Danish, 
Dutch, 
English, 
French, 
German, 
Italian, 
Portuguese, 
Russian, 
Spanish and 
Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
-
Used in 175 countries
Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
+
+Used in 175 countries
Since its first public
+release in early 2018, this R package has been used in almost all
+countries in the world. Click the map to enlarge and to see the country
+names.
How to conduct AMR data analysis
-Dr. Matthijs Berends
+Dr Matthijs +Berends
-23 December 2021
+10 March 2022
Source:vignettes/AMR.Rmd
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 23 December 2021.
-
-
Introduction -
-Conducting AMR data analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
+Note: values on this page will change with every +website update since they are based on randomly created values and the +page was written in R +Markdown. However, the methodology remains unchanged. This page was +generated on 10 March 2022.
+
+
-Introduction +
+Conducting AMR data analysis unfortunately requires in-depth +knowledge from different scientific fields, which makes it hard to do +right. At least, it requires:
- Good questions (always start with those!) -
- A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results -
- A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance -
- Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values -
- Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD) -
- Available (inter-)national guidelines, and profound methods to apply them +
- A thorough understanding of (clinical) epidemiology, to understand +the clinical and epidemiological relevance and possible bias of +results +
- A thorough understanding of (clinical) microbiology/infectious +diseases, to understand which microorganisms are causal to which +infections and the implications of pharmaceutical treatment, as well as +understanding intrinsic and acquired microbial resistance +
- Experience with data analysis with microbiological tests and their +results, to understand the determination and limitations of MIC values +and their interpretations to RSI values +
- Availability of the biological taxonomy of microorganisms and +probably normalisation factors for pharmaceuticals, such as defined +daily doses (DDD) +
- Available (inter-)national guidelines, and profound methods to apply +them
Of course, we cannot instantly provide you with knowledge and experience. But with this AMR package, we aimed at providing (1) tools to simplify antimicrobial resistance data cleaning, transformation and analysis, (2) methods to easily incorporate international guidelines and (3) scientifically reliable reference data, including the requirements mentioned above.
The AMR package enables standardised and reproducible AMR data analysis, with the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends.
Of course, we cannot instantly provide you with knowledge and
+experience. But with this AMR package, we aimed at
+providing (1) tools to simplify antimicrobial resistance data cleaning,
+transformation and analysis, (2) methods to easily incorporate
+international guidelines and (3) scientifically reliable reference data,
+including the requirements mentioned above.
The AMR package enables standardised and reproducible
+AMR data analysis, with the application of evidence-based rules,
+determination of first isolates, translation of various codes for
+microorganisms and antimicrobial agents, determination of (multi-drug)
+resistant microorganisms, and calculation of antimicrobial resistance,
+prevalence and future trends.
-
Preparation -
-For this tutorial, we will create fake demonstration data to work with.
-You can skip to Cleaning the data if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
+
+
-
Preparation +
+For this tutorial, we will create fake demonstration data to work +with.
+You can skip to Cleaning the data if +you already have your own data ready. If you start your analysis, try to +make the structure of your data generally look like this:
| date | @@ -231,21 +261,21 @@|||||
|---|---|---|---|---|---|
| 2021-12-23 | +2022-03-10 | abcd | Escherichia coli | S | S |
| 2021-12-23 | +2022-03-10 | abcd | Escherichia coli | S | R |
| 2021-12-23 | +2022-03-10 | efgh | Escherichia coli | R | @@ -253,59 +283,84 @@
-
-Needed R packages -
-As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by RStudio. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
We will also use the cleaner package, that can be used for cleaning data and creating frequency tables.
+
Needed R packages +
+As with many uses in R, we need some additional packages for AMR data
+analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by
+RStudio. The tidyverse tremendously improves the way we conduct data
+science - it allows for a very natural way of writing syntaxes and
+creating beautiful plots in R.
We will also use the cleaner package, that can be used
+for cleaning data and creating frequency tables.
-
Creation of data -
-We will create some fake example data to use for analysis. For AMR data analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).
-With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.
-Patients
+
Patients
+Creation of data
+
We will create some fake example data to use for analysis. For AMR +data analysis, we need at least: a patient ID, name or code of a +microorganism, a date and antimicrobial results (an antibiogram). It +could also include a specimen type (e.g. to filter on blood or urine), +the ward type (e.g. to filter on ICUs).
+With additional columns (like a hospital name, the patients gender of +even [well-defined] clinical properties) you can do a comparative +analysis, as this tutorial will demonstrate too.
+
+
-Patients +
To start with patients, we need a unique list of patients.
-The LETTERS object is available in R - it’s a vector with 26 characters: A to Z. The patients object we just created is now a vector of length 260, with values (patient IDs) varying from A1 to Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
The LETTERS object is available in R - it’s a vector
+with 26 characters: A to Z. The
+patients object we just created is now a vector of length
+260, with values (patient IDs) varying from A1 to
+Z10. Now we we also set the gender of our patients, by
+putting the ID and the gender in a table:
patients_table <- data.frame(patient_id = patients,
gender = c(rep("M", 135),
rep("F", 125)))The first 135 patient IDs are now male, the other 125 are female.
-
Dates -
-Let’s pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.
+
+
Dates +
+Let’s pretend that our data consists of blood cultures isolates from +between 1 January 2010 and 1 January 2018.
-This dates object now contains all days in our date range.
-
-Microorganisms -
-For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
+This dates object now contains all days in our date
+range.
+
Microorganisms +
+For this tutorial, we will uses four different microorganisms: +Escherichia coli, Staphylococcus aureus, +Streptococcus pneumoniae, and Klebsiella +pneumoniae:
bacteria <- c("Escherichia coli", "Staphylococcus aureus",
"Streptococcus pneumoniae", "Klebsiella pneumoniae")
-
-Put everything together -
-Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results, using the random_rsi() function.
+
-
+
Put everything together +
+Using the sample() function, we can randomly select
+items from all objects we defined earlier. To let our fake data reflect
+reality a bit, we will also approximately define the probabilities of
+bacteria and the antibiotic results, using the random_rsi()
+function.
sample_size <- 20000
data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
@@ -322,13 +377,28 @@
AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)),
CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)),
GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00)))Using the left_join() function from the dplyr package, we can ‘map’ the gender to the patient ID using the patients_table object we created earlier:
Using the left_join() function from the
+dplyr package, we can ‘map’ the gender to the patient ID
+using the patients_table object we created earlier:
The resulting data set contains 20,000 blood culture isolates. With the head() function we can preview the first 6 rows of this data set:
The resulting data set contains 20,000 blood culture isolates. With
+the head() function we can preview the first 6 rows of this
+data set:
head(data)| date | patient_id | @@ -342,87 +412,89 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010-03-07 | -U8 | +2018-01-01 | +Z9 | Hospital B | -Escherichia coli | -S | +Staphylococcus aureus | R | S | S | +S | F | ||
| 2015-08-15 | -A4 | +2010-04-06 | +G1 | +Hospital B | +Staphylococcus aureus | +R | +S | +S | +S | +M | +||||
| 2016-12-08 | +I1 | +Hospital B | +Staphylococcus aureus | +R | +I | +S | +S | +M | +||||||
| 2015-12-05 | +R10 | Hospital C | Escherichia coli | S | S | S | S | -M | +F | |||||
| 2012-02-22 | -T7 | -Hospital A | -Staphylococcus aureus | -S | -S | +2014-11-30 | +N6 | +Hospital C | +Escherichia coli | +R | S | +R | S | F |
| 2017-04-28 | -J10 | -Hospital A | -Klebsiella pneumoniae | +2012-10-12 | +I6 | +Hospital B | +Escherichia coli | S | S | S | S | M | ||
| 2013-05-14 | -X5 | -Hospital A | -Escherichia coli | -S | -I | -S | -S | -F | -||||||
| 2011-09-09 | -Z1 | -Hospital A | -Escherichia coli | -R | -R | -R | -S | -F | -
Now, let’s start the cleaning and the analysis!
-
Cleaning the data -
-We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
+
1
M
-10,513
-52.56%
-10,513
-52.56%
+10,395
+51.98%
+10,395
+51.98%
2
F
-9,487
-47.44%
+9,605
+48.03%
20,000
100.00%
-
-
-Cleaning the data +
+We also created a package dedicated to data cleaning and checking,
+called the cleaner package. It freq() function
+can be used to create frequency tables.
For example, for the gender variable:
Frequency table
Class: character
Length: 20,000
-Available: 20,000 (100.0%, NA: 0 = 0.0%)
+Available: 20,000 (100%, NA: 0 = 0%)
Unique: 2
Shortest: 1
Longest: 1
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate() function of the dplyr package makes this really easy:
So, we can draw at least two conclusions immediately. From a data
+scientists perspective, the data looks clean: only values M
+and F. From a researchers perspective: there are slightly
+more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some
+variables. The bacteria column now consists of text, and we
+want to add more variables based on microbial IDs later on. So, we will
+transform this column to valid IDs. The mutate() function
+of the dplyr package makes this really easy:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The is.rsi.eligible() can check which columns are probably columns with R/SI test results. Using mutate() and across(), we can apply the transformation to the formal <rsi> class:
We also want to transform the antibiotics, because in real life data
+we don’t know if they are really clean. The as.rsi()
+function ensures reliability and reproducibility in these kind of
+variables. The is.rsi.eligible() can check which columns
+are probably columns with R/SI test results. Using mutate()
+and across(), we can apply the transformation to the formal
+<rsi> class:
is.rsi.eligible(data)
# [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
@@ -468,30 +553,73 @@ Longest: 1
data <- data %>%
mutate(across(where(is.rsi.eligible), as.rsi))Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column AMX) and amoxicillin/clavulanic acid (column AMC) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The eucast_rules() fixes this:
Finally, we will apply EUCAST
+rules on our antimicrobial results. In Europe, most medical
+microbiological laboratories already apply these rules. Our package
+features their latest insights on intrinsic resistance and exceptional
+phenotypes. Moreover, the eucast_rules() function can also
+apply additional rules, like forcing
+
Because the amoxicillin (column AMX) and
+amoxicillin/clavulanic acid (column AMC) in our data were
+generated randomly, some rows will undoubtedly contain AMX = S and AMC =
+R, which is technically impossible. The eucast_rules()
+fixes this:
data <- eucast_rules(data, col_mo = "bacteria", rules = "all")
-
Adding new variables -
-Now that we have the microbial ID, we can add some taxonomic properties:
+
+
-
Adding new variables +
+Now that we have the microbial ID, we can add some taxonomic +properties:
data <- data %>%
mutate(gramstain = mo_gramstain(bacteria),
genus = mo_genus(bacteria),
species = mo_species(bacteria))
-
First isolates -
-We also need to know which isolates we can actually use for analysis.
-To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.
-The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:
+
+
-
-
-
+
-
@@ -933,49 +1088,68 @@ Longest: 24
E. coli
GEN
-4002
+3997
0
-607
-4609
+599
+4596
K. pneumoniae
GEN
-1063
+1070
0
-145
-1208
+114
+1184
S. aureus
GEN
-2446
+2476
0
-327
-2773
+329
+2805
S. pneumoniae
GEN
0
0
-2087
-2087
+2128
+2128
-
-First isolates +
+We also need to know which isolates we can actually use for +analysis.
+To conduct an analysis of antimicrobial resistance, you must only include the first +isolate of every patient per episode (Hindler et al., Clin +Infect Dis. 2007). If you would not do this, you could easily get an +overestimate or underestimate of the resistance of an antibiotic. +Imagine that a patient was admitted with an MRSA and that it was found +in 5 different blood cultures the following weeks (yes, some countries +like the Netherlands have these blood drawing policies). The resistance +percentage of oxacillin of all isolates would be overestimated, because +you included this MRSA more than once. It would clearly be selection +bias.
+The Clinical and Laboratory Standards Institute (CLSI) appoints this +as follows:
--(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
+
M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4(…) When preparing a cumulative antibiogram to guide clinical +decisions about empirical antimicrobial therapy of initial infections, +only the first isolate of a given species per patient, per +analysis period (eg, one year) should be included, irrespective of body +site, antimicrobial susceptibility profile, or other phenotypical +characteristics (eg, biotype). The first isolate is easily +identified, and cumulative antimicrobial susceptibility test data +prepared using the first isolate are generally comparable to cumulative +antimicrobial susceptibility test data calculated by other methods, +providing duplicate isolates are excluded.
M39-A4 +Analysis and Presentation of Cumulative Antimicrobial Susceptibility +Test Data, 4th Edition. CLSI, 2014. Chapter 6.4
This AMR package includes this methodology with the first_isolate() function and is able to apply the four different methods as defined by Hindler et al. in 2007: phenotype-based, episode-based, patient-based, isolate-based. The right method depends on your goals and analysis, but the default phenotype-based method is in any case the method to properly correct for most duplicate isolates. This method also takes into account the antimicrobial susceptibility test results using all_microbials(). Read more about the methods on the first_isolate() page.
This AMR package includes this methodology with the
+first_isolate() function and is able to apply the four
+different methods as defined by Hindler
+et al. in 2007: phenotype-based, episode-based,
+patient-based, isolate-based. The right method depends on your goals and
+analysis, but the default phenotype-based method is in any case the
+method to properly correct for most duplicate isolates. This method also
+takes into account the antimicrobial susceptibility test results using
+all_microbials(). Read more about the methods on the
+first_isolate() page.
The outcome of the function can easily be added to our data:
data <- data %>%
@@ -502,9 +630,11 @@ Longest: 1
# ℹ Using column 'patient_id' as input for `col_patient_id`.
# Basing inclusion on all antimicrobial results, using a points threshold of
# 2
-# => Found 10,677 'phenotype-based' first isolates (53.4% of total where a
+# => Found 10,713 'phenotype-based' first isolates (53.6% of total where a
# microbial ID was available)So only 53.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 53.6% is suitable for resistance analysis! We can now filter
+on it with the filter() function, also from the
+dplyr package:
data_1st <- data %>%
filter_first_isolate()So we end up with 10,677 isolates for analysis. Now our data looks like:
+So we end up with 10,713 isolates for analysis. Now our data looks +like:
head(data_1st)| - | date | -patient_id | -hospital | -bacteria | -AMX | -AMC | -CIP | -GEN | -gender | -gramstain | -genus | -species | -first | -
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-03-07 | -U8 | -Hospital B | -B_ESCHR_COLI | -R | -R | -S | -S | -F | -Gram-negative | -Escherichia | -coli | -TRUE | -
| 2 | -2015-08-15 | -A4 | -Hospital C | -B_ESCHR_COLI | -S | -S | -S | -S | -M | -Gram-negative | -Escherichia | -coli | -TRUE | -
| 4 | -2017-04-28 | -J10 | -Hospital A | -B_KLBSL_PNMN | -R | -S | -S | -S | -M | -Gram-negative | -Klebsiella | -pneumoniae | -TRUE | -
| 6 | -2011-09-09 | -Z1 | -Hospital A | -B_ESCHR_COLI | -R | -R | -R | -S | -F | -Gram-negative | -Escherichia | -coli | -TRUE | -
| 7 | -2017-04-11 | -I3 | -Hospital B | -B_ESCHR_COLI | -S | -S | -R | -R | -M | -Gram-negative | -Escherichia | -coli | -TRUE | -
| 10 | -2011-06-22 | -D10 | -Hospital B | -B_KLBSL_PNMN | -R | -S | -S | -S | -M | -Gram-negative | -Klebsiella | -pneumoniae | -TRUE | -
Time for the analysis!
- - -
-
+Analysing the data -
-You might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis. Although this package contains a convenient function to make frequency tables, exploratory data analysis (EDA) is not the primary scope of this package. Use a package like DataExplorer for that, or read the free online book Exploratory Data Analysis with R by Roger D. Peng.
-
-
-
-Dispersion of species -
-To just get an idea how the species are distributed, create a frequency table with our freq() function. We created the genus and species column earlier based on the microbial ID. With paste(), we can concatenate them together.
The freq() function can be used like the base R language was intended:
Or can be used like the dplyr way, which is easier readable:
Frequency table
-Class: character
-Length: 10,677
-Available: 10,677 (100.0%, NA: 0 = 0.0%)
-Unique: 4
Shortest: 16
-Longest: 24
| - | Item | -Count | -Percent | -Cum. Count | -Cum. Percent | -
|---|---|---|---|---|---|
| 1 | -Escherichia coli | -4,609 | -43.17% | -4,609 | -43.17% | -
| 2 | -Staphylococcus aureus | -2,773 | -25.97% | -7,382 | -69.14% | -
| 3 | -Streptococcus pneumoniae | -2,087 | -19.55% | -9,469 | -88.69% | -
| 4 | -Klebsiella pneumoniae | -1,208 | -11.31% | -10,677 | -100.00% | -
-
-
-
+
+Overview of different bug/drug combinations -
-Using tidyverse selections, you can also select or filter columns based on the antibiotic class they are in:
-
-data_1st %>%
- filter(any(aminoglycosides() == "R"))# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)| date | patient_id | hospital | @@ -757,23 +681,252 @@ Longest: 24|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-04-11 | -I3 | +3 | +2016-12-08 | +I1 | Hospital B | +B_STPHY_AURS | +R | +I | +S | +S | +M | +Gram-positive | +Staphylococcus | +aureus | +TRUE | +
| 5 | +2014-11-30 | +N6 | +Hospital C | +B_ESCHR_COLI | +R | +S | +R | +S | +F | +Gram-negative | +Escherichia | +coli | +TRUE | +||
| 8 | +2016-12-03 | +J3 | +Hospital A | +B_KLBSL_PNMN | +R | +S | +R | +R | +M | +Gram-negative | +Klebsiella | +pneumoniae | +TRUE | +||
| 9 | +2010-04-28 | +J10 | +Hospital A | B_ESCHR_COLI | S | S | R | -R | +S | M | Gram-negative | Escherichia | coli | TRUE | |
| 15 | +2010-08-04 | +L1 | +Hospital B | +B_STRPT_PNMN | +R | +R | +S | +R | +M | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +||
| 2013-07-04 | -C4 | +17 | +2014-04-28 | +Q5 | +Hospital C | +B_STRPT_PNMN | +R | +R | +S | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +
Time for the analysis!
+
+
Analysing the data +
+You might want to start by getting an idea of how the data is
+distributed. It’s an important start, because it also decides how you
+will continue your analysis. Although this package contains a convenient
+function to make frequency tables, exploratory data analysis (EDA) is
+not the primary scope of this package. Use a package like DataExplorer
+for that, or read the free online book Exploratory Data Analysis
+with R by Roger D. Peng.
+
+
+
+Dispersion of species +
+To just get an idea how the species are distributed, create a
+frequency table with our freq() function. We created the
+genus and species column earlier based on the
+microbial ID. With paste(), we can concatenate them
+together.
The freq() function can be used like the base R language
+was intended:
Or can be used like the dplyr way, which is easier
+readable:
Frequency table
+Class: character
+Length: 10,713
+Available: 10,713 (100%, NA: 0 = 0%)
+Unique: 4
Shortest: 16
+Longest: 24
| + | Item | +Count | +Percent | +Cum. Count | +Cum. Percent | +
|---|---|---|---|---|---|
| 1 | +Escherichia coli | +4,596 | +42.90% | +4,596 | +42.90% | +
| 2 | +Staphylococcus aureus | +2,805 | +26.18% | +7,401 | +69.08% | +
| 3 | +Streptococcus pneumoniae | +2,128 | +19.86% | +9,529 | +88.95% | +
| 4 | +Klebsiella pneumoniae | +1,184 | +11.05% | +10,713 | +100.00% | +
+
+
+
-
Overview of different bug/drug combinations +
+Using tidyverse +selections, you can also select or filter columns based on the +antibiotic class they are in:
+
+data_1st %>%
+ filter(any(aminoglycosides() == "R"))# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)| date | +patient_id | +hospital | +bacteria | +AMX | +AMC | +CIP | +GEN | +gender | +gramstain | +genus | +species | +first | +||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-12-03 | +J3 | +Hospital A | +B_KLBSL_PNMN | +R | +S | +R | +R | +M | +Gram-negative | +Klebsiella | +pneumoniae | +TRUE | +||
| 2010-08-04 | +L1 | Hospital B | B_STRPT_PNMN | R | @@ -787,38 +940,8 @@ Longest: 24TRUE | |||||||||
| 2010-11-18 | -F8 | -Hospital B | -B_STPHY_AURS | -R | -R | -S | -R | -M | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -||
| 2017-06-06 | -H4 | -Hospital D | -B_ESCHR_COLI | -R | -R | -S | -R | -M | -Gram-negative | -Escherichia | -coli | -TRUE | -||
| 2014-02-16 | -V2 | +2014-04-28 | +Q5 | Hospital C | B_STRPT_PNMN | R | @@ -832,12 +955,42 @@ Longest: 24TRUE | |||||||
| 2013-02-16 | -M1 | +2015-05-06 | +L9 | Hospital D | +B_ESCHR_COLI | +R | +R | +S | +R | +M | +Gram-negative | +Escherichia | +coli | +TRUE | +
| 2010-09-03 | +M3 | +Hospital A | B_STRPT_PNMN | +S | +S | R | R | +M | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +||
| 2011-10-13 | +D4 | +Hospital C | +B_STRPT_PNMN | +S | +S | S | R | M | @@ -848,7 +1001,9 @@ Longest: 24
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
If you want to get a quick glance of the number of isolates in
+different bug/drug combinations, you can use the
+bug_drug_combinations() function:
data_1st %>%
bug_drug_combinations() %>%
@@ -867,50 +1022,50 @@ Longest: 24
E. coli
AMX
-2190
-112
-2307
-4609
+2197
+113
+2286
+4596
E. coli
AMC
-3359
-170
-1080
-4609
+3395
+149
+1052
+4596
E. coli
CIP
-3366
+3341
0
-1243
-4609
+1255
+4596
E. coli
GEN
-4002
+3997
0
-607
-4609
+599
+4596
K. pneumoniae
AMX
0
0
-1208
-1208
+1184
+1184
K. pneumoniae
AMC
-951
-35
-222
-1208
+917
+43
+224
+1184
This will only give you the crude numbers in the data. To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the resistance() and susceptibility() functions.
This will only give you the crude numbers in the data. To calculate
+antimicrobial resistance in a more sensible way, also by correcting for
+too few results, we use the resistance() and
+susceptibility() functions.
-
@@ -190,7 +190,7 @@
@@ -199,42 +199,71 @@
-
Resistance percentages -
-The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
+
-
+Resistance percentages +
+The functions resistance() and
+susceptibility() can be used to calculate antimicrobial
+resistance or susceptibility. For more specific analyses, the functions
+proportion_S(), proportion_SI(),
+proportion_I(), proportion_IR() and
+proportion_R() can be used to determine the proportion of a
+specific antimicrobial outcome.
All these functions contain a minimum argument, denoting
+the minimum required number of test results for returning a value. These
+functions will otherwise return NA. The default is
+minimum = 30, following the CLSI
+M39-A4 guideline for applying microbial epidemiology.
As per the EUCAST guideline of 2019, we calculate resistance as the
+proportion of R (proportion_R(), equal to
+resistance()) and susceptibility as the proportion of S and
+I (proportion_SI(), equal to
+susceptibility()). These functions can be used on their
+own:
data_1st %>% resistance(AMX)
-# [1] 0.544254Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
Or can be used in conjunction with group_by() and
+summarise(), both from the dplyr package:
data_1st %>%
group_by(hospital) %>%
@@ -988,23 +1162,27 @@ Longest: 24
Hospital A
-0.5349716
+0.5412355
Hospital B
-0.5482514
+0.5406417
Hospital C
-0.5800508
+0.5450746
Hospital D
-0.5244591
+0.5452821
-Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
+Of course it would be very convenient to know the number of isolates
+responsible for the percentages. For that purpose the
+n_rsi() can be used, which works exactly like
+n_distinct() from the dplyr package. It counts
+all isolates available for every group (i.e. values S, I or R):
data_1st %>%
group_by(hospital) %>%
@@ -1019,27 +1197,29 @@ Longest: 24
Hospital A
-0.5349716
-3174
+0.5412355
+3189
Hospital B
-0.5482514
-3803
+0.5406417
+3740
Hospital C
-0.5800508
-1574
+0.5450746
+1675
Hospital D
-0.5244591
-2126
+0.5452821
+2109
-These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
+These functions can also be used to get the proportion of multiple
+antibiotics, to calculate empiric susceptibility of combination
+therapies very easily:
data_1st %>%
group_by(genus) %>%
@@ -1056,31 +1236,34 @@ Longest: 24
Escherichia
-0.7656759
-0.8683011
-0.9709264
+0.7711053
+0.8696693
+0.9738903
Klebsiella
-0.8162252
-0.8799669
-0.9726821
+0.8108108
+0.9037162
+0.9805743
Staphylococcus
-0.7991345
-0.8820772
-0.9772809
+0.7907308
+0.8827094
+0.9732620
Streptococcus
-0.5462386
+0.5324248
0.0000000
-0.5462386
+0.5324248
-Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as penicillins(), which automatically will include the columns AMX and AMC of our data:
+Or if you are curious for the resistance within certain antibiotic
+classes, use a antibiotic class selector such as
+penicillins(), which automatically will include the columns
+AMX and AMC of our data:
data_1st %>%
# group by hospital
@@ -1101,27 +1284,28 @@ Longest: 24
Hospital A
-53.5%
-26.0%
+54.1%
+27.2%
Hospital B
-54.8%
-26.1%
+54.1%
+27.4%
Hospital C
-58.0%
-28.5%
+54.5%
+25.0%
Hospital D
-52.4%
-25.4%
+54.5%
+25.9%
-To make a transition to the next part, let’s see how differences in the previously calculated combination therapies could be plotted:
+To make a transition to the next part, let’s see how differences in
+the previously calculated combination therapies could be plotted:
-
-Plots
-
-To show results in plots, most R users would nowadays use the ggplot2 package. This package lets you create plots in layers. You can read more about it on their website. A quick example would look like these syntaxes:
+
+Plots
+
+To show results in plots, most R users would nowadays use the
+ggplot2 package. This package lets you create plots in
+layers. You can read more about it on their website. A quick
+example would look like these syntaxes:
ggplot(data = a_data_set,
mapping = aes(x = year,
@@ -1153,13 +1340,21 @@ Longest: 24
# or as short as:
ggplot(a_data_set) +
geom_bar(aes(year))
-The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or proportion_df() and show results in stacked bars. Its simplest and shortest example:
+The AMR package contains functions to extend this
+ggplot2 package, for example geom_rsi(). It
+automatically transforms data with count_df() or
+proportion_df() and show results in stacked bars. Its
+simplest and shortest example:
-Omit the translate_ab = FALSE to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin).
-If we group on e.g. the genus column and add some additional functions from our package, we can create this:
+Omit the translate_ab = FALSE to have the antibiotic
+codes (AMX, AMC, CIP, GEN) translated to official WHO names
+(amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin,
+gentamicin).
+If we group on e.g. the genus column and add some
+additional functions from our package, we can create this:
# group the data on `genus`
ggplot(data_1st %>% group_by(genus)) +
@@ -1182,7 +1377,8 @@ Longest: 24
# (is now y axis because we turned the plot)
theme(axis.text.y = element_text(face = "italic"))
-To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:
+To simplify this, we also created the ggplot_rsi()
+function, which combines almost all above functions:
data_1st %>%
group_by(genus) %>%
@@ -1192,25 +1388,30 @@ Longest: 24
datalabels = FALSE) +
coord_flip()
-
-Plotting MIC and disk diffusion values
-
-The AMR package also extends the plot() and ggplot2::autoplot() functions for plotting minimum inhibitory concentrations (MIC, created with as.mic()) and disk diffusion diameters (created with as.disk()).
-With the random_mic() and random_disk() functions, we can generate sampled values for the new data types (S3 classes) <mic> and <disk>:
+
+Plotting MIC and disk diffusion values
+
+The AMR package also extends the plot() and
+ggplot2::autoplot() functions for plotting minimum
+inhibitory concentrations (MIC, created with as.mic()) and
+disk diffusion diameters (created with as.disk()).
+With the random_mic() and random_disk()
+functions, we can generate sampled values for the new data types (S3
+classes) <mic> and <disk>:
mic_values <- random_mic(size = 100)
mic_values
# Class <mic>
-# [1] 0.5 1 0.002 0.125 0.025 2 8 0.125 0.125 4
-# [11] 0.0625 0.001 0.025 0.001 8 64 0.025 32 0.001 64
-# [21] 8 0.001 16 0.5 0.0625 0.002 128 8 0.125 4
-# [31] 0.125 64 32 0.001 0.0625 0.005 2 4 0.125 32
-# [41] 0.125 0.5 0.0625 128 0.125 0.125 0.01 0.25 0.5 0.002
-# [51] 8 8 0.002 0.002 0.25 256 1 8 2 2
-# [61] 0.0625 0.005 0.005 16 0.5 0.025 16 8 8 0.0625
-# [71] 0.002 0.125 0.025 0.025 1 32 0.25 16 128 8
-# [81] 0.0625 0.025 64 0.001 0.5 4 8 4 16 32
-# [91] 0.005 8 0.125 256 64 0.001 1 32 0.005 0.025
+# [1] 1 128 256 8 64 0.5 4 32 16 0.025
+# [11] 16 0.025 2 1 0.025 64 32 32 8 0.5
+# [21] 0.025 256 1 0.005 0.001 1 16 0.025 1 0.002
+# [31] 0.0625 0.125 0.0625 0.01 0.01 4 64 0.002 0.25 128
+# [41] 64 16 16 0.005 0.125 0.5 0.5 0.5 1 32
+# [51] 0.25 0.125 8 8 0.25 256 2 256 0.125 128
+# [61] 0.001 0.001 32 4 16 256 2 0.01 4 8
+# [71] 0.001 0.125 1 0.01 0.25 8 1 0.005 64 0.025
+# [81] 256 16 1 64 8 32 0.002 0.01 4 0.0625
+# [91] 0.025 0.002 0.005 0.01 256 0.125 16 1 0.5 0.01
# base R:
plot(mic_values)
@@ -1219,11 +1420,17 @@ Longest: 24
# ggplot2:
autoplot(mic_values)
-But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
+But we could also be more specific, by generating MICs that are
+likely to be found in E. coli for ciprofloxacin:
mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")
-For the plot() and autoplot() function, we can define the microorganism and an antimicrobial agent the same way. This will add the interpretation of those values according to a chosen guidelines (defaults to the latest EUCAST guideline).
-Default colours are colour-blind friendly, while maintaining the convention that e.g. ‘susceptible’ should be green and ‘resistant’ should be red:
+For the plot() and autoplot() function, we
+can define the microorganism and an antimicrobial agent the same way.
+This will add the interpretation of those values according to a chosen
+guidelines (defaults to the latest EUCAST guideline).
+Default colours are colour-blind friendly, while maintaining the
+convention that e.g. ‘susceptible’ should be green and ‘resistant’
+should be red:
# base R:
plot(mic_values, mo = "E. coli", ab = "cipro")
@@ -1232,22 +1439,25 @@ Longest: 24
# ggplot2:
autoplot(mic_values, mo = "E. coli", ab = "cipro")
-For disk diffusion values, there is not much of a difference in plotting:
+For disk diffusion values, there is not much of a difference in
+plotting:
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro")
# ℹ Function `as.mo()` is uncertain about "E. coli" (assuming Escherichia
# coli). Run `mo_uncertainties()` to review this.
disk_values
# Class <disk>
-# [1] 29 17 17 18 17 29 28 25 21 24 30 29 20 26 21 28 22 21 28 25 25 22 29 29 27
-# [26] 19 30 19 25 28 27 26 19 16 29 20 19 25 16 27 25 19 30 23 20 20 25 23 25 17
-# [51] 19 21 20 25 19 25 22 16 29 25 25 21 29 29 28 24 30 23 22 22 31 26 20 19 30
-# [76] 30 28 26 25 26 27 16 25 27 21 29 17 31 16 27 22 19 26 20 21 19 27 21 24 27
+# [1] 28 24 25 16 19 16 27 30 25 19 27 24 27 26 18 28 21 25 22 25 19 21 19 29 31
+# [26] 28 25 16 22 16 26 28 20 18 27 26 29 28 19 26 30 21 30 27 28 18 27 28 31 18
+# [51] 29 22 17 22 22 19 19 25 19 24 16 22 16 28 22 27 26 25 20 25 26 19 18 17 27
+# [76] 24 24 18 16 28 29 25 16 23 28 29 16 19 29 17 21 26 17 19 25 23 17 22 25 20
# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")
-And when using the ggplot2 package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term “Susceptible, incr. exp.” has changed to “Intermediate”):
+And when using the ggplot2 package, but now choosing the
+latest implemented CLSI guideline (notice that the EUCAST-specific term
+“Susceptible, incr. exp.” has changed to “Intermediate”):
autoplot(disk_values,
mo = "E. coli",
@@ -1256,11 +1466,17 @@ Longest: 24
-
-Independence test
-
-The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.
-We will compare the resistance to fosfomycin (column FOS) in hospital A and D. The input for the fisher.test() can be retrieved with a transformation like this:
+
+Independence test
+
+The next example uses the example_isolates data set.
+This is a data set included with this package and contains 2,000
+microbial isolates with their full antibiograms. It reflects reality and
+can be used to practice AMR data analysis.
+We will compare the resistance to fosfomycin (column
+FOS) in hospital A and D. The input for the
+fisher.test() can be retrieved with a transformation like
+this:
# use package 'tidyr' to pivot data:
library(tidyr)
@@ -1294,7 +1510,9 @@ Longest: 24
# sample estimates:
# odds ratio
# 0.4488318
-As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
+As can be seen, the p value is 0.031, which means that the fosfomycin
+resistance found in isolates from patients in hospital A and D are
+really different.
@@ -1311,12 +1529,14 @@ Longest: 24
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diff --git a/docs/articles/datasets.html b/docs/articles/datasets.html
index 96ba838f..c1109448 100644
--- a/docs/articles/datasets.html
+++ b/docs/articles/datasets.html
@@ -44,7 +44,7 @@
Data sets for download / own use
-03 March 2022
+10 March 2022
Source:vignettes/datasets.Rmd
datasets.RmdAll reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
On this page, we explain how to download them and how the structure of the data sets look like.
+All reference data (about microorganisms, antibiotics, R/SI
+interpretation, EUCAST rules, etc.) in this AMR package are
+reliable, up-to-date and freely available. We continually export our
+data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also
+supply tab separated files that are machine-readable and suitable for
+input in any software program, such as laboratory information
+systems.
On this page, we explain how to download them and how the structure +of the data sets look like.
-If you are reading this page from within R, please visit our website, which is automatically updated with every code change. +If you are reading this page from within R, please +visit +our website, which is automatically updated with every code change.
Microorganisms (currently accepted names)
-A data set with 70,760 rows and 16 columns, containing the following column names:
mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence and snomed.
This data set is in R available as microorganisms, after you load the AMR package.
It was last updated on 29 November 2021 11:38:23 UTC. Find more info about the structure of this data set here.
+A data set with 70,760 rows and 16 columns, containing the following
+column names:
mo, fullname, kingdom, phylum,
+class, order, family, genus,
+species, subspecies, rank, ref,
+species_id, source, prevalence and
+snomed.
This data set is in R available as microorganisms, after
+you load the AMR package.
It was last updated on 29 November 2021 11:38:23 UTC. Find more info +about the structure of this data set here.
Direct download links:
-
-
- Download as R file (1.3 MB)
+ - Download as R
+file (1.3 MB)
- - Download as Excel file (6.4 MB)
+ - Download as Excel
+file (6.4 MB)
- - Download as plain text file (13.1 MB)
+ - Download as plain
+text file (13.1 MB)
- - Download as SAS file (30.7 MB)
+ - Download as SAS
+file (30.7 MB)
- - Download as SPSS file (16.3 MB)
+ - Download as SPSS
+file (16.3 MB)
- - Download as Stata file (27.5 MB) +
- Download as Stata +file (27.5 MB)
NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub. Advice? Use R instead.
+NOTE: The exported files for SAS, SPSS and Stata do not +contain SNOMED codes, as their file size would exceed 100 MB; the file +size limit of GitHub. Advice? Use R instead.
Source
-Our full taxonomy of microorganisms is based on the authoritative and comprehensive:
+Our full taxonomy of microorganisms is based on the authoritative and +comprehensive:
- -Catalogue of Life (included version: 2019) +Catalogue of Life +(included version: 2019)
- -List of Prokaryotic names with Standing in Nomenclature (LPSN, last updated: 5 October 2021) -
- US Edition of SNOMED CT from 1 September 2020, retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12 +List of Prokaryotic names with +Standing in Nomenclature (LPSN, last updated: 5 October 2021) +
- US Edition of SNOMED CT from 1 September 2020, retrieved from the Public +Health Information Network Vocabulary Access and Distribution System +(PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12
@@ -427,40 +456,64 @@ If you are reading this page from within R, please
Microorganisms (previously accepted names)
-A data set with 14,338 rows and 4 columns, containing the following column names:
fullname, fullname_new, ref and prevalence.
Note: remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column ‘fullname’. For the scientific reference of the new names, i.e. of column ‘fullname_new’, see the microorganisms data set.
This data set is in R available as microorganisms.old, after you load the AMR package.
It was last updated on 6 October 2021 14:38:29 UTC. Find more info about the structure of this data set here.
+A data set with 14,338 rows and 4 columns, containing the following
+column names:
fullname, fullname_new, ref and
+prevalence.
Note: remember that the ‘ref’ columns contains the
+scientific reference to the old taxonomic entries, i.e. of column
+‘fullname’. For the scientific reference of the new names,
+i.e. of column ‘fullname_new’, see the
+microorganisms data set.
This data set is in R available as microorganisms.old,
+after you load the AMR package.
It was last updated on 6 October 2021 14:38:29 UTC. Find more info +about the structure of this data set here.
Direct download links:
-
-
- Download as R file (0.2 MB)
+ - Download as R
+file (0.2 MB)
- - Download as Excel file (0.5 MB)
+ - Download as Excel
+file (0.5 MB)
- - Download as plain text file (1 MB)
+ - Download as plain
+text file (1 MB)
- - Download as SAS file (2.1 MB)
+ - Download as SAS
+file (2.1 MB)
- - Download as SPSS file (1.3 MB)
+ - Download as SPSS
+file (1.3 MB)
- - Download as Stata file (2 MB) +
- Download as Stata +file (2 MB)
Source
-This data set contains old, previously accepted taxonomic names. The data sources are the same as the microorganisms data set:
This data set contains old, previously accepted taxonomic names. The
+data sources are the same as the microorganisms data
+set:
- -Catalogue of Life (included version: 2019) +Catalogue of Life +(included version: 2019)
- -List of Prokaryotic names with Standing in Nomenclature (LPSN, last updated: 5 October 2021) +List of Prokaryotic names with +Standing in Nomenclature (LPSN, last updated: 5 October 2021)
Example content
Example rows when filtering on Escherichia:
-
