% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/antibiogram.R
\name{antibiogram}
\alias{antibiogram}
\alias{wisca}
\alias{get_long_numeric_format}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{knit_print.antibiogram}
\title{Generate Traditional, Combination, Syndromic, or WISCA Antibiograms}
\source{
\itemize{
\item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397}
\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
\item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
\item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
}
\usage{
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 18, 10)), col_mo = NULL, language = get_AMR_locale(),
minimum = 30, combine_SI = TRUE, sep = " + ", wisca = FALSE,
simulations = 1000, conf_interval = 0.95, interval_side = "two-tailed",
info = interactive())
wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
col_mo = NULL, language = get_AMR_locale(), minimum = 30,
combine_SI = TRUE, sep = " + ", simulations = 1000,
info = interactive())
get_long_numeric_format(antibiogram)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
\method{knit_print}{antibiogram}(x, italicise = TRUE,
na = getOption("knitr.kable.NA", default = ""), ...)
}
\arguments{
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{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", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input.}
\item{ab_transform}{a character to transform antimicrobial input - must be one of the column names of the \link{antibiotics} data set (defaults to \code{"name"}): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be \code{NULL} to not transform the input.}
\item{syndromic_group}{a column name of \code{x}, or values calculated to split rows of \code{x}, e.g. by using \code{\link[=ifelse]{ifelse()}} or \code{\link[dplyr:case_when]{case_when()}}. See \emph{Examples}.}
\item{add_total_n}{a \link{logical} to indicate whether total available numbers per pathogen should be added to the table (default is \code{TRUE}). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for \emph{E. coli} 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").}
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
\item{digits}{number of digits to use for rounding the susceptibility percentage}
\item{formatting_type}{numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
\item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
\item{language}{language to translate text, which defaults to the system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}})}
\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
\item{combine_SI}{a \link{logical} to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is \code{TRUE})}
\item{sep}{a separating character for antimicrobial columns in combination antibiograms}
\item{wisca}{a \link{logical} to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is \code{FALSE}). This will use a Bayesian hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set \code{simulations} to adjust.}
\item{simulations}{(for WISCA) a numerical value to set the number of Montecarlo simulations}
\item{conf_interval}{(for WISCA) a numerical value to set confidence interval (default is \code{0.95})}
\item{interval_side}{(for WISCA) the side of the confidence interval, either \code{"two-tailed"} (default), \code{"left"} or \code{"right"}}
\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
\item{antibiogram}{the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}}
\item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
\item{italicise}{a \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}.}
\item{na}{character to use for showing \code{NA} values}
}
\description{
Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
Adhering to previously described approaches (see \emph{Source}) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki \emph{et al.}, these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
}
\details{
This function returns a table with values between 0 and 100 for \emph{susceptibility}, not resistance.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top \emph{n} species encountered in the data. You can filter on this top \emph{n} using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}. For example, use \code{top_n_microorganisms(your_data, n = 10)} as a pre-processing step to only include the top 10 species in the data.
Using \code{\link[=get_long_numeric_format]{get_long_numeric_format()}}, the antibiogram is converted to a long format containing numeric values. This is ideal for e.g. advanced plotting.
\subsection{Formatting Type}{
The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} is the susceptibility percentage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
\enumerate{
\item 5
\item 15
\item 300
\item 15/300
\item 5 (300)
\item 5\% (300)
\item 5 (N=300)
\item 5\% (N=300)
\item 5 (15/300)
\item 5\% (15/300) - \strong{default for non-WISCA}
\item 5 (N=15/300)
\item 5\% (N=15/300)
Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \code{wisca()}):
\item 5 (4-6)
\item 5\% (4-6\%)
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
\item 5\% (4-6\%,N=300) - \strong{default for WISCA}
\item 5 (4-6,15/300)
\item 5\% (4-6\%,15/300)
\item 5 (4-6,N=15/300)
\item 5\% (4-6\%,N=15/300)
}
The default is \code{18} for WISCA and \code{10} for non-WISCA, which can be set globally with the package option \code{\link[=AMR-options]{AMR_antibiogram_formatting_type}}, e.g. \code{options(AMR_antibiogram_formatting_type = 5)}.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
\subsection{Antibiogram Types}{
There are various antibiogram types, as summarised by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}.
\strong{Use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section \emph{Why Use WISCA?} on this page.
\enumerate{
\item \strong{Traditional Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/tazobactam (TZP)
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antibiotics = "TZP")
}\if{html}{\out{
}}
\item \strong{Combination Antibiogram}
Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP + tobramycin versus TZP alone
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
\item \strong{Syndromic Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respiratory specimens (obtained among ICU patients only)
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antibiotics = penicillins(),
syndromic_group = "ward")
}\if{html}{\out{
}}
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
WISCA can be applied to any antibiogram, see the section \emph{Why Use WISCA?} on this page for more information.
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
wisca = TRUE)
# this is equal to:
wisca(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
}
Grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
Code example:
\if{html}{\out{}}\preformatted{your_data \%>\%
group_by(has_sepsis, is_neonate, sex) \%>\%
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
}
\subsection{Inclusion in Combination Antibiogram and Syndromic Antibiogram}{
Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
\if{html}{\out{}}\preformatted{--------------------------------------------------------------------
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 - - - -
- - - -
--------------------------------------------------------------------
}\if{html}{\out{
}}
}
\subsection{Plotting}{
All types of antibiograms as listed above can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R's \code{\link[=plot]{plot()}} and \code{\link[=barplot]{barplot()}}).
THe outcome of \code{\link[=antibiogram]{antibiogram()}} can also be used directly in R Markdown / Quarto (i.e., \code{knitr}) for reports. In this case, \code{\link[knitr:kable]{knitr::kable()}} will be applied automatically and microorganism names will even be printed in italics at default (see argument \code{italicise}).
You can also use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. with \code{flextable::as_flextable()} or \code{gt::gt()}.
}
}
\section{Why Use WISCA?}{
WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for
Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
This method leverages a Bayesian hierarchical logistic regression framework with random
effects for pathogens and regimens, enabling robust estimates in the presence of sparse
data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the
coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
is modeled using a Beta distribution as a prior:
\ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
\ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
The likelihood function is constructed based on observed data, where the number of covered
cases for a regimen follows a binomial distribution:
\ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
\ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
Posterior parameter estimates are obtained by combining the prior and likelihood using
Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
\ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
\ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
This hierarchical structure ensures partial pooling of estimates across groups,
improving stability in strata with small sample sizes. The model is implemented using
Hamiltonian Monte Carlo (HMC) sampling.
Stratified results are provided based on covariates such as age, sex, and clinical
complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
For example, posterior odds ratios (ORs) are derived to quantify the effect of these
covariates on coverage probabilities:
\ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
\ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
By combining empirical data with prior knowledge, WISCA overcomes the limitations
of traditional combination antibiograms, offering disease-specific, patient-stratified
estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial
stewardship programs and empirical treatment guideline refinement.
}
\examples{
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates
\donttest{
# 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"
)
# WISCA antibiogram ----------------------------------------------------
# can be used for any of the above types - just add `wisca = TRUE`
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain",
wisca = TRUE
)
# 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)
}
}