How to conduct AMR data analysis

Dr Matthijs Berends

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14 March 2022

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12 March 2022

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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 14 March 2022.

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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 12 March 2022.

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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

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:

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date	patient_id	mo	AMX	CIP
2022-03-14	2022-03-12	abcd	Escherichia coli	S	S
2022-03-14	2022-03-12	abcd	Escherichia coli	S	R
2022-03-14	2022-03-12	efgh	Escherichia coli	R	S

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.

library(dplyr)
library(ggplot2)
library(AMR)
library(cleaner)

# (if not yet installed, install with:)
# install.packages(c("dplyr", "ggplot2", "AMR", "cleaner"))

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.

patients <- unlist(lapply(LETTERS, paste0, 1:10))

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 <- seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")

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.

sample_size <- 20000
data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
                   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)))

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:

data <- data %>% left_join(patients_table)

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)

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date	patient_id	hospital	bacteria	AMX	AMC	CIP	GEN	gender
2014-06-27	G7	Hospital B	2014-03-12	Z10	Hospital C	Escherichia coli	S	R	S	S	S	M
2014-02-01	H10	Hospital B	Streptococcus pneumoniae	2013-07-09	X5	Hospital D	Escherichia coli	R	S	S	S	F
2011-05-09	V2	Hospital C	Escherichia coli	S	2015-10-01	G6	Hospital D	Klebsiella pneumoniae	R	S	R	S	F
2016-09-06	V10	Hospital C	Escherichia coli	S	S	R	2015-08-31	B6	Hospital B	Staphylococcus aureus	S	R	S	S	M
2014-04-11	M4	Hospital D	Staphylococcus aureus	S	2010-05-21	T10	Hospital A	Escherichia coli	S	S	S	S	S	S	M
2017-06-26	D7	2011-03-10	N4	Hospital B	Staphylococcus aureus	I	S	S	R	M

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.

For example, for the gender variable:

data %>% freq(gender)

Frequency table

Class: character
Length: 20,000
Available: 20,000 (100%, NA: 0 = 0%)
Unique: 2

Shortest: 1
Longest: 1

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	Item	Count	Percent	Cum. Count	Cum. Percent
1	M	10,413	52.07%	10,413	52.07%	10,445	52.23%	10,445	52.23%
2	F	9,587	47.94%	9,555	47.78%	20,000	100.00%

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:

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 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
colnames(data)[is.rsi.eligible(data)]
# [1] "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 ampicillin = R when amoxicillin/clavulanic acid = R.

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:

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:

(…) 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.

The outcome of the function can easily be added to our data:

data <- data %>% 
  mutate(first = first_isolate(info = TRUE))
# Determining first isolates using 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
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# => Found 10,616 'phenotype-based' first isolates (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 dplyr package:

======= # => Found 10,541 'phenotype-based' first isolates (52.7% of total where a # microbial ID was available)

So only 52.7% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

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data_1st <- data %>% 
  filter(first == TRUE)

For future use, the above two syntaxes can be shortened:

data_1st <- data %>% 
  filter_first_isolate()

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So we end up with 10,616 isolates for analysis. Now our data looks like:

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So we end up with 10,541 isolates for analysis. Now our data looks like:

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head(data_1st)

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	date	patient_id	hospital	bacteria	AMX	AMC	CIP	GEN	gender	gramstain	genus	species	first
1	2014-06-27	G7	Hospital B	B_ESCHR_COLI	S	S	S	S	M	5	2010-05-21	T10	Hospital A	B_ESCHR_COLI	S	S	S	S	F	Gram-negative	Escherichia	coli	TRUE
2	2014-02-01	H10	Hospital B	B_STRPT_PNMN	R	R	7	2016-10-03	O8	Hospital C	B_ESCHR_COLI	S	S	S	S	R	M	Gram-positive	Streptococcus	pneumoniae	TRUE
3	2011-05-09	V2	8	2014-09-09	X6	Hospital C	B_ESCHR_COLI	R	R	S	S	F	Gram-negative	Escherichia	coli	TRUE
6	2017-06-26	D7	Hospital B	B_ESCHR_COLI	9	2015-06-26	E8	Hospital D	B_STPHY_AURS	S	S	S	S	R	M	Gram-positive	Staphylococcus	aureus	TRUE
7	2013-09-19	U8	Hospital B	B_ESCHR_COLI	S	S	R	S	F	Gram-negative	Escherichia	coli	TRUE
9	2012-08-01	I2	Hospital D	B_STPHY_AURS	S	S	S	S	M	11	2012-07-01	N5	Hospital A	B_STPHY_AURS	S	S	S	S	M	Gram-positive	Staphylococcus	aureus	TRUE
12	2015-10-24	Y10	Hospital A	B_STPHY_AURS	R	S	S	R	F	Gram-positive	Staphylococcus	aureus	TRUE

Time for the analysis!