 3 oral \"\" (0.5 g… 12 #> # … with 326 more rows, and abbreviated variable names ¹dose_times, #> # ²administration, ³original_txt, ⁴eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules( x, col_mo = NULL, info = interactive(), rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")), verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE, custom_rules = NULL, ... ) eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\". EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv. Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylpenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) head(a) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - - - - - S S #> 2 Enterococcus faecalis - - - - - S S #> 3 Escherichia coli - - - - - S S #> 4 Klebsiella pneumoniae - - - - - S S #> 5 Pseudomonas aeruginosa - - - - - S S # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R S S S S #> 2 Enterococcus faecalis - - R R R S R #> 3 Escherichia coli R - - - - R S #> 4 Klebsiella pneumoniae R R - - - R S #> 5 Pseudomonas aeruginosa R R - - R R R # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #> - a %>% as.sir(CXM:AMX) #> - a %>% mutate_if(is_sir_eligible, as.sir) #> - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #> row col mo_fullname old new rule rule_group #> 1 1 AMX Staphylococcus aureus - S Breakpoints #> 2 1 CXM Staphylococcus aureus - S Breakpoints #> 3 1 CAZ Staphylococcus aureus R S Expert Rules #> 4 1 CAZ Staphylococcus aureus - R Expert Rules #> 5 1 COL Staphylococcus aureus - R Expert Rules #> 6 2 CAZ Enterococcus faecalis - R Expert Rules #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # } # Dosage guidelines: eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #>
microorganisms.codes.Rd
A tibble with 5,910 observations and 2 variables:
code Commonly used code of a microorganism
code
A tibble with 5 910 observations and 2 variables:
mo ID of the microorganism in the microorganisms data set
microorganisms.Rd
A tibble with 52,141 observations and 22 variables:
mo ID of microorganism as used by this package
A tibble with 52 142 observations and 22 variables:
fullname Full name, like "Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
fullname
"Escherichia coli"
status Status of the taxon, either "accepted" or "synonym"
kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank of the microorganism
kingdom
phylum
class
order
family
genus
species
subspecies
microorganisms -#> # A tibble: 52,141 × 22 +#> # A tibble: 52,142 × 22 #> mo fullname status kingdom phylum class order family genus #> <mo> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… -#> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… -#> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 1 B_GRAMN (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 2 B_GRAMP (unknown Gra… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 3 B_ANAER (unknown ana… accep… Bacter… (unkn… "(un… "(un… "(unk… "(un… +#> 4 F_FUNGUS (unknown fun… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… "(un… "(un… "(unk… "(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… "(un… "(un… "(unk… "(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "Abdi… "" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "Abd… "Abd… "" "" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… "Abd… "" "" "" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… "" "" "" "" -#> # … with 52,131 more rows, and 13 more variables: species <chr>, +#> # … with 52,132 more rows, and 13 more variables: species <chr>, #> # subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> # lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> # gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 6f8cccad9..f9d933796 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + diff --git a/reference/mo_property.html b/reference/mo_property.html index 19a8773cb..c2a62e79c 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9103 + 1.8.2.9111 @@ -42,6 +42,11 @@ Conduct AMR Analysis + + + + Generate Antibiogram (Trad./Syndromic/WISCA) + @@ -400,7 +405,7 @@ property -one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname" +one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed"
one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed", or must be "shortname"
"shortname"
one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed"