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Conducting AMR data analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
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.
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.
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:
As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the tidyverse packages
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.
library(dplyr) library(ggplot2) library(AMR) library(cleaner) # (if not yet installed, install with:) # install.packages(c("dplyr", "ggplot2", "AMR", "cleaner"))
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.
To start with patients, we need a unique list of patients.
<- unlist(lapply(LETTERS, paste0, 1:10))patients
LETTERS object is available in R - it’s a vector with 26 characters:
patients object we just created is now a vector of length 260, with values (patient IDs) varying from
Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
<- data.frame(patient_id = patients, patients_table gender = c(rep("M", 135), rep("F", 125)))
The first 135 patient IDs are now male, the other 125 are female.
Let’s pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.
<- seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")dates
dates object now contains all days in our date range.
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
<- c("Escherichia coli", "Staphylococcus aureus", bacteria "Streptococcus pneumoniae", "Klebsiella pneumoniae")
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
<- 20000 sample_size <- data.frame(date = sample(dates, size = sample_size, replace = TRUE), data patient_id = sample(patients, size = sample_size, replace = TRUE), hospital = sample(c("Hospital A", "Hospital B", "Hospital C", "Hospital D"), size = sample_size, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)), bacteria = sample(bacteria, size = sample_size, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)), AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)), 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)))
left_join() function from the
dplyr package, we can ‘map’ the gender to the patient ID using the
patients_table object we created earlier:
<- data %>% left_join(patients_table)data
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:
|2017-08-29||U5||Hospital D||Escherichia coli||S||I||S||S||F|
|2012-10-19||U5||Hospital B||Streptococcus pneumoniae||S||S||S||S||F|
|2010-02-13||W10||Hospital A||Escherichia coli||R||R||S||R||F|
|2017-03-20||Y1||Hospital B||Staphylococcus aureus||R||S||S||S||F|
|2016-08-03||U6||Hospital B||Staphylococcus aureus||R||S||S||S||F|
|2015-06-16||X5||Hospital B||Staphylococcus aureus||S||R||R||S||F|
Now, let’s start the cleaning and the analysis!
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
Available: 20,000 (100.0%, NA: 0 = 0.0%)
|Item||Count||Percent||Cum. Count||Cum. Percent|
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values
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:
<- data %>% data mutate(bacteria = as.mo(bacteria))
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
across(), we can apply the transformation to the formal
is.rsi.eligible(data) #  FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE colnames(data)[is.rsi.eligible(data)] #  "AMX" "AMC" "CIP" "GEN" <- 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:
<- eucast_rules(data, col_mo = "bacteria", rules = "all")data
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))
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
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
The outcome of the function can easily be added to our data:
<- data %>% data mutate(first = first_isolate(info = TRUE)) # Determining first isolates using the 'phenotype-based' method and an # episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. # ℹ Using column 'date' as input for `col_date`. # ℹ Using column 'patient_id' as input for `col_patient_id`. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # => Found 10,622 first weighted isolates (phenotype-based, 53.1% of total # where a microbial ID was available)
So only 53.1% is suitable for resistance analysis! We can now filter on it with the
filter() function, also from the
<- data %>% data_1st filter(first == TRUE)
For future use, the above two syntaxes can be shortened:
<- data %>% data_1st filter_first_isolate()
So we end up with 10,622 isolates for analysis. Now our data looks like:
Time for the analysis!
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.
To just get an idea how the species are distributed, create a frequency table with our
freq() function. We created the
species column earlier based on the microbial ID. With
paste(), we can concatenate them together.
freq() function can be used like the base R language was intended:
Or can be used like the
dplyr way, which is easier readable:
%>% freq(genus, species)data_1st
Available: 10,622 (100.0%, NA: 0 = 0.0%)
|Item||Count||Percent||Cum. Count||Cum. Percent|
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)
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the
%>% data_1st bug_drug_combinations() %>% head() # show first 6 rows
# ℹ Using column 'bacteria' as input for `col_mo`.
%>% data_1st select(bacteria, aminoglycosides()) %>% bug_drug_combinations()
# ℹ For `aminoglycosides()` using column: 'GEN' (gentamicin) # ℹ Using column 'bacteria' as input for `col_mo`.
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
susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions
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(AMX) data_1st #  0.540482
Or can be used in conjunction with
summarise(), both from the
%>% data_1st group_by(hospital) %>% summarise(amoxicillin = resistance(AMX))
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) %>% summarise(amoxicillin = resistance(AMX), available = n_rsi(AMX))
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) %>% summarise(amoxiclav = susceptibility(AMC), gentamicin = susceptibility(GEN), amoxiclav_genta = susceptibility(AMC, GEN))
To make a transition to the next part, let’s see how this difference could be plotted:
%>% data_1st group_by(genus) %>% summarise("1. Amoxi/clav" = susceptibility(AMC), "2. Gentamicin" = susceptibility(GEN), "3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>% # pivot_longer() from the tidyr package "lengthens" data: ::pivot_longer(-genus, names_to = "antibiotic") %>% tidyrggplot(aes(x = genus, y = value, fill = antibiotic)) + geom_col(position = "dodge2")
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, y = value)) + geom_col() + labs(title = "A title", subtitle = "A subtitle", x = "My X axis", y = "My Y axis") # or as short as: ggplot(a_data_set) + geom_bar(aes(year))
AMR package contains functions to extend this
ggplot2 package, for example
geom_rsi(). It automatically transforms data with
proportion_df() and show results in stacked bars. Its simplest and shortest example:
ggplot(data_1st) + geom_rsi(translate_ab = FALSE)
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)) + # create bars with genus on x axis # it looks for variables with class `rsi`, # of which we have 4 (earlier created with `as.rsi`) geom_rsi(x = "genus") + # split plots on antibiotic facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations (colour-blind friendly) scale_rsi_colours() + # show percentages on y axis scale_y_percent(breaks = 0:4 * 25) + # turn 90 degrees, to make it bars instead of columns coord_flip() + # add labels labs(title = "Resistance per genus and antibiotic", subtitle = "(this is fake data)") + # and print genus in italic to follow our convention # (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:
%>% data_1st group_by(genus) %>% ggplot_rsi(x = "genus", facet = "antibiotic", breaks = 0:4 * 25, datalabels = FALSE) + coord_flip()
The AMR package also extends the
ggplot() functions for plotting minimum inhibitory concentrations (MIC, created with
as.mic()) and disk diffusion diameters (created with
random_disk() functions, we can generate sampled values for the new data types (S3 classes)
<- random_mic(size = 100) mic_values mic_values# Class <mic> #  0.25 8 0.25 2 4 2 1 0.5 #  8 16 0.25 >=128 8 2 <=0.0625 64 #  <=0.0625 64 32 32 2 1 64 64 #  0.125 0.25 2 <=0.0625 32 16 8 2 #  <=0.0625 4 1 4 0.25 <=0.0625 32 >=128 #  0.125 >=128 0.25 0.25 <=0.0625 64 2 16 #  16 0.5 0.5 8 >=128 64 >=128 0.125 #  64 4 0.5 1 8 16 4 <=0.0625 #  64 <=0.0625 4 2 64 16 32 16 #  2 0.5 4 0.25 1 2 0.25 0.5 #  0.25 2 4 1 0.125 0.25 8 0.125 #  16 4 <=0.0625 4 1 1 <=0.0625 0.125 #  >=128 >=128 <=0.0625 1
# base R: plot(mic_values)
# ggplot2: ggplot(mic_values)
But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
<- random_mic(size = 100, mo = "E. coli", ab = "cipro")mic_values
ggplot() 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")
# ggplot2: ggplot(mic_values, mo = "E. coli", ab = "cipro")
For disk diffusion values, there is not much of a difference in plotting:
<- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # ℹ Translation is uncertain of one microorganism. Use `mo_uncertainties()` # to review it. disk_values# Class <disk> #  23 17 25 30 29 25 17 26 27 21 20 25 17 29 20 18 20 17 18 22 27 31 30 17 19 #  28 26 20 20 18 30 30 28 24 25 19 27 31 31 24 29 20 28 20 29 22 29 22 22 26 #  31 28 21 31 20 24 27 24 19 25 31 29 18 17 29 31 20 17 23 17 25 18 27 28 31 #  30 24 26 29 25 19 30 25 27 27 23 21 28 29 26 31 31 27 27 17 28 24 29 18 17
# 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 “Incr. exposure” has changed to “Intermediate”):
ggplot(disk_values, mo = "E. coli", ab = "cipro", guideline = "CLSI")
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) <- example_isolates %>% check_FOS filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D select(hospital_id, FOS) %>% # select the hospitals and fosfomycin group_by(hospital_id) %>% # group on the hospitals count_df(combine_SI = TRUE) %>% # count all isolates per group (hospital_id) pivot_wider(names_from = hospital_id, # transform output so A and D are columns values_from = value) %>% select(A, D) %>% # and only select these columns as.matrix() # transform to a good old matrix for fisher.test() check_FOS# A D # [1,] 25 77 # [2,] 24 33
We can apply the test now with:
# do Fisher's Exact Test fisher.test(check_FOS) # # Fisher's Exact Test for Count Data # # data: check_FOS # p-value = 0.03104 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: # 0.2111489 0.9485124 # 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.