Note: values on this page will be regenerated with every website update since it is written in RMarkdown, so actual results will change over time. However, the methodology remains unchanged. This page was generated on 31 December 2018.

Introduction

(work in progress)

Tutorial

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	patient_id	mo	amox	cipr
2018-12-31	abcd	Escherichia coli	S	S
2018-12-31	abcd	Escherichia coli	S	R
2018-12-31	efgh	Escherichia coli	R	S

Needed R packages

As with many uses in R, we need some additional packages for AMR analysis. The most important one is dplyr, which tremendously improves the way we work with data - it allows for a very natural way of writing syntaxes in R. Another important dependency is ggplot2. This package can be used to create beautiful plots in R.

Our AMR package depends on these packages and even extends their use and functions.

library(dplyr)   # the data science package
library(AMR)     # this package, to simplify and automate AMR analysis
library(ggplot2) # for appealing plots

Creation of data

We will create some fake example data to use for analysis. For antimicrobial resistance 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 1 January 2010 until 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")

Other variables

For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:

hospitals <- c("Hospital A", "Hospital B", "Hospital C", "Hospital D")
ab_interpretations <- c("S", "I", "R")

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 with the prob parameter.

data <- data.frame(date = sample(dates, 5000, replace = TRUE),
                   patient_id = sample(patients, 5000, replace = TRUE),
                   hospital = sample(hospitals, 5000, replace = TRUE),
                   bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
                   amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.6, 0.05, 0.35)),
                   amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.1, 0.15)),
                   cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.8, 0, 0.2)),
                   gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0, 0.07))
                   )

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 5,000 blood culture isolates. With the head() function we can preview the first 6 values of this data set:

head(data)

date	patient_id	hospital	bacteria	amox	amcl	cipr	gent	gender
2015-12-16	C5	Hospital A	Staphylococcus aureus	I	S	S	S	M
2016-11-26	Y1	Hospital D	Streptococcus pneumoniae	S	I	R	R	F
2016-08-27	E3	Hospital B	Escherichia coli	S	R	S	S	M
2014-01-30	V9	Hospital A	Staphylococcus aureus	R	S	S	S	F
2010-05-14	S10	Hospital B	Staphylococcus aureus	S	S	S	S	F
2015-12-21	N8	Hospital B	Escherichia coli	S	S	S	S	F

Now, let’s start the cleaning and the analysis!

Cleaning the data

Use the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:

data %>% freq(gender) # this would be the same: freq(data$gender)

# Frequency table of `gender` 
# Class:   factor (numeric)  
# Levels:  F, M  
# Length:  5,000 (of which NA: 0 = 0.00%)  
# Unique:  2
# 
#      Item    Count   Percent   Cum. Count   Cum. Percent
# ---  -----  ------  --------  -----------  -------------
# 1    M       2,568     51.4%        2,568          51.4%
# 2    F       2,432     48.6%        5,000         100.0%

So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher 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 mutate_at() will run the as.rsi() function on defined variables:

data <- data %>%
  mutate_at(vars(amox:cipr), 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 amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:

data <- eucast_rules(data, col_mo = "bacteria")
# 
# Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST)
# 
# EUCAST Clinical Breakpoints (v8.1, 2018)
# Enterobacteriales (Order) (no changes)
# Staphylococcus (no changes)
# Enterococcus (no changes)
# Streptococcus groups A, B, C, G (no changes)
# Streptococcus pneumoniae (358 changes)
# Viridans group streptococci (no changes)
# Haemophilus influenzae (no changes)
# Moraxella catarrhalis (no changes)
# Anaerobic Gram positives (no changes)
# Anaerobic Gram negatives (no changes)
# Pasteurella multocida (no changes)
# Campylobacter jejuni and C. coli (no changes)
# Aerococcus sanguinicola and A. urinae (no changes)
# Kingella kingae (no changes)
# 
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
# Table 1:  Intrinsic resistance in Enterobacteriaceae (341 changes)
# Table 2:  Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
# Table 3:  Intrinsic resistance in other Gram-negative bacteria (no changes)
# Table 4:  Intrinsic resistance in Gram-positive bacteria (704 changes)
# Table 8:  Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
# Table 9:  Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
# Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)
# Table 12: Interpretive rules for aminoglycosides (no changes)
# Table 13: Interpretive rules for quinolones (no changes)
# 
# Other rules
# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (366 changes)
# Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes)
# Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes)
# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (217 changes)
# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
# 
# => EUCAST rules affected 4,613 out of 5,000 rows -> changed 1,986 test results.

Adding new variables

Now we have the microbial ID, we can add some taxonomic properties:

data <- data %>% 
  mutate(gramstain = mo_gramstain(bacteria),
         family = mo_family(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.
Chapter 6.4, M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. https://clsi.org/standards/products/microbiology/documents/m39/

This AMR package includes this methodology with the first_isolate() function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:

data <- data %>% 
  mutate(first = first_isolate(.))
# NOTE: Using column `bacteria` as input for `col_mo`.
# NOTE: Using column `date` as input for `col_date`.
# NOTE: Using column `patient_id` as input for `col_patient_id`.
# => Found 2,941 first isolates (58.8% of total)

So only 58.8% is suitable for resistance analysis! We can now filter on is with the filter() function, also from the dplyr package:

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

For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:

data_1st <- data %>% 
  filter_first_isolate()

First weighted isolates

We made a slight twist to the CLSI algorithm, to take into account antimicrobial results. Imagine this data, sorted on date:

isolate	date	patient_id	bacteria	amox	amcl	cipr	gent	first
1	2010-08-17	D4	B_ESCHR_COL	S	S	S	S	TRUE
2	2011-07-17	D4	B_ESCHR_COL	S	S	S	S	FALSE
3	2012-02-06	D4	B_ESCHR_COL	S	S	S	S	TRUE
4	2012-06-16	D4	B_ESCHR_COL	R	S	S	S	FALSE
5	2012-09-13	D4	B_ESCHR_COL	S	S	S	S	FALSE
6	2013-06-02	D4	B_ESCHR_COL	S	S	S	S	TRUE
7	2014-12-11	D4	B_ESCHR_COL	S	S	S	S	TRUE
8	2015-03-23	D4	B_ESCHR_COL	S	S	S	S	FALSE
9	2015-04-14	D4	B_ESCHR_COL	S	S	S	S	FALSE
10	2015-05-19	D4	B_ESCHR_COL	R	S	S	S	FALSE

Only 4 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and show be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.

If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:

data <- data %>% 
  mutate(keyab = key_antibiotics(.)) %>% 
  mutate(first_weighted = first_isolate(.))
# NOTE: Using column `bacteria` as input for `col_mo`.
# Warning: These columns do not exist and will be ignored: cfur, pita, trsu, vanc, teic, tetr, eryt, oxac, rifa, tobr, coli, cfot, cfta, mero.
# THIS MAY STRONGLY INFLUENCE THE OUTCOME.
# NOTE: Using column `bacteria` as input for `col_mo`.
# NOTE: Using column `date` as input for `col_date`.
# NOTE: Using column `patient_id` as input for `col_patient_id`.
# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
# [Criterion] Inclusion based on key antibiotics, ignoring I.
# => Found 4,415 first weighted isolates (88.3% of total)

isolate	date	patient_id	bacteria	amox	amcl	cipr	gent	first	first_weighted
1	2010-08-17	D4	B_ESCHR_COL	S	S	S	S	TRUE	TRUE
2	2011-07-17	D4	B_ESCHR_COL	S	S	S	S	FALSE	FALSE
3	2012-02-06	D4	B_ESCHR_COL	S	S	S	S	TRUE	TRUE
4	2012-06-16	D4	B_ESCHR_COL	R	S	S	S	FALSE	TRUE
5	2012-09-13	D4	B_ESCHR_COL	S	S	S	S	FALSE	TRUE
6	2013-06-02	D4	B_ESCHR_COL	S	S	S	S	TRUE	TRUE
7	2014-12-11	D4	B_ESCHR_COL	S	S	S	S	TRUE	TRUE
8	2015-03-23	D4	B_ESCHR_COL	S	S	S	S	FALSE	FALSE
9	2015-04-14	D4	B_ESCHR_COL	S	S	S	S	FALSE	FALSE
10	2015-05-19	D4	B_ESCHR_COL	R	S	S	S	FALSE	TRUE

Instead of 4, now 7 isolates are flagged. In total, 88.3% of all isolates are marked ‘first weighted’ - 29.5% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.

As with filter_first_isolate(), there’s a shortcut for this new algorithm too:

data_1st <- data %>% 
  filter_first_weighted_isolate()

So we end up with 4,415 isolates for analysis.

We can remove unneeded columns:

data_1st <- data_1st %>% 
  select(-first, -keyab)

Now our data looks like:

head(data_1st)

date	patient_id	hospital	bacteria	amox	amcl	cipr	gent	gender	gramstain	family	first_weighted
2015-12-16	C5	Hospital A	B_STPHY_AUR	I	S	S	S	M	Gram positive	Staphylococcaceae	TRUE
2016-11-26	Y1	Hospital D	B_STRPTC_PNE	S	S	R	R	F	Gram positive	Streptococcaceae	TRUE
2016-08-27	E3	Hospital B	B_ESCHR_COL	R	R	S	S	M	Gram negative	Enterobacteriaceae	TRUE
2014-01-30	V9	Hospital A	B_STPHY_AUR	R	S	S	S	F	Gram positive	Staphylococcaceae	TRUE
2010-05-14	S10	Hospital B	B_STPHY_AUR	S	S	S	S	F	Gram positive	Staphylococcaceae	TRUE
2015-12-21	N8	Hospital B	B_ESCHR_COL	S	S	S	S	F	Gram negative	Enterobacteriaceae	TRUE

Time for the analysis!

Analysing the data