# Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker **et al.** and Barbieri **et al.** (see **Source**), and allow reporting in e.g. R Markdown and Quarto as well. ## Source * Klinker KP **et al.** (2021). Antimicrobial stewardship and antibiograms: importance of moving beyondtraditional antibiograms . **Therapeutic Advances in Infectious Disease**, May 5;8:20499361211011373; tools:::Rd_expr_doi("10.1177/20499361211011373") * Barbieri E **et al.** (2021). Development of a Weighted-Incidence Syndromic Combination Antibiogram(WISCA) to guide the choice of the empiric antibiotic treatment forurinary tract infection in paediatric patients: a Bayesian approach **Antimicrobial Resistance & Infection Control** May 1;10(1):74; tools:::Rd_expr_doi("10.1186/s13756-021-00939-2") * M39 Analysis and Presentation of Cumulative AntimicrobialSusceptibility Test Data, 5th Edition , 2022, **Clinical and Laboratory Standards Institute (CLSI)**. [https://clsi.org/standards/products/microbiology/documents/m39/](https://clsi.org/standards/products/microbiology/documents/m39/). ```r antibiogram( x, antibiotics = where(is.sir), mo_transform = "shortname", ab_transform = NULL, syndromic_group = NULL, add_total_n = TRUE, only_all_tested = FALSE, digits = 0, col_mo = NULL, language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, sep = " + ", info = interactive() ) ## S3 method for class 'antibiogram' plot(x, ...) ## S3 method for class 'antibiogram' autoplot(object, ...) ## S3 method for class 'antibiogram' knit_print( x, italicise = TRUE, na = getOption("knitr.kable.NA", default = ""), ... ) ``` ## Arguments - `x`: a data.frame containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see `as.sir()`) - `antibiotics`: vector of any antibiotic name or code (will be evaluated with `as.ab()`, column name of `x`, or (any combinations of) antibiotic selectors such as `aminoglycosides()` or `carbapenems()`. For combination antibiograms, this can also be set to values separated with `"+"`, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antibiotics exist in `x`. See **Examples**. - `mo_transform`: a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be `NULL` to not transform the input. - `ab_transform`: a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be `NULL` to not transform the input. - `syndromic_group`: a column name of `x`, or values calculated to split rows of `x`, e.g. by using `ifelse()` or `case_when()`. See **Examples**. - `add_total_n`: a logical to indicate whether total available numbers per pathogen should be added to the table (default is `TRUE`). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for **E. coli** 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). - `only_all_tested`: (for combination antibiograms): a logical to indicate that isolates must be tested for all antibiotics, see **Details** - `digits`: number of digits to use for rounding - `col_mo`: column name of the names or codes of the microorganisms (see `as.mo()`) - the default is the first column of class `mo`. Values will be coerced using `as.mo()`. - `language`: language to translate text, which defaults to the system language (see `get_AMR_locale()`) - `minimum`: the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see **Source**. - `combine_SI`: a logical to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (default is `TRUE`) - `sep`: a separating character for antibiotic columns in combination antibiograms - `info`: a logical to indicate info should be printed - the default is `TRUE` only in interactive mode - `...`: when used in R Markdown or Quarto : arguments passed on to `knitr::kable()` (otherwise, has no use) - `object`: an `antibiogram()` object - `italicise`: a logical to indicate whether the microorganism names in the knitr table should be made italic, using `italicise_taxonomy()`. - `na`: character to use for showing `NA` values ## Details This function returns a table with values between 0 and 100 for **susceptibility**, not resistance. Remember that you should filter your data to let it contain only firstisolates! This is needed to exclude duplicates and to reduce selection bias. Use `first_isolate()` to determine them in your data set with one of the four available algorithms. All types of antibiograms as listed below can be plotted (using `ggplot2::autoplot()` or base `plot()`/`barplot()`). The `antibiogram` object can also be used directly in R Markdown / Quarto (i.e., `knitr`) for reports. In this case, `knitr::kable()` will be applied automatically and microorganism names will even be printed in italics at default (see argument `italicise`). You can also use functions from specific 'table reporting' packages to transform the output of `antibiogram()` to your needs, e.g. with `flextable::as_flextable()` or `gt::gt()`. ### Antibiogram Types There are four antibiogram types, as proposed by Klinker **et al.** (2021, tools:::Rd_expr_doi("10.1177/20499361211011373") ), and they are all supported by `antibiogram()`: 1. Traditional Antibiogram Case example: Susceptibility of **Pseudomonas aeruginosa** to piperacillin/tazobactam (TZP) Code example: ``` antibiogram(your_data, antibiotics = "TZP") ``` 2. Combination Antibiogram Case example: Additional susceptibility of **Pseudomonas aeruginosa** to TZP + tobramycin versus TZP alone Code example: ``` antibiogram(your_data, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN")) ``` 3. Syndromic Antibiogram Case example: Susceptibility of **Pseudomonas aeruginosa** to TZP among respiratory specimens (obtained among ICU patients only) Code example: ``` antibiogram(your_data, antibiotics = penicillins(), syndromic_group = "ward") ``` 4. Weighted-Incidence Syndromic Combination Antibiogram (WISCA) Case example: Susceptibility of **Pseudomonas aeruginosa** to TZP among respiratory specimens (obtained among ICU patients only) for male patients age \>=65 years with heart failure Code example: ``` library(dplyr) your_data %\>% filter(ward == "ICU" & specimen_type == "Respiratory") %\>% antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), syndromic_group = ifelse(.$age \>= 65 & .$gender == "Male" & .$condition == "Heart Disease", "Study Group", "Control Group")) ``` Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the `only_all_tested` argument (default is `FALSE`). See this example for two antibiotics, Drug A and Drug B, about how `antibiogram()` works to calculate the %SI: ``` -------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- ``` ## Examples ```r # example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates # Traditional antibiogram ---------------------------------------------- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()) ) antibiogram(example_isolates, antibiotics = aminoglycosides(), ab_transform = "atc", mo_transform = "gramstain" ) antibiogram(example_isolates, antibiotics = carbapenems(), ab_transform = "name", mo_transform = "name" ) # Combined antibiogram ------------------------------------------------- # combined antibiotics yield higher empiric coverage antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain" ) # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates, antibiotics = c("Cipro", "cipro + genta"), mo_transform = "gramstain", ab_transform = "name", sep = " & " ) # Syndromic antibiogram ------------------------------------------------ # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = "ward" ) # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ] # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1, antibiotics = aminoglycosides(), ab_transform = "name", syndromic_group = ifelse(ex1$ward == "ICU", "UCI", "No UCI" ), language = "es" ) # Weighted-incidence syndromic combination antibiogram (WISCA) --------- # the data set could contain a filter for e.g. respiratory specimens/ICU antibiogram(example_isolates, antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"), mo_transform = "gramstain", minimum = 10, # this should be >=30, but now just as example syndromic_group = ifelse(example_isolates$age >= 65 & example_isolates$gender == "M", "WISCA Group 1", "WISCA Group 2" ) ) # Print the output for R Markdown / Quarto ----------------------------- ureido <- antibiogram(example_isolates, antibiotics = ureidopenicillins(), ab_transform = "name" ) # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace("knitr")) { cat(knitr::knit_print(ureido)) } # Generate plots with ggplot2 or base R -------------------------------- ab1 <- antibiogram(example_isolates, antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), mo_transform = "gramstain" ) ab2 <- antibiogram(example_isolates, antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"), mo_transform = "gramstain", syndromic_group = "ward" ) if (requireNamespace("ggplot2")) { ggplot2::autoplot(ab1) } if (requireNamespace("ggplot2")) { ggplot2::autoplot(ab2) } plot(ab1) plot(ab2) ```

antibiogram function