diff --git a/DESCRIPTION b/DESCRIPTION index cf12e989..8fcb3350 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: AMR -Version: 1.8.2.9142 -Date: 2023-02-23 +Version: 1.8.2.9143 +Date: 2023-02-24 Title: Antimicrobial Resistance Data Analysis Description: Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by diff --git a/NEWS.md b/NEWS.md index 2633fd16..1a772e11 100755 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# AMR 1.8.2.9142 +# AMR 1.8.2.9143 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)* diff --git a/R/aa_helper_pm_functions.R b/R/aa_helper_pm_functions.R index 2ad3ea12..800976ab 100755 --- a/R/aa_helper_pm_functions.R +++ b/R/aa_helper_pm_functions.R @@ -988,7 +988,7 @@ pm_summarise.default <- function(.data, ...) { if (is.list(x_res)) I(x_res) else x_res } ) - res <- as.data.frame(res) + res <- as.data.frame(res, stringsAsFactors = FALSE) fn_names <- names(fns) colnames(res) <- if (is.null(fn_names)) fns else fn_names if (pm_groups_exist) res <- cbind(group, res, row.names = NULL) diff --git a/R/antibiogram.R b/R/antibiogram.R index 82e4c699..b6c3ef84 100755 --- a/R/antibiogram.R +++ b/R/antibiogram.R @@ -49,7 +49,11 @@ #' @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 first isolates!** 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 \R [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 [`as_flextable()`][flextable::as_flextable()] or [`gt()`][gt::gt()]. #' +#' ### Antibiogram Types +#' #' There are four antibiogram types, as proposed by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()]: #' #' 1. **Traditional Antibiogram** @@ -103,8 +107,6 @@ #' "Study Group", "Control Group")) #' ``` #' -#' All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using [ggplot2::autoplot()] or base \R [plot()]/[barplot()]) or directly used into R Markdown / Quarto formats for reports (in the last case, [knitr::kable()] will be applied automatically). Use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. `flextable::as_flextable()` or `gt::gt()`. -#' #' 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: #' #' ``` @@ -125,6 +127,7 @@ #' - - - - #' -------------------------------------------------------------------- #' ``` +#' #' @source #' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \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 for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2} @@ -208,6 +211,7 @@ #' "WISCA Group 1", "WISCA Group 2" #' ) #' ) +#' #' #' # Print the output for R Markdown / Quarto ----------------------------- #' @@ -504,6 +508,7 @@ antibiogram <- function(x, out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram") rownames(out) <- NULL structure(out, + has_syndromic_group = has_syndromic_group, long = long, combine_SI = combine_SI ) @@ -578,39 +583,25 @@ autoplot.antibiogram <- function(object, ...) { } # will be exported in zzz.R -#' @param italicise a [logical] to indicate whether the microorganism names in the [knitr][knitr::kable()] table should be made italic, using [italicise_taxonomy()]. This only works when the output format is markdown, such as in HTML output. +#' @method knit_print antibiogram +#' @param italicise a [logical] to indicate whether the microorganism names in the [knitr][knitr::kable()] table should be made italic, using [italicise_taxonomy()]. #' @param na character to use for showing `NA` values #' @rdname antibiogram knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.kable.NA", default = ""), ...) { stop_ifnot_installed("knitr") meet_criteria(italicise, allow_class = "logical", has_length = 1) meet_criteria(na, allow_class = "character", has_length = 1, allow_NA = TRUE) - + + if (isTRUE(italicise)) { + # make all microorganism names italic, according to nomenclature + names_col <- ifelse(isTRUE(attributes(x)$has_syndromic_group), 2, 1) + x[[names_col]] <- italicise_taxonomy(x[[names_col]], type = "markdown") + } + old_option <- getOption("knitr.kable.NA") options(knitr.kable.NA = na) on.exit(options(knitr.kable.NA = old_option)) - out <- knitr::kable(x, ..., output = FALSE) - - format <- attributes(out)$format - if (isTRUE(italicise) && - !is.null(format) && - format %in% c("markdown", "pipe")) { - # try to italicise the output - rows_with_txt <- which(out %like% "[a-z]") - rows_without_txt <- setdiff(seq_len(length(out)), rows_with_txt) - out[rows_with_txt] <- gsub("^[|]", "| ", out[rows_with_txt]) - # put hyphen directly after second character - out[rows_without_txt] <- gsub("^[|](.)", "|\\1-", out[rows_without_txt]) - out_ita <- italicise_taxonomy(as.character(out), type = "markdown") - if (length(unique(nchar(out_ita))) != 1) { - # so there has been alterations done by italicise_taxonomy() - to_fill <- which(nchar(out_ita) < max(nchar(out_ita))) - out_ita[intersect(to_fill, rows_with_txt)] <- gsub("(^[|].*?)([|])(.*)", "\\1 \\2\\3", out_ita[intersect(to_fill, rows_with_txt)], perl = TRUE) - out_ita[intersect(to_fill, rows_without_txt)] <- gsub("(^[|].*?)([|])(.*)", "\\1--\\2\\3", out_ita[intersect(to_fill, rows_without_txt)], perl = TRUE) - } - attributes(out_ita) <- attributes(out) - out <- out_ita - } - res <- paste(c("", "", out), collapse = "\n") - knitr::asis_output(res) + + out <- paste(c("", "", knitr::kable(x, ..., output = FALSE)), collapse = "\n") + knitr::asis_output(out) } diff --git a/R/bug_drug_combinations.R b/R/bug_drug_combinations.R index 099b2c23..5e9ef885 100755 --- a/R/bug_drug_combinations.R +++ b/R/bug_drug_combinations.R @@ -39,7 +39,7 @@ #' @param ... arguments passed on to `FUN` #' @inheritParams sir_df #' @inheritParams base::formatC -#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S. +#' @details The function [format()] calculates the resistance per bug-drug combination and returns a table ready for reporting/publishing. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S. This table can also directly be used in R Markdown / Quarto without the need for e.g. [knitr::kable()]. #' @export #' @rdname bug_drug_combinations #' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total". @@ -327,7 +327,15 @@ format.bug_drug_combinations <- function(x, } rownames(y) <- NULL - as_original_data_class(y, class(x.bak)) # will remove tibble groups + as_original_data_class(y, class(x.bak), extra_class = "formatted_bug_drug_combinations") # will remove tibble groups +} + +# will be exported in zzz.R +knit_print.formatted_bug_drug_combinations <- function(x, ...) { + stop_ifnot_installed("knitr") + # make columns with MO names italic according to nomenclature + colnames(x)[3:NCOL(x)] <- italicise_taxonomy(colnames(x)[3:NCOL(x)], type = "markdown") + knitr::asis_output(paste("", "", knitr::kable(x, ...), collapse = "\n")) } #' @method print bug_drug_combinations diff --git a/R/sysdata.rda b/R/sysdata.rda index 3033c228..11333965 100755 Binary files a/R/sysdata.rda and b/R/sysdata.rda differ diff --git a/R/zzz.R b/R/zzz.R index ced87f1f..e8ebeb7a 100755 --- a/R/zzz.R +++ b/R/zzz.R @@ -128,8 +128,9 @@ if (utf8_supported && !is_latex) { s3_register("ggplot2::fortify", "sir") s3_register("ggplot2::fortify", "mic") s3_register("ggplot2::fortify", "disk") - # Support for knitr / R Markdown + # Support for knitr (R Markdown/Quarto) s3_register("knitr::knit_print", "antibiogram") + s3_register("knitr::knit_print", "formatted_bug_drug_combinations") # Support vctrs package for use in e.g. dplyr verbs # S3: ab_selector s3_register("vctrs::vec_ptype2", "character.ab_selector") diff --git a/data-raw/antibiograms.Rmd b/data-raw/antibiograms.Rmd index 8be57b1c..d515f152 100644 --- a/data-raw/antibiograms.Rmd +++ b/data-raw/antibiograms.Rmd @@ -2,7 +2,7 @@ title: "Generating antibiograms with the AMR package" author: "AMR package developers" date: "`r Sys.Date()`" -output: html_document +output: pdf_document --- ```{r setup, include=FALSE} diff --git a/data-raw/antibiograms.html b/data-raw/antibiograms.html index b53df843..989da0ac 100644 --- a/data-raw/antibiograms.html +++ b/data-raw/antibiograms.html @@ -11,7 +11,7 @@ - + Generating antibiograms with the AMR package @@ -299,23 +299,17 @@ overflow-y: auto; border: 1px solid #ddd; border-radius: 4px; } -.tabset-dropdown > .nav-tabs > li.active:before { -content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { +content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; border-right: 1px solid #ddd; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { -content: ""; -border: none; -} -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { -content: ""; +content: "\e258"; font-family: 'Glyphicons Halflings'; -display: inline-block; -padding: 10px; -border-right: 1px solid #ddd; +border: none; } .tabset-dropdown > .nav-tabs > li.active { display: block; @@ -359,7 +353,7 @@ display: none;

Generating antibiograms with the AMR package

AMR package developers

-

2023-02-23

+

2023-02-24

@@ -370,26 +364,25 @@ package looks like:

example_isolates
## # A tibble: 2,000 × 46
-##    date       patient   age gender ward   mo           PEN  
-##    <date>     <chr>   <dbl> <chr>  <chr>  <mo>         <sir>
-##  1 2002-01-02 A77334     65 F      Clini… B_ESCHR_COLI R    
-##  2 2002-01-03 A77334     65 F      Clini… B_ESCHR_COLI R    
-##  3 2002-01-07 067927     45 F      ICU    B_STPHY_EPDR R    
-##  4 2002-01-07 067927     45 F      ICU    B_STPHY_EPDR R    
-##  5 2002-01-13 067927     45 F      ICU    B_STPHY_EPDR R    
-##  6 2002-01-13 067927     45 F      ICU    B_STPHY_EPDR R    
-##  7 2002-01-14 462729     78 M      Clini… B_STPHY_AURS R    
-##  8 2002-01-14 462729     78 M      Clini… B_STPHY_AURS R    
-##  9 2002-01-16 067927     45 F      ICU    B_STPHY_EPDR R    
-## 10 2002-01-17 858515     79 F      ICU    B_STPHY_EPDR R    
-## # … with 1,990 more rows, and 39 more variables: OXA <sir>,
-## #   FLC <sir>, AMX <sir>, AMC <sir>, AMP <sir>, TZP <sir>,
-## #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>,
-## #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>,
-## #   KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>,
-## #   LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
-## #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, …
-## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
+## date patient age gender ward mo PEN OXA FLC AMX +## <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +## 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +## 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +## 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +## 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +## 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +## 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +## 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +## 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +## 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +## 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +## # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, +## # TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, +## # CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, +## # TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, +## # MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, +## # ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, +## # CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>

Traditional Antibiogram

antibiogram(example_isolates,
@@ -397,7 +390,7 @@ looks like:

 
-
+
@@ -408,7 +401,7 @@ looks like:

-
+
@@ -507,7 +500,7 @@ looks like:

 
 
Pathogeen (N min-max)Pathogen (N min-max)
 AMK
 GEN
 IPM
 

 
 
CNS (43-309)CoNS (43-309)
 0
 86
 52
 

-
+
@@ -515,7 +508,7 @@ looks like:

-
+
@@ -577,10 +570,20 @@ looks like:

              antibiotics = c(aminoglycosides(), carbapenems()),
              syndromic_group = "ward")

 
 
Pathogeen (N min-max)Pathogen (N min-max)
 TZP
 TZP + GEN
 TZP + TOB
 

 
 
CNS (29-274)CoNS (29-274)
 30
 97
 
 

++++++++++
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+
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+
@@ -742,9 +745,9 @@ looks like:

             syndromic_group = ifelse(example_isolates$age >= 65 &
                                        example_isolates$gender == "M",
                                      "WISCA Group 1", "WISCA Group 2"))
-










 
 
SyndroomgroepPathogeen (N min-max)Syndromic GroupPathogen (N min-max)
 AMK
 GEN
 IPM
 

 

 ClinicalCNS (23-205)CoNS (23-205)
 
 89
 57
 

 

 ICUCNS (10-73)CoNS (10-73)
 
 79
 
 

 

 OutpatientCNS (3-31)CoNS (3-31)
 
 84
 
 

 

 ClinicalE. coli (0-299)E. coli (0-299)
 100
 98
 100
 

 

 ICUE. coli (0-137)E. coli (0-137)
 100
 99
 100
 

 

 ClinicalK. pneumoniae (0-51)K. pneumoniae (0-51)
 
 92
 100
 

 

 ClinicalP. mirabilis (0-30)P. mirabilis (0-30)
 
 100
 
 

 

 ClinicalS. aureus (2-150)S. aureus (2-150)
 
 99
 
 

 

 ICUS. aureus (0-66)S. aureus (0-66)
 
 100
 
 

 

 ClinicalS. epidermidis (4-79)S. epidermidis (4-79)
 
 82
 
 

 

 ICUS. epidermidis (4-75)S. epidermidis (4-75)
 
 72
 
 

 

 ClinicalS. hominis (1-45)S. hominis (1-45)
 
 96
 
 

 

 ClinicalS. pneumoniae (5-78)S. pneumoniae (5-78)
 0
 0
 
 

 

 ICUS. pneumoniae (5-30)S. pneumoniae (5-30)
 0
 0
 

+
-+@@ -753,8 +756,8 @@ looks like:

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+
diff --git a/data-raw/antibiograms.pdf b/data-raw/antibiograms.pdf
new file mode 100644
index 00000000..cce88d05
Binary files /dev/null and b/data-raw/antibiograms.pdf differ
diff --git a/data-raw/reproduction_of_poorman.R b/data-raw/reproduction_of_poorman.R
index 11237603..96660fad 100644
--- a/data-raw/reproduction_of_poorman.R
+++ b/data-raw/reproduction_of_poorman.R
@@ -107,3 +107,4 @@ contents <- c(
 writeLines(contents, "R/aa_helper_pm_functions.R")
 
 # note: pm_left_join() will be overwritten by aaa_helper_functions.R, which contains a faster implementation
+# replace `res <- as.data.frame(res)` with  `res <- as.data.frame(res, stringsAsFactors = FALSE)`
diff --git a/man/antibiogram.Rd b/man/antibiogram.Rd
index cb7798d0..55ca6e45 100644
--- a/man/antibiogram.Rd
+++ b/man/antibiogram.Rd
@@ -35,7 +35,7 @@ antibiogram(
 
 \method{autoplot}{antibiogram}(object, ...)
 
-knit_print.antibiogram(
+\method{knit_print}{antibiogram}(
   x,
   italicise = TRUE,
   na = getOption("knitr.kable.NA", default = ""),
@@ -75,7 +75,7 @@ knit_print.antibiogram(
 
 \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()}}. This only works when the output format is markdown, such as in HTML output.}
+\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}
 }
@@ -87,6 +87,9 @@ This function returns a table with values between 0 and 100 for \emph{susceptibi
 
 \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.
 
+All types of antibiograms as listed below can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R \code{\link[=plot]{plot()}}/\code{\link[=barplot]{barplot()}}). The \code{antibiogram} object 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{\link[flextable:as_flextable]{as_flextable()}} or \code{\link[gt:gt]{gt()}}.
+\subsection{Antibiogram Types}{
+
 There are four antibiogram types, as proposed by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}:
 \enumerate{
 \item \strong{Traditional Antibiogram}
@@ -134,8 +137,6 @@ your_data \%>\%
 }\if{html}{\out{}}
 }
 
-All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R \code{\link[=plot]{plot()}}/\code{\link[=barplot]{barplot()}}) or directly used into R Markdown / Quarto formats for reports (in the last case, \code{\link[knitr:kable]{knitr::kable()}} will be applied automatically). Use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. \code{flextable::as_flextable()} or \code{gt::gt()}.
-
 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 antibiotics, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
 
 \if{html}{\out{
}}\preformatted{--------------------------------------------------------------------
@@ -156,6 +157,7 @@ Note that for combination antibiograms, it is important to realise that suscepti
 --------------------------------------------------------------------
 }\if{html}{\out{
}}
 }
+}
 \examples{
 # example_isolates is a data set available in the AMR package.
 # run ?example_isolates for more info.
@@ -233,6 +235,7 @@ antibiogram(example_isolates,
             )
 )
 
+
 # Print the output for R Markdown / Quarto -----------------------------
 
 ureido <- antibiogram(example_isolates,
diff --git a/man/bug_drug_combinations.Rd b/man/bug_drug_combinations.Rd
index be2e829f..4c12583a 100644
--- a/man/bug_drug_combinations.Rd
+++ b/man/bug_drug_combinations.Rd
@@ -55,7 +55,7 @@ The function \code{\link[=bug_drug_combinations]{bug_drug_combinations()}} retur
 Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{\link[=format]{format()}} on the result to prettify it to a publishable/printable format, see \emph{Examples}.
 }
 \details{
-The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination. Use \code{combine_SI = TRUE} (default) to test R vs. S+I and \code{combine_SI = FALSE} to test R+I vs. S.
+The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination and returns a table ready for reporting/publishing. Use \code{combine_SI = TRUE} (default) to test R vs. S+I and \code{combine_SI = FALSE} to test R+I vs. S. This table can also directly be used in R Markdown / Quarto without the need for e.g. \code{\link[knitr:kable]{knitr::kable()}}.
 }
 \examples{
 # example_isolates is a data set available in the AMR package.


 


 
 
 

 
 
 
SyndroomgroepPathogeen (N min-max)Syndromic GroupPathogen (N min-max)
 AMC
 AMC + CIP
 TZP
 

 

 WISCA Group 1Gram-negatief (261-285)Gram-negative (261-285)
 76
 95
 89
 

 

 WISCA Group 2Gram-negatief (380-442)Gram-negative (380-442)
 76
 98
 88
 

 

 WISCA Group 1Gram-positief (123-406)Gram-positive (123-406)
 76
 89
 81
 

 

 WISCA Group 2Gram-positief (222-732)Gram-positive (222-732)
 76
 89
 88