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 25 February 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:

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:

date	patient_id	mo	AMX	CIP
2021-02-25	abcd	Escherichia coli	S	S
2021-02-25	abcd	Escherichia coli	S	R
2021-02-25	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)

date	patient_id	hospital	bacteria	AMX	AMC	CIP	GEN	gender
2014-07-28	B5	Hospital A	Escherichia coli	S	S	S	S	M
2017-09-25	X1	Hospital B	Klebsiella pneumoniae	R	I	S	S	F
2017-04-18	V9	Hospital D	Klebsiella pneumoniae	I	S	S	S	F
2016-06-06	Z2	Hospital A	Staphylococcus aureus	R	S	S	S	F
2016-01-08	V6	Hospital C	Klebsiella pneumoniae	S	S	S	S	F
2015-03-14	Y2	Hospital B	Staphylococcus aureus	S	S	S	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.

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

	Item	Count	Percent	Cum. Count	Cum. Percent
1	M	10,502	52.51%	10,502	52.51%
2	F	9,498	47.49%	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 mutate_at() will run the as.rsi() function on defined variables:

data <- data %>%
  mutate_at(vars(AMX:GEN), 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. 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`.

So only 28.3% 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 == 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 the antimicrobial susceptibility profile. Have a look at all E. coli isolates of patient B7, sorted on date:

isolate	date	patient_id	bacteria	AMX	AMC	CIP	GEN	first
1	2010-03-16	B7	B_ESCHR_COLI	R	S	S	S	TRUE
2	2010-05-03	B7	B_ESCHR_COLI	S	S	R	S	FALSE
3	2010-05-16	B7	B_ESCHR_COLI	R	S	S	S	FALSE
4	2010-07-25	B7	B_ESCHR_COLI	S	S	S	S	FALSE
5	2010-07-26	B7	B_ESCHR_COLI	R	R	S	R	FALSE
6	2010-10-14	B7	B_ESCHR_COLI	S	S	S	S	FALSE
7	2010-12-24	B7	B_ESCHR_COLI	R	S	S	S	FALSE
8	2011-02-27	B7	B_ESCHR_COLI	S	S	S	S	FALSE
9	2011-09-02	B7	B_ESCHR_COLI	S	S	S	R	TRUE
10	2011-11-08	B7	B_ESCHR_COLI	I	I	S	S	FALSE

Only 2 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 should 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 'keyab' as input for `col_keyantibiotics`. Use
#       col_keyantibiotics = FALSE to prevent this.

isolate	date	patient_id	bacteria	AMX	AMC	CIP	GEN	first	first_weighted
1	2010-03-16	B7	B_ESCHR_COLI	R	S	S	S	TRUE	TRUE
2	2010-05-03	B7	B_ESCHR_COLI	S	S	R	S	FALSE	TRUE
3	2010-05-16	B7	B_ESCHR_COLI	R	S	S	S	FALSE	TRUE
4	2010-07-25	B7	B_ESCHR_COLI	S	S	S	S	FALSE	TRUE
5	2010-07-26	B7	B_ESCHR_COLI	R	R	S	R	FALSE	TRUE
6	2010-10-14	B7	B_ESCHR_COLI	S	S	S	S	FALSE	TRUE
7	2010-12-24	B7	B_ESCHR_COLI	R	S	S	S	FALSE	TRUE
8	2011-02-27	B7	B_ESCHR_COLI	S	S	S	S	FALSE	TRUE
9	2011-09-02	B7	B_ESCHR_COLI	S	S	S	R	TRUE	TRUE
10	2011-11-08	B7	B_ESCHR_COLI	I	I	S	S	FALSE	TRUE

Instead of 2, now 10 isolates are flagged. In total, 79.1% of all isolates are marked ‘first weighted’ - 50.8% 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 15,821 isolates for analysis.

We can remove unneeded columns:

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

Now our data looks like:

head(data_1st)

	date	patient_id	hospital	bacteria	AMX	AMC	CIP	GEN	gender	gramstain	genus	species	first_weighted
1	2014-07-28	B5	Hospital A	B_ESCHR_COLI	S	S	S	S	M	Gram-negative	Escherichia	coli	TRUE
2	2017-09-25	X1	Hospital B	B_KLBSL_PNMN	R	I	S	S	F	Gram-negative	Klebsiella	pneumoniae	TRUE
3	2017-04-18	V9	Hospital D	B_KLBSL_PNMN	R	S	S	S	F	Gram-negative	Klebsiella	pneumoniae	TRUE
5	2016-01-08	V6	Hospital C	B_KLBSL_PNMN	R	S	S	S	F	Gram-negative	Klebsiella	pneumoniae	TRUE
6	2015-03-14	Y2	Hospital B	B_STPHY_AURS	S	S	S	S	F	Gram-positive	Staphylococcus	aureus	TRUE
7	2014-12-23	G8	Hospital A	B_STPHY_AURS	S	S	S	S	M	Gram-positive	Staphylococcus	aureus	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:

freq(paste(data_1st$genus, data_1st$species))

Or can be used like the dplyr way, which is easier readable:

data_1st %>% freq(genus, species)

Frequency table

Class: character
Length: 15,821
Available: 15,821 (100%, NA: 0 = 0%)
Unique: 4

Shortest: 16
Longest: 24

	Item	Count	Percent	Cum. Count	Cum. Percent
1	Escherichia coli	7,994	50.53%	7,994	50.53%
2	Staphylococcus aureus	3,923	24.80%	11,917	75.32%
3	Streptococcus pneumoniae	2,321	14.67%	14,238	89.99%
4	Klebsiella pneumoniae	1,583	10.01%	15,821	100.00%

Overview of different bug/drug combinations

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() %>% 
  head() # show first 6 rows

mo	ab	S	I	R	total
E. coli	AMX	3815	239	3940	7994
E. coli	AMC	6289	304	1401	7994
E. coli	CIP	6086	0	1908	7994
E. coli	GEN	7158	0	836	7994
K. pneumoniae	AMX	0	0	1583	1583
K. pneumoniae	AMC	1224	57	302	1583

Using Tidyverse selections, you can also select columns based on the antibiotic class they are in:

data_1st %>% 
  select(bacteria, fluoroquinolones()) %>% 
  bug_drug_combinations()

# Selecting fluoroquinolones: column 'CIP' (ciprofloxacin)

mo	ab	S	R	total
E. coli	CIP	6086	1908	7994
K. pneumoniae	CIP	1215	368	1583
S. aureus	CIP	3062	861	3923
S. pneumoniae	CIP	1765	556	2321

This will only give you the crude numbers in the data. To calculate antimicrobial resistance, we use the resistance() and susceptibility() functions.

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.

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] 0.5370078

Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:

data_1st %>% 
  group_by(hospital) %>% 
  summarise(amoxicillin = resistance(AMX))

# `summarise()` ungrouping output (override with `.groups` argument)

hospital	amoxicillin
Hospital A	0.5410321
Hospital B	0.5381272
Hospital C	0.5306981
Hospital D	0.5338134

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))

# `summarise()` ungrouping output (override with `.groups` argument)

hospital	amoxicillin	available
Hospital A	0.5410321	4728
Hospital B	0.5381272	5521
Hospital C	0.5306981	2378
Hospital D	0.5338134	3194

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))

# `summarise()` ungrouping output (override with `.groups` argument)

genus	amoxiclav	gentamicin	amoxiclav_genta
Escherichia	0.8247436	0.8954216	0.9836127
Klebsiella	0.8092230	0.9001895	0.9823121
Staphylococcus	0.8179964	0.9166454	0.9836860
Streptococcus	0.5489013	0.0000000	0.5489013

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:
  tidyr::pivot_longer(-genus, names_to = "antibiotic") %>% 
  ggplot(aes(x = genus,
             y = value,
             fill = antibiotic)) +
  geom_col(position = "dodge2")
# `summarise()` ungrouping output (override with `.groups` argument)

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,
                     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))

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:

ggplot(data_1st) +
  geom_rsi(translate_ab = FALSE)

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)) + 
  # 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()

Plotting MIC and disk diffusion values

The AMR package also extends the plot() and ggplot() 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] 128    64     0.5    8      64     0.125  16     64     8      2     
#  [11] 0.0625 8      1      4      0.25   0.5    0.5    32     1      1     
#  [21] 0.25   8      8      0.0625 16     16     2      64     0.125  8     
#  [31] 1      0.125  4      0.125  32     16     0.25   1      0.5    128   
#  [41] 32     128    64     1      0.125  32     0.125  0.125  8      0.125 
#  [51] 64     8      8      0.0625 0.25   0.5    2      2      128    0.25  
#  [61] 4      1      1      0.25   64     0.5    32     0.25   16     2     
#  [71] 16     8      32     4      0.25   16     128    32     128    4     
#  [81] 64     1      0.25   0.5    1      1      4      8      16     16    
#  [91] 32     0.5    1      0.125  2      128    0.5    1      4      64

# 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:

# this will generate MICs that are likely to be found in E. coli for ciprofloxacin:
mic_values_specific <- random_mic(size = 100, mo = "E. coli", ab = "cipro")

For the plot() and 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_specific, mo = "E. coli", ab = "cipro")

# ggplot2:
ggplot(mic_values_specific, mo = "E. coli", ab = "cipro")

For disk diffusion values, there is not much of a difference in plotting:

# this will generate disks that are likely to be found in E. coli for ciprofloxacin:
disk_values_specific <- random_disk(size = 100, mo = "E. coli", ab = "cipro")
# NOTE: Translation to one microorganism was guessed with uncertainty. Use
#       `mo_uncertainties()` to review it.
disk_values_specific
# Class <disk>
#   [1] 20 29 20 26 27 22 18 21 29 23 18 19 29 26 27 24 20 20 23 20 18 25 31 31 29
#  [26] 28 27 31 18 21 18 26 30 20 21 27 24 27 19 24 17 26 24 31 30 28 19 30 19 21
#  [51] 17 19 22 19 23 23 19 20 28 31 31 30 20 17 21 31 29 29 27 21 29 27 26 18 21
#  [76] 29 21 26 29 22 24 18 22 29 30 17 19 23 26 31 17 18 18 28 30 25 22 21 27 17

# base R:
plot(disk_values_specific, 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”):

# and ggplot2, but now choosing an old CLSI guideline:
ggplot(disk_values_specific,
       mo = "E. coli",
       ab = "cipro",
       guideline = "CLSI")

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)

check_FOS <- example_isolates %>%
  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.

How to conduct AMR data analysis

Matthijs S. Berends

25 February 2021