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(v2.1.1.9140) WISCA fix
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@ -3,6 +3,7 @@
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\name{antibiogram}
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\alias{antibiogram}
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\alias{wisca}
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\alias{retrieve_wisca_parameters}
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\alias{plot.antibiogram}
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\alias{autoplot.antibiogram}
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\alias{knit_print.antibiogram}
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@ -19,21 +20,23 @@
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\usage{
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antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
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ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
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only_all_tested = FALSE, digits = 0,
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only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
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formatting_type = getOption("AMR_antibiogram_formatting_type",
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ifelse(wisca, 18, 10)), col_mo = NULL, language = get_AMR_locale(),
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ifelse(wisca, 14, 10)), col_mo = NULL, language = get_AMR_locale(),
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minimum = 30, combine_SI = TRUE, sep = " + ", wisca = FALSE,
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simulations = 1000, conf_interval = 0.95, interval_side = "two-tailed",
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info = interactive())
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wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
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ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
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only_all_tested = FALSE, digits = 0,
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formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
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only_all_tested = FALSE, digits = 1,
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formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
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col_mo = NULL, language = get_AMR_locale(), minimum = 30,
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combine_SI = TRUE, sep = " + ", simulations = 1000,
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info = interactive())
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retrieve_wisca_parameters(wisca_model, ...)
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\method{plot}{antibiogram}(x, ...)
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\method{autoplot}{antibiogram}(object, ...)
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@ -44,9 +47,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
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\arguments{
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\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()}})}
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\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}.}
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\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 \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
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\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.}
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\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 or \code{NA} to consider all microorganisms 'unknown'.}
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\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.}
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@ -56,7 +59,7 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
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\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
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\item{digits}{number of digits to use for rounding the susceptibility percentage}
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\item{digits}{number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise}
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\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.}
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@ -70,9 +73,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
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\item{sep}{a separating character for antimicrobial columns in combination antibiograms}
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\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.}
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\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 decision model to estimate regimen coverage probabilities using \href{https://en.wikipedia.org/wiki/Monte_Carlo_method}{Monte Carlo simulations}. Set \code{simulations} to adjust.}
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\item{simulations}{(for WISCA) a numerical value to set the number of Montecarlo simulations}
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\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations}
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\item{conf_interval}{(for WISCA) a numerical value to set confidence interval (default is \code{0.95})}
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@ -80,6 +83,8 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
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\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
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\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \link{antibiogram(..., wisca = TRUE)}}
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\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)}
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\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
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@ -103,7 +108,7 @@ For estimating antimicrobial coverage, especially when creating a WISCA, the out
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The numeric values of an antibiogram are stored in a long format as the \link[=attributes]{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
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\subsection{Formatting Type}{
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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:
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The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} is the antimicrobial coverage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
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\enumerate{
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\item 5
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\item 15
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@ -120,18 +125,18 @@ The formatting of the 'cells' of the table can be set with the argument \code{fo
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Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \code{wisca()}):
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\item 5 (4-6)
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\item 5\% (4-6\%)
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\item 5\% (4-6\%) - \strong{default for WISCA}
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\item 5 (4-6,300)
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\item 5\% (4-6\%,300)
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\item 5 (4-6,N=300)
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\item 5\% (4-6\%,N=300) - \strong{default for WISCA}
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\item 5\% (4-6\%,N=300)
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\item 5 (4-6,15/300)
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\item 5\% (4-6\%,15/300)
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\item 5 (4-6,N=15/300)
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\item 5\% (4-6\%,N=15/300)
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}
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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)}.
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The default is \code{14} 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)}. Do note that for WISCA, the numerator and denominator are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
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Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
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}
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@ -140,7 +145,7 @@ Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibi
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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()}}.
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\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.
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\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{Explaining WISCA} on this page.
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\enumerate{
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\item \strong{Traditional Antibiogram}
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@ -172,7 +177,7 @@ Code example:
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}\if{html}{\out{</div>}}
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\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
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WISCA can be applied to any antibiogram, see the section \emph{Why Use WISCA?} on this page for more information.
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WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA} on this page for more information.
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Code example:
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@ -187,36 +192,88 @@ wisca(your_data,
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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).
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}
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}
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Grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
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\subsection{Grouped tibbles}{
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For any type of antibiogram, grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
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Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{your_data \%>\%
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
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your_data \%>\%
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group_by(has_sepsis, is_neonate, sex) \%>\%
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wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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}\if{html}{\out{</div>}}
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}
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\subsection{Inclusion in Combination Antibiogram and Syndromic Antibiogram}{
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\subsection{Stepped Approach for Clinical Insight}{
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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:
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In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
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\enumerate{
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\item \strong{Initial Empirical Therapy (Admission / Pre-Culture Data)}
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At admission, no pathogen information is available.
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\itemize{
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\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
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antibiotics = selected_regimens,
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wisca = TRUE,
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mo_transform = NA) # all pathogens set to `NA`
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}\if{html}{\out{</div>}}
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}
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\item \strong{Refinement with Gram Stain Results}
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When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
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\itemize{
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\item Action: narrow coverage based on Gram stain-specific resistance patterns.
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
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antibiotics = selected_regimens,
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wisca = TRUE,
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mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
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}\if{html}{\out{</div>}}
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}
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\item \strong{Definitive Therapy Based on Species Identification}
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After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
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\itemize{
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\item Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
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antibiotics = selected_regimens,
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wisca = TRUE,
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mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
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}\if{html}{\out{</div>}}
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}
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}
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By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
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}
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\subsection{Inclusion in Combination Antibiograms}{
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Note that for combination antibiograms, 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:
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\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
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only_all_tested = FALSE only_all_tested = TRUE
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----------------------- -----------------------
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Drug A Drug B include as include as include as include as
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numerator denominator numerator denominator
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-------- -------- ---------- ----------- ---------- -----------
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S or I S or I X X X X
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R S or I X X X X
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<NA> S or I X X - -
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S or I R X X X X
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R R - X - X
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<NA> R - - - -
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S or I <NA> X X - -
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R <NA> - - - -
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<NA> <NA> - - - -
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Drug A Drug B considered considered considered considered
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susceptible tested susceptible tested
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-------- -------- ----------- ---------- ----------- ----------
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S or I S or I X X X X
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R S or I X X X X
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<NA> S or I X X - -
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S or I R X X X X
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R R - X - X
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<NA> R - - - -
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S or I <NA> X X - -
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R <NA> - - - -
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<NA> <NA> - - - -
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--------------------------------------------------------------------
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}\if{html}{\out{</div>}}
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}
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@ -230,7 +287,7 @@ The outcome of \code{\link[=antibiogram]{antibiogram()}} can also be used direct
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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()}.
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}
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}
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\section{Why Use WISCA?}{
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\section{Explaining WISCA}{
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WISCA, as outlined by Bielicki \emph{et al.} (\doi{10.1093/jac/dkv397}), 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.
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