--- title: "How to conduct AMR data analysis" author: "Matthijs S. Berends" date: '`r format(Sys.Date(), "%d %B %Y")`' output: rmarkdown::html_vignette: toc: true toc_depth: 3 vignette: > %\VignetteIndexEntry{How to conduct AMR data analysis} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console --- ```{r setup, include = FALSE, results = 'markup'} knitr::opts_chunk$set( warning = FALSE, collapse = TRUE, comment = "#", fig.width = 7.5, fig.height = 5 ) ``` **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](https://rmarkdown.rstudio.com/). However, the methodology remains unchanged. This page was generated on `r format(Sys.Date(), "%d %B %Y")`. # 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](#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: ```{r example table, echo = FALSE, results = 'asis'} knitr::kable(data.frame(date = Sys.Date(), patient_id = c("abcd", "abcd", "efgh"), mo = "Escherichia coli", AMX = c("S", "S", "R"), CIP = c("S", "R", "S"), stringsAsFactors = FALSE), align = "c") ``` ## 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](https://www.tidyverse.org) [`dplyr`](https://dplyr.tidyverse.org/) and [`ggplot2`](https://ggplot2.tidyverse.org) 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. ```{r lib packages, message = FALSE, warning = FALSE, results = 'asis'} 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. ```{r create 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 `r length(patients)`, with values (patient IDs) varying from ``r patients[1]`` to ``r patients[length(patients)]``. Now we we also set the gender of our patients, by putting the ID and the gender in a table: ```{r create gender} 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. ```{r create dates} 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*: ```{r mo} 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: ```{r create other} 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. ```{r merge data} 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(hospitals, 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 = sample(ab_interpretations, size = sample_size, replace = TRUE, prob = c(0.60, 0.05, 0.35)), AMC = sample(ab_interpretations, size = sample_size, replace = TRUE, prob = c(0.75, 0.10, 0.15)), CIP = sample(ab_interpretations, size = sample_size, replace = TRUE, prob = c(0.80, 0.00, 0.20)), GEN = sample(ab_interpretations, size = sample_size, replace = TRUE, prob = c(0.92, 0.00, 0.08))) ``` 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: ```{r merge data 2, message = FALSE, warning = FALSE} data <- data %>% left_join(patients_table) ``` The resulting data set contains `r format(nrow(data), big.mark = ",")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set: ```{r preview data set 1, eval = FALSE} head(data) ``` ```{r preview data set 2, echo = FALSE, results = 'asis'} knitr::kable(head(data), align = "c") ``` 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: ```{r freq gender 1, results="asis"} data %>% freq(gender) ``` 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: ```{r transform mo 1} 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: ```{r transform abx} data <- data %>% mutate_at(vars(AMX:GEN), as.rsi) ``` Finally, we will apply [EUCAST rules](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) 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: ```{r eucast, warning = FALSE, message = FALSE} 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: ```{r new taxo} 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](https:/pubmed.ncbi.nlm.nih.gov/17304462/) (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 \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would clearly be [selection bias](https://en.wikipedia.org/wiki/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](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: ```{r 1st isolate} data <- data %>% mutate(first = first_isolate()) ``` So only `r percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package: ```{r 1st isolate filter} data_1st <- data %>% filter(first == TRUE) ``` For future use, the above two syntaxes can be shortened with the `filter_first_isolate()` function: ```{r 1st isolate filter 2, eval = FALSE} data_1st <- data %>% filter_first_isolate() ``` ## First *weighted* isolates ```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'} weighted_df <- data %>% filter(bacteria == as.mo("Escherichia coli")) %>% # only most prevalent patient filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>% arrange(date) %>% select(date, patient_id, bacteria, AMX:GEN, first) %>% # maximum of 10 rows .[1:min(10, nrow(.)),] %>% mutate(isolate = row_number()) %>% select(isolate, everything()) ``` 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 `r as.data.frame(weighted_df[1, 'patient_id'])`, sorted on date: ```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'} weighted_df %>% knitr::kable(align = "c") ``` Only `r sum(weighted_df$first)` isolates are marked as 'first' according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The `key_antibiotics()` function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user. If a column exists with a name like 'key(...)ab' the `first_isolate()` function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output: ```{r 1st weighted, warning = FALSE} data <- data %>% mutate(keyab = key_antibiotics()) %>% mutate(first_weighted = first_isolate()) ``` ```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'} weighted_df2 <- data %>% filter(bacteria == as.mo("Escherichia coli")) %>% # only most prevalent patient filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>% arrange(date) %>% select(date, patient_id, bacteria, AMX:GEN, first, first_weighted) %>% # maximum of 10 rows .[1:min(10, nrow(.)),] %>% mutate(isolate = row_number()) %>% select(isolate, everything()) weighted_df2 %>% knitr::kable(align = "c") ``` Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r percentage(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r percentage((sum(data$first_weighted) / nrow(data)) - (sum(data$first) / nrow(data)))` 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: ```{r 1st isolate filter 3, results = 'hide', message = FALSE, warning = FALSE} data_1st <- data %>% filter_first_weighted_isolate() ``` So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analysis. We can remove unneeded columns: ```{r} data_1st <- data_1st %>% select(-c(first, keyab)) ``` Now our data looks like: ```{r preview data set 3, eval = FALSE} head(data_1st) ``` ```{r preview data set 4, echo = FALSE, results = 'asis'} knitr::kable(head(data_1st), align = "c") ``` 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`](https://cran.r-project.org/package=DataExplorer) for that, or read the free online book [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/) 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: ```{r freq 1, eval = FALSE} freq(paste(data_1st$genus, data_1st$species)) ``` Or can be used like the `dplyr` way, which is easier readable: ```{r freq 2a, eval = FALSE} data_1st %>% freq(genus, species) ``` ```{r freq 2b, results = 'asis', echo = FALSE} data_1st %>% freq(genus, species, header = TRUE) ``` ## 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: ```{r bug_drg 1a, eval = FALSE} data_1st %>% bug_drug_combinations() %>% head() # show first 6 rows ``` ```{r bug_drg 1b, echo = FALSE, results = 'asis'} knitr::kable(data_1st %>% bug_drug_combinations() %>% head(), align = "c") ``` Using [Tidyverse selections](https://tidyselect.r-lib.org/reference/language.html), you can also select columns based on the antibiotic class they are in: ```{r bug_drg 2a, eval = FALSE} data_1st %>% select(bacteria, fluoroquinolones()) %>% bug_drug_combinations() ``` ```{r bug_drg 2b, echo = FALSE, results = 'asis'} knitr::kable(data_1st %>% select(bacteria, fluoroquinolones()) %>% bug_drug_combinations(), align = "c") ``` 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: ```{r} data_1st %>% resistance(AMX) ``` Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package: ```{r, eval = FALSE} data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) ``` ```{r, echo = FALSE} data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) %>% knitr::kable(align = "c", big.mark = ",") ``` 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): ```{r, eval = FALSE} data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX), available = n_rsi(AMX)) ``` ```{r, echo = FALSE} data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX), available = n_rsi(AMX)) %>% knitr::kable(align = "c", big.mark = ",") ``` These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily: ```{r, eval = FALSE} data_1st %>% group_by(genus) %>% summarise(amoxiclav = susceptibility(AMC), gentamicin = susceptibility(GEN), amoxiclav_genta = susceptibility(AMC, GEN)) ``` ```{r, echo = FALSE} data_1st %>% group_by(genus) %>% summarise(amoxiclav = susceptibility(AMC), gentamicin = susceptibility(GEN), amoxiclav_genta = susceptibility(AMC, GEN)) %>% knitr::kable(align = "c", big.mark = ",") ``` To make a transition to the next part, let's see how this difference could be plotted: ```{r plot 1} 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](https://ggplot2.tidyverse.org/). A quick example would look like these syntaxes: ```{r plot 2, eval = FALSE} 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: ```{r plot 3} 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: ```{r plot 4} # 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 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: ```{r plot 5} data_1st %>% group_by(genus) %>% ggplot_rsi(x = "genus", facet = "antibiotic", breaks = 0:4 * 25, datalabels = FALSE) + coord_flip() ``` ## 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: ```{r, results = 'markup'} # 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 ``` We can apply the test now with: ```{r} # do Fisher's Exact Test fisher.test(check_FOS) ``` As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.