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.
-
New
-
-
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.
-
-
-
New antibiogram function
-
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 20 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").
-
With the new arguments include_PKPD (default: TRUE) and include_screening (default: FALSE), users can now specify whether breakpoints for screening and from the PK/PD table should be included when interpreting MICs and disks diffusion values. These options can be set globally, which can be read in our new manual.
-
Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.
-
-
-
Supported languages
-
We added support for the following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, 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’s system language.
-
We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 20 languages in total, and according to download stats used in almost all countries in the world!
-
-
-
Outbreak management
-
For analysis in outbreak management, we updated the get_episode() and is_new_episode() functions: they now contain an argument case_free_days. This argument can be used to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start.
-
This is common requirement in outbreak management, e.g. when determining the number of norovirus outbreaks in a hospital. The case-free period could then be 14 or 28 days, so that new norovirus cases after that time will be considered a different (or new) episode.
-
-
-
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.
-
We also made the following changes regarding the included taxonomy or microorganisms functions:
-
- Updated full microbiological taxonomy according to the latest daily LPSN data set (December 2022) and latest yearly GBIF taxonomy backbone (November 2022)
-- Added function
mo_current() to get the currently valid taxonomic name of a microorganism
-- Support for all 1,516 city-like serovars of Salmonella, such as Salmonella Goldcoast. Formally, these are serovars belonging to the S. enterica species, but they are reported with only the name of the genus and the city. For this reason, the serovars are in the
subspecies column of the microorganisms data set and “enterica” is in the species column, but the full name does not contain the species name (enterica).
-- All new algorithm for
as.mo() (and thus all mo_*() functions) while still following our original set-up as described in our recently published JSS paper (DOI 10.18637/jss.v104.i03).
-- A new argument
keep_synonyms allows to not correct for updated taxonomy, in favour of the now deleted argument allow_uncertain
-
-- It has increased tremendously in speed and returns generally more consequent results
-- Sequential coercion is now extremely fast as results are stored to the package environment, although coercion of unknown values must be run once per session. Previous results can be reset/removed with the new
mo_reset_session() function.
-- Support for microorganism codes of the ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net)
-- The MO matching score algorithm (
mo_matching_score()) now counts deletions and substitutions as 2 instead of 1, which impacts the outcome of as.mo() and any mo_*() function
-
--
-Removed all species of the taxonomic kingdom Chromista from the package. This was done for multiple reasons:
-
- CRAN allows packages to be around 5 MB maximum, some packages are exempted but this package is not one of them
-- Chromista are not relevant when it comes to antimicrobial resistance, thus lacking the primary scope of this package
-- Chromista are almost never clinically relevant, thus lacking the secondary scope of this package
-
-- 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
-
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)
-- Added some missing ATC codes
-- Updated DDDs and PubChem Compound IDs
-- Updated some antibiotic name spelling, now used by WHOCC (such as cephalexin -> cefalexin, and phenethicillin -> pheneticillin)
-- Antibiotic code “CEI” for ceftolozane/tazobactam has been replaced with “CZT” to comply with EARS-Net and WHONET 2022. The old code will still work in all cases when using
as.ab() or any of the ab_*() functions.
-- Support for antimicrobial interpretation of anaerobic bacteria, by adding a ‘placeholder’ code
B_ANAER to the microorganisms data set and adding the breakpoints of anaerobics to the clinical_breakpoints data set, which is used by as.sir() for interpretion of MIC and disk diffusion values
-
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
-
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
-
- 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.
-
-