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"))

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.

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)

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

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 28.4% 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 C3, sorted on date:

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.

Instead of 2, now 9 isolates are flagged. In total, 78.7% of all isolates are marked ‘first weighted’ - 50.3% 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 15,742 isolates for analysis.

We can remove unneeded columns:

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

Now our data looks like:

head(data_1st)

Time for the analysis!

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: 15,742
Available: 15,742 (100%, NA: 0 = 0%)
Unique: 4

Shortest: 16
Longest: 24

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

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)

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] 0.5393851

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))

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:
  tidyr::pivot_longer(-genus, names_to = "antibiotic") %>% 
  ggplot(aes(x = genus,
             y = value,
             fill = antibiotic)) +
  geom_col(position = "dodge2")

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()

mic_values <- random_mic(size = 100)
mic_values
# Class <mic>
#   [1] 8        2        16       256      0.25     8        64       8       
#   [9] >=512    1        0.25     <=0.0625 2        0.5      128      16      
#  [17] 4        256      <=0.0625 16       0.125    16       4        <=0.0625
#  [25] 2        32       1        0.125    0.125    >=512    2        32      
#  [33] 128      0.25     256      4        2        <=0.0625 128      >=512   
#  [41] 0.25     0.125    32       128      16       0.125    8        256     
#  [49] 32       1        16       64       8        0.25     0.25     128     
#  [57] 0.125    <=0.0625 0.125    2        0.25     0.125    <=0.0625 0.25    
#  [65] 8        16       256      8        0.5      256      >=512    8       
#  [73] 0.25     128      0.25     8        >=512    8        0.25     8       
#  [81] 64       16       2        8        0.5      0.125    >=512    >=512   
#  [89] 0.25     0.125    >=512    256      256      32       0.25     >=512   
#  [97] 64       0.125    128      8

# base R:
plot(mic_values)

# ggplot2:
ggplot(mic_values)

mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")

# base R:
plot(mic_values, mo = "E. coli", ab = "cipro")

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

disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro")
# NOTE: Translation is uncertain of one microorganism. Use
#       `mo_uncertainties()` to review it.
disk_values
# Class <disk>
#   [1] 26 26 18 28 20 20 29 24 22 29 28 25 22 18 27 17 19 24 26 18 20 18 22 30 19
#  [26] 29 21 31 31 25 26 17 28 31 31 31 23 18 22 22 26 22 21 21 25 19 28 31 18 21
#  [51] 21 19 17 20 26 18 30 28 29 30 19 28 27 23 19 27 31 23 26 20 22 28 21 26 22
#  [76] 24 27 18 19 22 27 29 23 28 27 26 25 17 17 22 29 22 22 30 22 21 20 25 24 22

# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")

ggplot(disk_values,
       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.