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(v2.1.1.9147) scale fixes and WISCA update, fix conserved capped values

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dr. M.S. (Matthijs) Berends 2025-02-14 14:16:46 +01:00
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 2.1.1.9146
Date: 2025-02-13
Version: 2.1.1.9147
Date: 2025-02-14
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

2
NAMESPACE
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@ -322,10 +322,12 @@ export(retrieve_wisca_parameters)
export(rifamycins)
export(right_join_microorganisms)
export(scale_color_mic)
export(scale_color_sir)
export(scale_colour_mic)
export(scale_colour_sir)
export(scale_fill_mic)
export(scale_fill_sir)
export(scale_sir_colors)
export(scale_sir_colours)
export(scale_x_mic)
export(scale_x_sir)

3
NEWS.md
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@ -1,4 +1,4 @@
# AMR 2.1.1.9146
# AMR 2.1.1.9147
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
@ -45,6 +45,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
* It is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users.
* Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
* To get quantitative values, `as.double()` on a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
* Fix for `conserve_capped_values`, which now again works as expected: in MIC values, `<x` will always be S, `>x` will always be R
* `antibiogram()` function
* New argument `formatting_type` to set any of the 22 options for the formatting of all 'cells'. This defaults to `10` for non-WISCA and `14` for WISCA, changing the output of antibiograms to cells with more info.
* For this reason, `add_total_n` is now `FALSE` at default since the denominators are added to the cells

2
PythonPackage/AMR/AMR.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.2
Name: AMR
Version: 2.1.1.9146
Version: 2.1.1.9147
Summary: A Python wrapper for the AMR R package
Home-page: https://github.com/msberends/AMR
Author: Matthijs Berends

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PythonPackage/AMR/setup.py
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@ -2,7 +2,7 @@ from setuptools import setup, find_packages
setup(
name='AMR',
version='2.1.1.9146',
version='2.1.1.9147',
packages=find_packages(),
install_requires=[
'rpy2',

420
R/antibiogram.R
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@ -38,7 +38,7 @@
#' @param mo_transform a character to transform microorganism input - must be `"name"`, `"shortname"` (default), `"gramstain"`, or one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input or `NA` to consider all microorganisms 'unknown'.
#' @param ab_transform a character to transform antimicrobial input - must be one of the column names of the [antibiotics] data set (defaults to `"name"`): `r vector_or(colnames(antibiotics), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input.
#' @param syndromic_group a column name of `x`, or values calculated to split rows of `x`, e.g. by using [ifelse()] or [`case_when()`][dplyr::case_when()]. See *Examples*.
#' @param 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 isolates per antimicrobial (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").
#' @param add_total_n a [logical] to indicate whether `n_tested` available numbers per pathogen should be added to the table (default is `TRUE`). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when `wisca = TRUE`; in that case, use [retrieve_wisca_parameters()] to get the parameters used for WISCA.
#' @param only_all_tested (for combination antibiograms): a [logical] to indicate that isolates must be tested for all antimicrobials, see *Details*
#' @param digits number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise
#' @param 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 *Details* > *Formatting Type* for a list of options.
@ -47,7 +47,7 @@
#' @param 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*.
#' @param combine_SI a [logical] to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is `TRUE`)
#' @param sep a separating character for antimicrobial columns in combination antibiograms
#' @param wisca a [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations` to adjust.
#' @param wisca a [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations`, `conf_interval`, and `interval_side` to adjust.
#' @param simulations (for WISCA) a numerical value to set the number of Monte Carlo simulations
#' @param conf_interval (for WISCA) a numerical value to set confidence interval (default is `0.95`)
#' @param interval_side (for WISCA) the side of the confidence interval, either `"two-tailed"` (default), `"left"` or `"right"`
@ -64,7 +64,7 @@
#'
#' ### Formatting Type
#'
#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the antimicrobial coverage (for WISCA: `4-6` indicates the confidence level), `15` the numerator, and `300` the denominator:
#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the antimicrobial coverage (`4-6` indicates the confidence level), `15` the number of susceptible isolates, and `300` the number of tested (i.e., available) isolates:
#'
#' 1. 5
#' 2. 15
@ -75,13 +75,11 @@
#' 7. 5 (N=300)
#' 8. 5% (N=300)
#' 9. 5 (15/300)
#' 10. 5% (15/300) - **default for non-WISCA**
#' 10. 5% (15/300)
#' 11. 5 (N=15/300)
#' 12. 5% (N=15/300)
#'
#' Additional options for WISCA (using `antibiogram(..., wisca = TRUE)` or `wisca()`):
#' 13. 5 (4-6)
#' 14. 5% (4-6%) - **default for WISCA**
#' 14. 5% (4-6%) - **default**
#' 15. 5 (4-6,300)
#' 16. 5% (4-6%,300)
#' 17. 5 (4-6,N=300)
@ -91,7 +89,7 @@
#' 21. 5 (4-6,N=15/300)
#' 22. 5% (4-6%,N=15/300)
#'
#' The default is `14` for WISCA and `10` for non-WISCA, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `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.
#' The default is `14`, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `options(AMR_antibiogram_formatting_type = 5)`. Do note that for WISCA, the total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
#'
#' Set `digits` (defaults to `0`) to alter the rounding of the susceptibility percentages.
#'
@ -99,7 +97,7 @@
#'
#' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()].
#'
#' **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 *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page.
#' For clinical coverage estimations, **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 *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
#'
#' 1. **Traditional Antibiogram**
#'
@ -174,14 +172,17 @@
#'
#' At admission, no pathogen information is available.
#'
#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = NA) # all pathogens set to `NA`
#'
#' # preferred: use WISCA
#' wisca(your_data,
#' antibiotics = selected_regimens)
#' ```
#'
#' 2. **Refinement with Gram Stain Results**
@ -194,7 +195,6 @@
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
#' ```
#'
@ -208,7 +208,6 @@
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
#' ```
#'
@ -247,7 +246,7 @@
#'
#' @section Explaining WISCA:
#'
#' WISCA, as outlined by Bielicki *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.
#' WISCA, as outlined by Bielicki *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 decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
#'
#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
#'
@ -282,6 +281,7 @@
#'
#' 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.
#'
#' **Note:** WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
#' @source
#' * Bielicki JA *et al.* (2016). **Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data** *Journal of Antimicrobial Chemotherapy* 71(3); \doi{10.1093/jac/dkv397}
#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
@ -307,14 +307,12 @@
#' antibiogram(example_isolates,
#' antibiotics = aminoglycosides(),
#' ab_transform = "atc",
#' mo_transform = "gramstain"
#' )
#' mo_transform = "gramstain")
#'
#' antibiogram(example_isolates,
#' antibiotics = carbapenems(),
#' ab_transform = "name",
#' mo_transform = "name"
#' )
#' mo_transform = "name")
#'
#'
#' # Combined antibiogram -------------------------------------------------
@ -322,16 +320,14 @@
#' # combined antibiotics yield higher empiric coverage
#' antibiogram(example_isolates,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' mo_transform = "gramstain"
#' )
#' 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 = " & "
#' )
#' sep = " & ")
#'
#'
#' # Syndromic antibiogram ------------------------------------------------
@ -339,8 +335,7 @@
#' # the data set could contain a filter for e.g. respiratory specimens
#' antibiogram(example_isolates,
#' antibiotics = c(aminoglycosides(), carbapenems()),
#' syndromic_group = "ward"
#' )
#' syndromic_group = "ward")
#'
#' # now define a data set with only E. coli
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -353,27 +348,24 @@
#' syndromic_group = ifelse(ex1$ward == "ICU",
#' "UCI", "No UCI"
#' ),
#' language = "es"
#' )
#' language = "es")
#'
#'
#' # WISCA antibiogram ----------------------------------------------------
#'
#' # can be used for any of the above types - just add `wisca = TRUE`
#' # WISCA are not stratified by species, but rather on syndromes
#' antibiogram(example_isolates,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' mo_transform = "gramstain",
#' wisca = TRUE
#' )
#' syndromic_group = "ward",
#' wisca = TRUE)
#'
#'
#' # Print the output for R Markdown / Quarto -----------------------------
#'
#' ureido <- antibiogram(example_isolates,
#' antibiotics = ureidopenicillins(),
#' ab_transform = "name",
#' wisca = TRUE
#' )
#' syndromic_group = "name",
#' wisca = TRUE)
#'
#' # in an Rmd file, you would just need to return `ureido` in a chunk,
#' # but to be explicit here:
@ -386,14 +378,11 @@
#'
#' ab1 <- antibiogram(example_isolates,
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' wisca = TRUE
#' )
#' mo_transform = "gramstain")
#' ab2 <- antibiogram(example_isolates,
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' syndromic_group = "ward"
#' )
#' syndromic_group = "ward")
#'
#' if (requireNamespace("ggplot2")) {
#' ggplot2::autoplot(ab1)
@ -413,7 +402,7 @@ antibiogram <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -437,7 +426,7 @@ antibiogram.default <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -450,6 +439,13 @@ antibiogram.default <- function(x,
info = interactive()) {
meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(wisca, allow_class = "logical", has_length = 1)
if (isTRUE(wisca)) {
if (!missing(mo_transform)) {
warning_("WISCA must be based on the species level as WISCA parameters are based on this. For that reason, `mo_transform` will be ignored.")
}
mo_transform <- function(x) suppressMessages(suppressWarnings(paste(mo_genus(x, keep_synonyms = TRUE, language = NULL), mo_species(x, keep_synonyms = TRUE, language = NULL))))
}
if (!is.function(mo_transform)) {
meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE, allow_NA = TRUE)
}
@ -460,8 +456,7 @@ antibiogram.default <- function(x,
meet_criteria(add_total_n, allow_class = "logical", has_length = 1)
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
meet_criteria(digits, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(wisca, allow_class = "logical", has_length = 1)
meet_criteria(formatting_type, allow_class = c("numeric", "integer"), has_length = 1, is_in = if (wisca == TRUE) c(1:22) else c(1:12))
meet_criteria(formatting_type, allow_class = c("numeric", "integer"), has_length = 1, is_in = c(1:22))
meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
@ -587,62 +582,156 @@ antibiogram.default <- function(x,
FUN = function(x) x,
include_n_rows = TRUE
)
colnames(out)[colnames(out) == "total"] <- "n_tested"
colnames(out)[colnames(out) == "total_rows"] <- "n_total"
counts <- out
wisca_params <- NULL
if (isTRUE(combine_SI)) {
out$n_susceptible <- out$S + out$I + out$SDD
} else {
out$n_susceptible <- out$S
}
if (all(out$n_tested < minimum, na.rm = TRUE) && wisca == FALSE) {
warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
} else if (any(out$n_tested < minimum, na.rm = TRUE)) {
out <- out %pm>%
# also for WISCA, refrain from anything below 15 isolates:
subset(n_tested > 15)
mins <- sum(out$n_tested < minimum, na.rm = TRUE)
if (wisca == FALSE) {
out <- out %pm>%
subset(n_tested >= minimum)
if (isTRUE(info) && mins > 0) {
message_("NOTE: ", mins, " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
} else if (isTRUE(info)) {
warning_("Number of tested isolates per regimen should exceed ", minimum, " for each species. Coverage estimates might be inaccurate.", call = FALSE)
}
}
if (NROW(out) == 0) {
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
}
out$p_susceptible <- out$n_susceptible / out$n_tested
# add confidence levels
out$lower_ci <- NA_real_
out$upper_ci <- NA_real_
for (r in seq_len(NROW(out))) {
if (!is.na(out$n_susceptible[r]) && !is.na(out$n_tested[r]) && out$n_tested[r] > 0) {
ci <- stats::binom.test(out$n_susceptible[r], out$n_tested[r], conf.level = conf_interval)$conf.int
out$lower_ci[r] <- ci[1]
out$upper_ci[r] <- ci[2]
}
}
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
out <- out %pm>%
pm_group_by(mo, ab)
}
long_numeric <- out %pm>%
pm_summarise(coverage = p_susceptible,
lower_ci = lower_ci,
upper_ci = upper_ci,
n_total = n_total,
n_tested = n_tested,
n_susceptible = n_susceptible)
wisca_parameters <- data.frame()
if (wisca == TRUE) {
# WISCA ----
# set up progress bar
progress <- progress_ticker(n = NROW(out[which(out$total > 0), , drop = FALSE]),
n_min = 10,
print = info,
title = "Calculating beta/gamma parameters for WISCA")
on.exit(close(progress))
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out_wisca <- out %pm>%
pm_group_by(syndromic_group, ab)
} else {
out_wisca <- out %pm>%
pm_group_by(ab)
}
out_wisca <- out_wisca %pm>%
pm_summarise(coverage = NA_real_,
lower_ci = NA_real_,
upper_ci = NA_real_,
n_total = sum(n_total, na.rm = TRUE),
n_tested = sum(n_tested, na.rm = TRUE),
n_susceptible = sum(n_susceptible, na.rm = TRUE))
out_wisca$p_susceptible <- out_wisca$n_susceptible / out_wisca$n_tested
out$coverage <- NA_real_
out$lower_ci <- NA_real_
out$upper_ci <- NA_real_
if (isTRUE(has_syndromic_group)) {
out$group <- paste(out$syndromic_group, out$ab)
out_wisca$group <- paste(out_wisca$syndromic_group, out_wisca$ab)
} else {
out$group <- out$ab
out_wisca$group <- out_wisca$ab
}
# create the WISCA parameters, including our priors/posteriors
out$gamma_posterior <- NA_real_
out$beta_posterior_1 <- NA_real_
out$beta_posterior_2 <- NA_real_
out$beta_posterior1 <- NA_real_
out$beta_posterior2 <- NA_real_
for (i in seq_len(NROW(out))) {
if (out$total[i] == 0) {
if (out$n_tested[i] == 0) {
next
}
progress$tick()
out_current <- out[i, , drop = FALSE]
priors <- calculate_priors(out_current, combine_SI = combine_SI)
out$gamma_posterior[i] = priors$gamma_posterior
out$beta_posterior_1[i] = priors$beta_posterior_1
out$beta_posterior_2[i] = priors$beta_posterior_2
out$beta_posterior1[i] = priors$beta_posterior_1
out$beta_posterior2[i] = priors$beta_posterior_2
}
wisca_parameters <- out
progress <- progress_ticker(n = length(unique(wisca_parameters$group)) * simulations,
n_min = 25,
print = info,
title = paste("Calculating WISCA for", length(unique(wisca_parameters$group)), "regimens"))
on.exit(close(progress))
# run WISCA
for (group in unique(wisca_parameters$group)) {
params_current <- wisca_parameters[which(wisca_parameters$group == group), , drop = FALSE]
if (sum(params_current$n_tested, na.rm = TRUE) == 0) {
next
}
# Monte Carlo simulation
coverage_simulations <- replicate(simulations, {
progress$tick()
# simulate pathogen incidence
# = Dirichlet (Gamma) parameters
random_incidence <- stats::runif(1, min = 0, max = 1)
simulated_incidence <- stats::qgamma(
p = random_incidence,
shape = priors$gamma_posterior,
shape = params_current$gamma_posterior,
scale = 1
)
# normalise
simulated_incidence <- simulated_incidence / sum(simulated_incidence)
simulated_incidence <- simulated_incidence / sum(simulated_incidence, na.rm = TRUE)
# simulate susceptibility
# = Beta parameters
random_susceptibity <- stats::runif(1, min = 0, max = 1)
simulated_susceptibility <- stats::qbeta(
p = random_susceptibity,
shape1 = priors$beta_posterior_1,
shape2 = priors$beta_posterior_2
shape1 = params_current$beta_posterior1,
shape2 = params_current$beta_posterior2
)
sum(simulated_incidence * simulated_susceptibility)
sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
})
# calculate coverage statistics
@ -656,72 +745,22 @@ antibiogram.default <- function(x,
}
coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
out$coverage[i] <- coverage_mean
out$lower_ci[i] <- coverage_ci[1]
out$upper_ci[i] <- coverage_ci[2]
out_wisca$coverage[which(out_wisca$group == group)] <- coverage_mean
out_wisca$lower_ci[which(out_wisca$group == group)] <- coverage_ci[1]
out_wisca$upper_ci[which(out_wisca$group == group)] <- coverage_ci[2]
}
# remove progress bar from console
close(progress)
}
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I + out$SDD
} else {
out$numerator <- out$S
}
if (all(out$total < minimum, na.rm = TRUE) && wisca == FALSE) {
warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
} else if (any(out$total < minimum, na.rm = TRUE)) {
out <- out %pm>%
# also for WISCA, refrain from anything below 15 isolates:
subset(total > 15)
mins <- sum(out$total < minimum, na.rm = TRUE)
if (wisca == FALSE) {
out <- out %pm>%
subset(total >= minimum)
if (isTRUE(info) && mins > 0) {
message_("NOTE: ", mins, " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
} else if (isTRUE(info)) {
warning_("Number of tested isolates per regimen should exceed ", minimum, ". Coverage estimates will be inaccurate for ", mins, " regimen", ifelse(mins == 1, "", "s"), ".", call = FALSE)
}
}
if (NROW(out) == 0) {
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
}
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
out <- out %pm>%
pm_group_by(mo, ab)
}
if (wisca == TRUE) {
long_numeric <- out %pm>%
pm_summarise(coverage = coverage,
lower_ci = lower_ci,
upper_ci = upper_ci,
n_tested = total,
n_total = total_rows,
n_susceptible = numerator,
p_susceptible = numerator / total,
gamma_posterior = gamma_posterior,
beta_posterior1 = beta_posterior_1,
beta_posterior2 = beta_posterior_2)
} else {
long_numeric <- out %pm>%
pm_summarise(coverage = numerator / total,
numerator = numerator,
total = total)
# prepare for definitive output
out <- out_wisca
wisca_parameters <- wisca_parameters[, colnames(wisca_parameters)[!colnames(wisca_parameters) %in% c(levels(NA_sir_), "lower_ci", "upper_ci", "group")], drop = FALSE]
}
out$digits <- digits # since pm_sumarise() cannot work with an object outside the current frame
if (isFALSE(wisca)) {
out$coverage <- out$p_susceptible
}
# formatting type:
# 1. 5
@ -746,24 +785,28 @@ antibiogram.default <- function(x,
# 20. 5% (4-6%,15/300)
# 21. 5 (4-6,N=15/300)
# 22. 5% (4-6%,N=15/300)
if (formatting_type == 1) out <- out %pm>% pm_summarise(out_value = round((numerator / total) * 100, digits = digits))
if (formatting_type == 2) out <- out %pm>% pm_summarise(out_value = numerator)
if (formatting_type == 3) out <- out %pm>% pm_summarise(out_value = total)
if (formatting_type == 4) out <- out %pm>% pm_summarise(out_value = paste0(numerator, "/", total))
if (formatting_type == 5) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (", total, ")"))
if (formatting_type == 6) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (", total, ")"))
if (formatting_type == 7) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (N=", total, ")"))
if (formatting_type == 8) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (N=", total, ")"))
if (formatting_type == 9) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (", numerator, "/", total, ")"))
if (formatting_type == 10) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (", numerator, "/", total, ")"))
if (formatting_type == 11) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (N=", numerator, "/", total, ")"))
if (formatting_type == 12) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (N=", numerator, "/", total, ")"))
if (formatting_type == 1) out <- out %pm>% pm_summarise(out_value = round(coverage * 100, digits = digits))
if (formatting_type == 2) out <- out %pm>% pm_summarise(out_value = n_susceptible)
if (formatting_type == 3) out <- out %pm>% pm_summarise(out_value = n_tested)
if (formatting_type == 4) out <- out %pm>% pm_summarise(out_value = paste0(n_susceptible, "/", n_tested))
if (formatting_type == 5) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", n_tested, ")"))
if (formatting_type == 6) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", n_tested, ")"))
if (formatting_type == 7) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (N=", n_tested, ")"))
if (formatting_type == 8) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (N=", n_tested, ")"))
if (formatting_type == 9) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 10) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 11) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 12) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ")"))
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%)"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", total, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", total, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", total, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", total, ")"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", n_tested, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", n_tested, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", n_tested, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", n_tested, ")"))
if (formatting_type == 19) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 20) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 21) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 22) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", n_susceptible, "/", n_tested, ")"))
# transform names of antibiotics
ab_naming_function <- function(x, t, l, s) {
@ -792,11 +835,18 @@ antibiogram.default <- function(x,
# transform long to wide
long_to_wide <- function(object) {
if (wisca == TRUE) {
# column `mo` has already been removed, but we create here a surrogate to make the stats::reshape() work since it needs an identifier
object$mo <- 1 #seq_len(NROW(object))
}
object <- object %pm>%
# an unclassed data.frame is required for stats::reshape()
as.data.frame(stringsAsFactors = FALSE) %pm>%
stats::reshape(direction = "wide", idvar = "mo", timevar = "ab", v.names = "out_value")
colnames(object) <- gsub("^out_value?[.]", "", colnames(object))
if (wisca == TRUE) {
object <- object[, colnames(object)[colnames(object) != "mo"], drop = FALSE]
}
return(object)
}
@ -818,33 +868,46 @@ antibiogram.default <- function(x,
)
}
}
# sort rows
new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
if (wisca == TRUE) {
# sort rows
new_df <- new_df %pm>% pm_arrange(syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", sort(colnames(new_df)[colnames(new_df) != "syndromic_group"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Syndromic Group", language = language)
} else {
# sort rows
new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
}
} else {
new_df <- long_to_wide(out)
# sort rows
new_df <- new_df %pm>% pm_arrange(mo)
# sort columns
new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
if (wisca == TRUE) {
# sort columns
new_df <- new_df[, c(sort(colnames(new_df))), drop = FALSE]
} else {
# sort rows
new_df <- new_df %pm>% pm_arrange(mo)
# sort columns
new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
}
}
# add total N if indicated
if (isTRUE(add_total_n)) {
# add n_tested N if indicated
if (isTRUE(add_total_n) && isFALSE(wisca)) {
if (isTRUE(has_syndromic_group)) {
n_per_mo <- counts %pm>%
pm_group_by(mo, .syndromic_group) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
pm_summarise(paste0(min(n_tested, na.rm = TRUE), "-", max(n_tested, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "syn", "count")
count_group <- n_per_mo$count[match(paste(new_df[[2]], new_df[[1]]), paste(n_per_mo$mo, n_per_mo$syn))]
edit_col <- 2
} else {
n_per_mo <- counts %pm>%
pm_group_by(mo) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
pm_summarise(paste0(min(n_tested, na.rm = TRUE), "-", max(n_tested, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "count")
count_group <- n_per_mo$count[match(new_df[[1]], n_per_mo$mo)]
edit_col <- 1
@ -862,6 +925,7 @@ antibiogram.default <- function(x,
out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
rownames(out) <- NULL
rownames(wisca_parameters) <- NULL
rownames(long_numeric) <- NULL
structure(out,
@ -869,7 +933,9 @@ antibiogram.default <- function(x,
combine_SI = combine_SI,
wisca = wisca,
conf_interval = conf_interval,
long_numeric = as_original_data_class(long_numeric, class(out))
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x))
)
}
@ -883,7 +949,7 @@ antibiogram.grouped_df <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -929,6 +995,7 @@ antibiogram.grouped_df <- function(x,
conf_interval = conf_interval,
interval_side = interval_side,
info = i == 1 && info == TRUE)
new_wisca_parameters <- attributes(new_out)$wisca_parameters
new_long_numeric <- attributes(new_out)$long_numeric
if (i == 1) progress$tick()
@ -938,8 +1005,10 @@ antibiogram.grouped_df <- function(x,
}
# remove first column 'Pathogen' (in whatever language)
new_out <- new_out[, -1, drop = FALSE]
new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
if (isFALSE(wisca)) {
new_out <- new_out[, -1, drop = FALSE]
new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
}
# add group names to data set
for (col in rev(seq_len(NCOL(groups) - 1))) {
@ -947,6 +1016,12 @@ antibiogram.grouped_df <- function(x,
col_value <- groups[i, col, drop = TRUE]
new_out[, col_name] <- col_value
new_out <- new_out[, c(col_name, setdiff(names(new_out), col_name))] # set place to 1st col
if (isTRUE(wisca)) {
new_wisca_parameters[, col_name] <- col_value
new_wisca_parameters <- new_wisca_parameters[, c(col_name, setdiff(names(new_wisca_parameters), col_name))] # set place to 1st col
}
new_long_numeric[, col_name] <- col_value
new_long_numeric <- new_long_numeric[, c(col_name, setdiff(names(new_long_numeric), col_name))] # set place to 1st col
}
@ -954,9 +1029,11 @@ antibiogram.grouped_df <- function(x,
if (i == 1) {
# the first go
out <- new_out
wisca_parameters <- new_wisca_parameters
long_numeric <- new_long_numeric
} else {
out <- rbind_AMR(out, new_out)
wisca_parameters <- rbind_AMR(wisca_parameters, new_wisca_parameters)
long_numeric <- rbind_AMR(long_numeric, new_long_numeric)
}
}
@ -968,6 +1045,8 @@ antibiogram.grouped_df <- function(x,
combine_SI = isTRUE(combine_SI),
wisca = isTRUE(wisca),
conf_interval = conf_interval,
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x)))
}
@ -975,7 +1054,6 @@ antibiogram.grouped_df <- function(x,
#' @rdname antibiogram
wisca <- function(x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
@ -988,10 +1066,11 @@ wisca <- function(x,
combine_SI = TRUE,
sep = " + ",
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive()) {
antibiogram(x = x,
antibiotics = antibiotics,
mo_transform = mo_transform,
ab_transform = ab_transform,
syndromic_group = syndromic_group,
add_total_n = add_total_n,
@ -1005,6 +1084,8 @@ wisca <- function(x,
sep = sep,
wisca = TRUE,
simulations = simulations,
conf_interval = conf_interval,
interval_side = interval_side,
info = info)
}
@ -1013,25 +1094,18 @@ wisca <- function(x,
#' @rdname antibiogram
retrieve_wisca_parameters <- function(wisca_model, ...) {
stop_ifnot(isTRUE(attributes(wisca_model)$wisca), "This function only applies to WISCA models. Use `wisca()` or `antibiogram(..., wisca = TRUE)` to create a WISCA model.")
attributes(wisca_model)$long_numeric
attributes(wisca_model)$wisca_parameters
}
calculate_priors <- function(data, combine_SI = TRUE) {
if (combine_SI == TRUE && "I" %in% colnames(data)) {
data$S <- data$S + data$I
}
if (combine_SI == TRUE && "SDD" %in% colnames(data)) {
data$S <- data$S + data$SDD
}
# Pathogen incidence (Dirichlet distribution)
gamma_prior <- rep(1, length(unique(data$mo))) # Dirichlet prior
gamma_posterior <- gamma_prior + data$total_rows # Posterior parameters
gamma_posterior <- gamma_prior + data$n_total # Posterior parameters
# Regimen susceptibility (Beta distribution)
beta_prior <- rep(1, length(unique(data$mo))) # Beta prior
r <- data$S # Number of pathogens tested susceptible
n <- data$total # Total tested
r <- data$n_susceptible # Number of pathogens tested susceptible
n <- data$n_tested # n_tested tested
beta_posterior_1 <- beta_prior + r # Posterior alpha
beta_posterior_2 <- beta_prior + (n - r) # Posterior beta
@ -1048,8 +1122,10 @@ tbl_sum.antibiogram <- function(x, ...) {
dims <- paste(format(NROW(x), big.mark = ","), AMR_env$cross_icon, format(NCOL(x), big.mark = ","))
if (isTRUE(attributes(x)$wisca)) {
names(dims) <- paste0("An Antibiogram (WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
} else if (isTRUE(attributes(x)$formatting_type >= 13)) {
names(dims) <- paste0("An Antibiogram (non-WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
} else {
names(dims) <- "An Antibiogram (non-WISCA)"
names(dims) <- paste0("An Antibiogram (non-WISCA)")
}
dims
}

2
R/ggplot_sir.R
View File

@ -244,7 +244,7 @@ ggplot_sir <- function(data,
theme_sir()
if (fill == "interpretation") {
p <- p + scale_sir_colours(aesthetics = "fill", colours = colours)
p <- suppressWarnings(p + scale_sir_colours(aesthetics = "fill", colours = colours))
}
if (identical(position, "fill")) {

74
R/plotting.R
View File

@ -50,11 +50,11 @@
#' @details
#' ### The `scale_*_mic()` Functions
#'
#' The functions [scale_x_mic()], [scale_y_mic()], [scale_colour_mic()], and [scale_fill_mic()] functions allow to plot the [mic][as.mic()] class (MIC values) on a continuous scale. They allow to rescale the MIC range, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
#' The functions [scale_x_mic()], [scale_y_mic()], [scale_colour_mic()], and [scale_fill_mic()] functions allow to plot the [mic][as.mic()] class (MIC values) on a continuous, logarithmic scale. They also allow to rescale the MIC range with an 'inside' or 'outside' range if required, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
#'
#' ### The `scale_*_sir()` Functions
#'
#' The functions [scale_x_sir()], [scale_colour_sir()], and [scale_fill_sir()] functions allow to plot the [sir][as.sir()] class (S/I/R values). They can translate the S/I/R values to any of the `r length(AMR:::LANGUAGES_SUPPORTED)` supported languages, and set colour-blind friendly colours to the `colour` and `fill` aesthetics.
#' The functions [scale_x_sir()], [scale_colour_sir()], and [scale_fill_sir()] functions allow to plot the [sir][as.sir()] class in the right order (`r paste(levels(NA_sir_), collapse = " < ")`). At default, they translate the S/I/R values to an interpretative text ("Susceptible", "Resistant", etc.) in any of the `r length(AMR:::LANGUAGES_SUPPORTED)` supported languages (use `language = NULL` to keep S/I/R). Also, except for [scale_x_sir()], they set colour-blind friendly colours to the `colour` and `fill` aesthetics.
#'
#' ### Additional `ggplot2` Functions
#'
@ -68,7 +68,7 @@
#'
#' The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
#'
#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r vector_and(AMR::clinical_breakpoints$guideline, quotes = TRUE, reverse = TRUE)`. Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
#' For interpreting MIC values as well as disk diffusion diameters, the default guideline is `r AMR::clinical_breakpoints$guideline[1]`, unless the package option [`AMR_guideline`][AMR-options] is set. See [as.sir()] for more information.
#' @name plot
#' @rdname plot
#' @return The `autoplot()` functions return a [`ggplot`][ggplot2::ggplot()] model that is extendible with any `ggplot2` function.
@ -231,7 +231,8 @@ create_scale_mic <- function(aest, keep_operators, mic_range, ...) {
ggplot_fn <- getExportedValue(paste0("scale_", aest, "_continuous"),
ns = asNamespace("ggplot2"))
args <- list(...)
args[c("trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
# do not take these arguments into account, as they will be overwritten and seem to allow weird behaviour
args[c("aesthetics", "trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
scale <- do.call(ggplot_fn, args)
scale$transform <- function(x) {
@ -294,8 +295,8 @@ scale_colour_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
}
#' @export
#' @inheritParams as.mic
#' @rdname plot
#' @usage NULL
scale_color_mic <- scale_colour_mic
#' @export
@ -307,32 +308,27 @@ scale_fill_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
create_scale_mic("fill", keep_operators = keep_operators, mic_range = mic_range, ...)
}
create_scale_sir <- function(aest, colours_SIR, language, eucast_I, ...) {
ggplot_fn <- getExportedValue(paste0("scale_", aest, "_discrete"),
ns = asNamespace("ggplot2"))
create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
args <- list(...)
args[c("aesthetics", "value", "labels", "limits")] <- NULL
args[c("value", "labels", "limits")] <- NULL
if (aest == "x") {
if (identical(aesthetics, "x")) {
ggplot_fn <- ggplot2::scale_x_discrete
args <- c(args,
list(limits = base::force))
} else {
ggplot_fn <- ggplot2::scale_discrete_manual
args <- c(args,
list(aesthetics = aest,
list(aesthetics = aesthetics,
values = c(S = colours_SIR[1],
SDD = colours_SIR[2],
I = colours_SIR[2],
R = colours_SIR[3],
NI = "grey30"),
limits = base::force))
NI = "grey30")))
}
scale <- do.call(ggplot_fn, args)
scale$labels <- function(x) {
stop_ifnot(all(x %in% levels(NA_sir_)),
"Apply `scale_", aest, "_sir()` to a variable of class 'sir', see `?as.sir`.",
stop_ifnot(all(x %in% c(levels(NA_sir_), NA)),
"Apply `scale_", aesthetics[1], "_sir()` to a variable of class 'sir', see `?as.sir`.",
call = FALSE)
x <- as.character(as.sir(x))
if (!is.null(language)) {
@ -351,7 +347,7 @@ create_scale_sir <- function(aest, colours_SIR, language, eucast_I, ...) {
}
scale$limits <- function(x, ...) {
# force SIR in the right order
x[match(x, levels(NA_sir_))]
as.character(sort(factor(x, levels = levels(NA_sir_))))
}
scale
@ -366,7 +362,7 @@ scale_x_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("x", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
create_scale_sir(aesthetics = "x", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I)
}
#' @rdname plot
@ -378,9 +374,21 @@ scale_colour_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("colour", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
args <- list(...)
args$colours_SIR <- colours_SIR
args$language <- language
args$eucast_I <- eucast_I
if (is.null(args$aesthetics)) {
args$aesthetics <- "colour"
}
do.call(create_scale_sir, args)
}
#' @export
#' @rdname plot
#' @usage NULL
scale_color_sir <- scale_colour_sir
#' @rdname plot
#' @export
scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
@ -390,7 +398,14 @@ scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("fill", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
args <- list(...)
args$colours_SIR <- colours_SIR
args$language <- language
args$eucast_I <- eucast_I
if (is.null(args$aesthetics)) {
args$aesthetics <- "fill"
}
do.call(create_scale_sir, args)
}
#' @method plot mic
@ -400,7 +415,7 @@ scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
plot.mic <- function(x,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(x)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -489,7 +504,7 @@ plot.mic <- function(x,
barplot.mic <- function(height,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(height)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -530,7 +545,7 @@ barplot.mic <- function(height,
autoplot.mic <- function(object,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -640,7 +655,7 @@ plot.disk <- function(x,
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -727,7 +742,7 @@ barplot.disk <- function(height,
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -766,7 +781,7 @@ autoplot.disk <- function(object,
title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -1268,6 +1283,11 @@ scale_sir_colours <- function(...,
ggplot2::scale_discrete_manual(aesthetics = aesthetics, values = cols, limits = force)
}
#' @export
#' @rdname plot
#' @usage NULL
scale_sir_colors <- scale_sir_colours
#' @rdname plot
#' @export
theme_sir <- function() {

14
R/sir.R
View File

@ -43,7 +43,7 @@
#' @param ab a vector (or column name) with [character]s that can be coerced to a valid antimicrobial drug code with [as.ab()]
#' @param uti (Urinary Tract Infection) a vector (or column name) with [logical]s (`TRUE` or `FALSE`) to specify whether a UTI specific interpretation from the guideline should be chosen. For using [as.sir()] on a [data.frame], this can also be a column containing [logical]s or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See *Examples*.
#' @inheritParams first_isolate
#' @param guideline defaults to EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` (the latest implemented EUCAST guideline in the [AMR::clinical_breakpoints] data set), but can be set with the package option [`AMR_guideline`][AMR-options]. Currently supports EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`), see *Details*.
#' @param guideline defaults to `r AMR::clinical_breakpoints$guideline[1]` (the latest implemented EUCAST guideline in the [AMR::clinical_breakpoints] data set), but can be set with the package option [`AMR_guideline`][AMR-options]. Currently supports EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`), see *Details*.
#' @param conserve_capped_values a [logical] to indicate that MIC values starting with `">"` (but not `">="`) must always return "R" , and that MIC values starting with `"<"` (but not `"<="`) must always return "S"
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a [logical] to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.3)`.
#' @param include_screening a [logical] to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the package option [`AMR_include_screening`][AMR-options].
@ -1361,7 +1361,7 @@ as_sir_method <- function(method_short,
mo = vectorise_log_entry(mo_current, length(rows)),
host = vectorise_log_entry(host_current, length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)),
input = vectorise_log_entry(as.double(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(NA_sir_, length(rows)),
notes = vectorise_log_entry("NO BREAKPOINT AVAILABLE", length(rows)),
guideline = vectorise_log_entry(guideline_coerced, length(rows)),
@ -1430,10 +1430,18 @@ as_sir_method <- function(method_short,
if (any(breakpoints_current$site %like% "screen", na.rm = TRUE) | any(breakpoints_current$ref_tbl %like% "screen", na.rm = TRUE)) {
notes_current <- c(notes_current, "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this")
}
if (method == "mic" && conserve_capped_values == TRUE && any(as.character(values) %like% "^[<][0-9]")) {
notes_current <- c(notes_current, "MIC values 'lower than' are all considered 'S' since conserve_capped_values = TRUE")
}
if (method == "mic" && conserve_capped_values == TRUE && any(as.character(values) %like% "^[>][0-9]")) {
notes_current <- c(notes_current, "MIC values 'greater than' are all considered 'R' since conserve_capped_values = TRUE")
}
if (method == "mic") {
new_sir <- case_when_AMR(
is.na(values) ~ NA_sir_,
conserve_capped_values == TRUE & as.character(values) %like% "^[<][0-9]" ~ as.sir("S"),
conserve_capped_values == TRUE & as.character(values) %like% "^[>][0-9]" ~ as.sir("R"),
values <= breakpoints_current$breakpoint_S ~ as.sir("S"),
guideline_coerced %like% "EUCAST" & values > breakpoints_current$breakpoint_R ~ as.sir("R"),
guideline_coerced %like% "CLSI" & values >= breakpoints_current$breakpoint_R ~ as.sir("R"),
@ -1471,7 +1479,7 @@ as_sir_method <- function(method_short,
mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)),
host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)),
input = vectorise_log_entry(as.double(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(as.sir(new_sir), length(rows)),
notes = vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)),
guideline = vectorise_log_entry(guideline_coerced, length(rows)),

BIN
R/sysdata.rda
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2
R/zzz.R
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@ -65,7 +65,7 @@ AMR_env$sir_interpretation_history <- data.frame(
mo = set_clean_class(character(0), c("mo", "character")),
host = character(0),
method = character(0),
input = double(0),
input = character(0),
outcome = NA_sir_[0],
notes = character(0),
guideline = character(0),

110
data-raw/gpt_training_text_v2.1.1.9146.txt → data-raw/gpt_training_text_v2.1.1.9147.txt
View File

@ -1,6 +1,6 @@
This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
First and foremost, you are trained on version 2.1.1.9146. Remember this whenever someone asks which AMR package version you’re at.
First and foremost, you are trained on version 2.1.1.9147. Remember this whenever someone asks which AMR package version you’re at.
Below are the contents of the file, the file, and all the files (documentation) in the package. Every file content is split using 100 hypens.
----------------------------------------------------------------------------------------------------
@ -333,10 +333,12 @@ export(retrieve_wisca_parameters)
export(rifamycins)
export(right_join_microorganisms)
export(scale_color_mic)
export(scale_color_sir)
export(scale_colour_mic)
export(scale_colour_sir)
export(scale_fill_mic)
export(scale_fill_sir)
export(scale_sir_colors)
export(scale_sir_colours)
export(scale_x_mic)
export(scale_x_sir)
@ -1650,18 +1652,19 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 14, 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",
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
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 = 1,
wisca(x, antibiotics = where(is.sir), ab_transform = "name",
syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE,
digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL, language = get_AMR_locale(), minimum = 30,
combine_SI = TRUE, sep = " + ", simulations = 1000,
conf_interval = 0.95, interval_side = "two-tailed",
info = interactive())
retrieve_wisca_parameters(wisca_model, ...)
@ -1684,7 +1687,7 @@ retrieve_wisca_parameters(wisca_model, ...)
\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{add_total_n}{a \link{logical} to indicate whether \code{n_tested} 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"). This option is unavailable when \code{wisca = TRUE}; in that case, use \code{\link[=retrieve_wisca_parameters]{retrieve_wisca_parameters()}} to get the parameters used for WISCA.}
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
@ -1702,7 +1705,7 @@ retrieve_wisca_parameters(wisca_model, ...)
\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 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.}
\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}, \code{conf_interval}, and \code{interval_side} to adjust.}
\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations}
@ -1737,7 +1740,7 @@ For estimating antimicrobial coverage, especially when creating a WISCA, the out
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.
\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 antimicrobial coverage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
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 (\code{4-6} indicates the confidence level), \code{15} the number of susceptible isolates, and \code{300} the number of tested (i.e., available) isolates:
\enumerate{
\item 5
\item 15
@ -1748,13 +1751,11 @@ The formatting of the 'cells' of the table can be set with the argument \code{fo
\item 5 (N=300)
\item 5\% (N=300)
\item 5 (15/300)
\item 5\% (15/300) - \strong{default for non-WISCA}
\item 5\% (15/300)
\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\%) - \strong{default for WISCA}
\item 5\% (4-6\%) - \strong{default}
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
@ -1765,7 +1766,7 @@ Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \co
\item 5\% (4-6\%,N=15/300)
}
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.
The default is \code{14}, 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 total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
@ -1774,7 +1775,7 @@ Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibi
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{Explaining WISCA} on this page.
For clinical coverage estimations, \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. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
\enumerate{
\item \strong{Traditional Antibiogram}
@ -1844,13 +1845,16 @@ In clinical practice, antimicrobial coverage decisions evolve as more microbiolo
At admission, no pathogen information is available.
\itemize{
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.
\item Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = NA) # all pathogens set to `NA`
# preferred: use WISCA
wisca(your_data,
antibiotics = selected_regimens)
}\if{html}{\out{</div>}}
}
\item \strong{Refinement with Gram Stain Results}
@ -1862,7 +1866,6 @@ When a blood culture becomes positive, the Gram stain provides an initial and cr
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
}\if{html}{\out{</div>}}
}
@ -1875,7 +1878,6 @@ After cultivation of the pathogen, full pathogen identification allows precise t
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
}\if{html}{\out{</div>}}
}
@ -1919,7 +1921,7 @@ You can also use functions from specific 'table reporting' packages to transform
\section{Explaining WISCA}{
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.
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 decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
@ -1953,6 +1955,8 @@ Stratified results can be provided based on covariates such as age, sex, and cli
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{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.
\strong{Note:} WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
}
\examples{
@ -1970,14 +1974,12 @@ antibiogram(example_isolates,
antibiogram(example_isolates,
antibiotics = aminoglycosides(),
ab_transform = "atc",
mo_transform = "gramstain"
)
mo_transform = "gramstain")
antibiogram(example_isolates,
antibiotics = carbapenems(),
ab_transform = "name",
mo_transform = "name"
)
mo_transform = "name")
# Combined antibiogram -------------------------------------------------
@ -1985,16 +1987,14 @@ antibiogram(example_isolates,
# combined antibiotics yield higher empiric coverage
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain"
)
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 = " & "
)
sep = " & ")
# Syndromic antibiogram ------------------------------------------------
@ -2002,8 +2002,7 @@ antibiogram(example_isolates,
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
)
syndromic_group = "ward")
# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -2016,27 +2015,24 @@ antibiogram(ex1,
syndromic_group = ifelse(ex1$ward == "ICU",
"UCI", "No UCI"
),
language = "es"
)
language = "es")
# WISCA antibiogram ----------------------------------------------------
# can be used for any of the above types - just add `wisca = TRUE`
# WISCA are not stratified by species, but rather on syndromes
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain",
wisca = TRUE
)
syndromic_group = "ward",
wisca = TRUE)
# Print the output for R Markdown / Quarto -----------------------------
ureido <- antibiogram(example_isolates,
antibiotics = ureidopenicillins(),
ab_transform = "name",
wisca = TRUE
)
syndromic_group = "name",
wisca = TRUE)
# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
@ -2049,14 +2045,11 @@ if (requireNamespace("knitr")) {
ab1 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
wisca = TRUE
)
mo_transform = "gramstain")
ab2 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
syndromic_group = "ward"
)
syndromic_group = "ward")
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab1)
@ -7339,6 +7332,7 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/plot.Rd':
\alias{scale_fill_mic}
\alias{scale_x_sir}
\alias{scale_colour_sir}
\alias{scale_color_sir}
\alias{scale_fill_sir}
\alias{plot.mic}
\alias{autoplot.mic}
@ -7349,6 +7343,7 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/plot.Rd':
\alias{facet_sir}
\alias{scale_y_percent}
\alias{scale_sir_colours}
\alias{scale_sir_colors}
\alias{theme_sir}
\alias{labels_sir_count}
\title{Plotting Helpers for AMR Data Analysis}
@ -7359,8 +7354,6 @@ scale_y_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_colour_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_color_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_x_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
@ -7375,7 +7368,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), eucast_I = getOption("AMR_guideline",
"EUCAST") == "EUCAST", ...)
\method{plot}{mic}(x, mo = NULL, ab = NULL, guideline = "EUCAST",
\method{plot}{mic}(x, mo = NULL, ab = NULL,
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(x)), ylab = translate_AMR("Frequency", language
= language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
@ -7385,8 +7379,9 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
\method{autoplot}{mic}(object, mo = NULL, ab = NULL,
guideline = "EUCAST", title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
guideline = getOption("AMR_guideline", "EUCAST"),
title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
language), colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
@ -7396,7 +7391,7 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
\method{plot}{disk}(x, main = deparse(substitute(x)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL, ab = NULL, guideline = "EUCAST",
mo = NULL, ab = NULL, guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
@ -7405,8 +7400,9 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
\method{autoplot}{disk}(object, mo = NULL, ab = NULL,
title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
language = language), xlab = translate_AMR("Disk diffusion diameter (mm)",
language = language), guideline = "EUCAST", colours_SIR = c("#3CAEA3",
"#F6D55C", "#ED553B"), language = get_AMR_locale(), expand = TRUE,
language = language), guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
@ -7499,12 +7495,12 @@ Especially the \verb{scale_*_mic()} functions are relevant wrappers to plot MIC
\details{
\subsection{The \verb{scale_*_mic()} Functions}{
The functions \code{\link[=scale_x_mic]{scale_x_mic()}}, \code{\link[=scale_y_mic]{scale_y_mic()}}, \code{\link[=scale_colour_mic]{scale_colour_mic()}}, and \code{\link[=scale_fill_mic]{scale_fill_mic()}} functions allow to plot the \link[=as.mic]{mic} class (MIC values) on a continuous scale. They allow to rescale the MIC range, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
The functions \code{\link[=scale_x_mic]{scale_x_mic()}}, \code{\link[=scale_y_mic]{scale_y_mic()}}, \code{\link[=scale_colour_mic]{scale_colour_mic()}}, and \code{\link[=scale_fill_mic]{scale_fill_mic()}} functions allow to plot the \link[=as.mic]{mic} class (MIC values) on a continuous, logarithmic scale. They also allow to rescale the MIC range with an 'inside' or 'outside' range if required, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
}
\subsection{The \verb{scale_*_sir()} Functions}{
The functions \code{\link[=scale_x_sir]{scale_x_sir()}}, \code{\link[=scale_colour_sir]{scale_colour_sir()}}, and \code{\link[=scale_fill_sir]{scale_fill_sir()}} functions allow to plot the \link[=as.sir]{sir} class (S/I/R values). They can translate the S/I/R values to any of the 20 supported languages, and set colour-blind friendly colours to the \code{colour} and \code{fill} aesthetics.
The functions \code{\link[=scale_x_sir]{scale_x_sir()}}, \code{\link[=scale_colour_sir]{scale_colour_sir()}}, and \code{\link[=scale_fill_sir]{scale_fill_sir()}} functions allow to plot the \link[=as.sir]{sir} class in the right order (S < SDD < I < R < NI). At default, they translate the S/I/R values to an interpretative text ("Susceptible", "Resistant", etc.) in any of the 20 supported languages (use \code{language = NULL} to keep S/I/R). Also, except for \code{\link[=scale_x_sir]{scale_x_sir()}}, they set colour-blind friendly colours to the \code{colour} and \code{fill} aesthetics.
}
\subsection{Additional \code{ggplot2} Functions}{
@ -7520,7 +7516,7 @@ This package contains more functions that extend the \code{ggplot2} package, to
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2024", "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2024", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011". Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option \code{\link[=AMR-options]{AMR_guideline}} is set. See \code{\link[=as.sir]{as.sir()}} for more information.
}
}
\examples{

18
data-raw/translations.tsv
View File

@ -6,8 +6,8 @@ Coagulase-positive Staphylococcus TRUE TRUE FALSE TRUE 凝固酶阳性葡萄球
Beta-haemolytic Streptococcus TRUE TRUE FALSE TRUE β-溶血性链球菌 Beta-hemolytický streptokok Beta-haemolytiske streptokokker Beta-hemolytische Streptococcus Beeta-hemolyyttinen streptokokki Streptococcus Bêta-hémolytique Beta-hämolytischer Streptococcus Β-αιμολυτικός στρεπτόκοκκος Streptococcus Beta-emolitico ベータ溶血性レンサ球菌 Beta-hemolytiske streptokokker Streptococcus beta-hemolityczny Streptococcus Beta-hemolítico Streptococ beta-hemolitic Бета-гемолитический стрептококк Streptococcus Beta-hemolítico Beta-hemolytiska streptokocker Beta-hemolitik Streptokok Бета-гемолітичний стрептокок
unknown Gram-negatives TRUE TRUE FALSE TRUE 不明革兰氏阴性菌 neznámé gramnegativní ukendte Gram-negative onbekende Gram-negatieven tuntemattomat gramnegatiiviset Gram négatifs inconnus unbekannte Gramnegativen άγνωστοι αρνητικοί κατά Gram Gram negativi sconosciuti 不明なグラム陰性菌 ukjent Gram-negative Nieznane bakterie Gram-ujemne Gram negativos desconhecidos Gram-negative necunoscute неизвестные грамотрицательные Gram negativos desconocidos okända gramnegativa bakterier bilinmeyen Gram-negatifler невідомі грамнегативні
unknown Gram-positives TRUE TRUE FALSE TRUE 不明革兰氏阳性菌 neznámé grampozitivní ukendte Gram-positive onbekende Gram-positieven tuntemattomat grampositiiviset Gram positifs inconnus unbekannte Grampositiven άγνωστοι θετικοί κατά Gram Gram positivi sconosciuti 未知のグラム陽性菌 ukjent Gram-positive Nieznane bakterie Gram-dodatnie Gram positivos desconhecidos Gram-pozitive necunoscute неизвестные грамположительные Gram positivos desconocidos okända Gram-positiva bilinmeyen Gram-pozitifler невідомі грампозитивні
unknown anaerobic Gram-negatives TRUE TRUE FALSE TRUE ukendte anaerobe Gram-negative onbekende anaerobe Gram-negatieven unbekannte anaerobe Gramnegativen
unknown anaerobic Gram-positives TRUE TRUE FALSE TRUE ukendte anaerobe Gram-positive onbekende anaerobe Gram-positieven unbekannte anaerobe Grampositiven
unknown anaerobic Gram-negatives TRUE FALSE FALSE FALSE 未知的厌氧革兰氏阴性菌 Neznámé anaerobní Gram-negativní bakterie Ukendte anaerobe Gram-negative Onbekende anaerobe Gram-negatieven Tuntemattomat anaerobiset gramnegatiivit Anaérobies à Gram négatif inconnues Unbekannte anaerobe Gram-negative Άγνωστοι αναερόβιοι Gram-αρνητικοί Sconosciuti anaerobi Gram-negativi 未知の嫌気性グラム陰性菌 Ukjente anaerobe Gram-negative Nieznane beztlenowe Gram-ujemne Anaeróbios Gram-negativos desconhecidos Necunoscuți anaerobi Gram-negativi Некоторые анаэробные Грам-отрицательные Desconocidos anaerobios Gram-negativos Okända anaeroba gramnegativa Bilinmeyen anaerobik Gram-negatif Невідомі анаеробні Грам-негативні
unknown anaerobic Gram-positives TRUE FALSE FALSE FALSE 未知的厌氧革兰氏阳性菌 Neznámé anaerobní Gram-pozitivní bakterie Ukendte anaerobe Gram-positive Onbekende anaerobe Gram-positieven Tuntemattomat anaerobiset grampositiiviset Anaérobies à Gram positif inconnues Unbekannte anaerobe Gram-positive Άγνωστοι αναερόβιοι Gram-θετικοί Sconosciuti anaerobi Gram-positivi 未知の嫌気性グラム陽性菌 Ukjente anaerobe Gram-positive Nieznane beztlenowe Gram-dodatnie Anaeróbios Gram-positivos desconhecidos Necunoscuți anaerobi Gram-pozitivi Некоторые анаэробные Грам-положительные Desconocidos anaerobios Gram-positivos Okända anaeroba grampositiva Bilinmeyen anaerobik Gram-pozitif Невідомі анаеробні Грам-позитивні
unknown protozoan TRUE TRUE FALSE TRUE 未知原生动物 neznámý prvok ukendt protozo onbekend protozoön tuntematon alkueläin protozoaire inconnu unbekanntes Protozoon άγνωστο πρωτόζωο protozoo sconosciuto 未知の原生動物 ukjent protozo nieznany pierwotniak protozoário desconhecido protozoar necunoscut неизвестное простейшее protozoo desconocido okänd protozo bilinmeyen protozoa невідоме найпростіше
unknown fungus TRUE TRUE FALSE TRUE 未知真菌 neznámé houby ukendt svamp onbekende schimmel tuntematon sieni champignon inconnu unbekannter Pilze άγνωστος μύκητας fungo sconosciuto 未知真菌 ukjent sopp Nieznany grzyb fungo desconhecido ciuperci necunoscute неизвестный грибок hongo desconocido Okänd svamp bilinmeyen mantar невідомий гриб
unknown yeast TRUE TRUE FALSE TRUE 未知酵母菌 neznámé kvasinky ukendt gær onbekende gist tuntematon hiiva levure inconnue unbekannte Hefe άγνωστος ζυμομύκητας lievito sconosciuto 未知酵母 ukjent gjær Nieznany drożdżak levedura desconhecida drojdie necunoscută неизвестные дрожжи levadura desconocida Okänd jäst bilinmeyen maya невідомі дріжджі
@ -39,13 +39,13 @@ vegetative TRUE TRUE FALSE FALSE 无性系 vegetativní vegetativ vegetatief kas
([([ ]*?)Group TRUE TRUE FALSE FALSE ([([]*?)组 \\1Skupina \\1Gruppe \\1Groep \\1Ryhmä \\1Groupe \\1Gruppe ([([ ]*;)ομάδα \\1Gruppo \\1グループ \\1Gruppe ([([ ]*?)Grupa \\1Grupo \\1Grup \\1Группа \\1Grupo \\1Grupp ([([ ]*?)Grup \\1Група
no .*growth FALSE FALSE FALSE FALSE 无.*生长 žádný .*růst ingen .*vækst geen .*groei ei .*kasvua pas .*croissance keine(|n|m|r|s)|nicht .*wachstum όχι .*αύξηση sem .*crescimento 成長なし nei .*vekst brak .*wzrostu sem .*crescimento fără creștere отсутствие.*роста no .*crecimientonon ingen .*tillväxt büyüme yok відсутність .*росту
no|not FALSE FALSE FALSE FALSE 不|不 ne nej|ikke geen|niet ei non keine? no|not sem no|ない nei|ikke nie|nie sem nu нет? no|sin nej|inte hayır|değil|hayir|degil ні
Intermediate TRUE FALSE FALSE FALSE 中级 Meziprodukt Mellemliggende Intermediair Väliaikainen Mittlere Ενδιάμεση 中間体 Mellomliggende Pośrednia Intermediar Intermedio Mellanliggande Orta seviye Знижена чутливість
Susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，暴露增加 Vnímavý, zvýš. expozice Modtagelig, øget eksp. Gevoelig bij verh. blootstelling Altis, lisääntynyt altist. Empfindlich, erh Belastung Ευάλωτος, αυξημένη έκθεση 感受性、曝露量増加 Mottakelig, økt eksp. Podatne, zwiększone narażenie Susceptibil, exp. crescută Susceptible, mayor exposición Mottaglig, inkr. exponering Duyarlı, enk. maruziyet Чутливий до підвищеної експозиції
susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，接触增加 náchylná,zvýš. Expozice modtagelig, øget eksp. gevoelig bij verh. blootstelling altis, lisääntynyt altist. empfindlich, erh Belastung Ευαίσθητος, αυξημένη έκθεση 影響を受けやすい、露出が増える mottakelig, økt eksp. podatny, zwiększone narażenie susceptibil, exp. crescută susceptible, mayor exposición mottaglig, inkr. exponering duyarlı, enk. maruziyet чутливий до підвищеної експозиції
Susceptible TRUE FALSE FALSE FALSE 易受影响 Susceptible Modtagelig Gevoelig Altis Empfindlich Ευαίσθητο 影響を受けやすい Mottakelig Podatny Susceptibil Susceptible Mottaglig Duyarlı Чутливий
Incr. exposure TRUE FALSE FALSE FALSE 暴露增加 zvýšená expozice Øget eksponering 'Incr. exposure' Lisääntynyt altistuminen Empfindlich, erh Belastung Αυξημένη έκθεση 曝露量増加 Økt eksp. Większe narażenie Exp. crescută Mayor exposición Inkr. exponering Enk. maruziyet Підвищена експозиція
Resistant TRUE FALSE FALSE FALSE 耐药性 Rezistentní Resistent Resistent Kestävä Resistent Ανθεκτικός 耐性 Resistent Odporny Rezistent Resistente Resistent Dayanıklı Стійкий
Not interpretable TRUE FALSE FALSE FALSE 无法解释 Nelze interpretovat Ufortolkelig Niet interpreteerbaar Ei tulkittavissa Non interprétable Nicht interpretierbar Μη ερμηνεύσιμο Non interpretabile 解釈不可 Utolkelig ikke Niemożliwe do interpretacji Neinterpretabil Непереводимо No interpretable Inte tolkningsbar Yorumlanamaz Непридатний до інтерпретації
Intermediate TRUE FALSE FALSE FALSE 中级 Meziprodukt Mellemliggende Intermediair Väliaikainen Intermédiaire Mittlere Ενδιάμεση Intermedio 中間体 Mellomliggende Pośrednia Intermediário Intermediar Проміжний Intermedio Mellanliggande Orta seviye Знижена чутливість
Susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，暴露增加 Vnímavý, zvýšená expozice Modtagelig, øget eksponering Gevoelig bij verhoogde blootstelling Altis, lisääntynyt altistuminen Sensible, exposition accrue Empfindlich, erhöhte Belastung Ευάλωτος, αυξημένη έκθεση Sensibile, esposizione aumentata 感受性、曝露量増加 Mottakelig, økt eksponering Podatne, zwiększone narażenie Suscetível, exposição aumentada Susceptibil, expunere crescută Чутливий, підвищена експозиція Susceptible, mayor exposición Mottaglig, ökad exponering Duyarlı, artmış maruziyet Чутливий до підвищеної експозиції
susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，接触增加 Vnímavý, zvýšená expozice Modtagelig, øget eksponering Gevoelig bij verhoogde blootstelling Altis, lisääntynyt altistuminen Sensible, exposition accrue Empfindlich, erhöhte Belastung Ευαίσθητος, αυξημένη έκθεση Sensibile, esposizione aumentata 影響を受けやすい、露出が増える Mottakelig, økt eksponering Podatne, zwiększone narażenie Suscetível, exposição aumentada Susceptibil, expunere crescută Чутливий, підвищена експозиція Susceptible, mayor exposición Mottaglig, ökad exponering Duyarlı, artmış maruziyet Чутливий до підвищеної експозиції
Susceptible TRUE FALSE FALSE FALSE 易受影响 Vnímavý Modtagelig Gevoelig Altis Sensible Empfindlich Ευαίσθητο Sensibile 影響を受けやすい Mottakelig Podatny Suscetível Susceptibil Чутливий Susceptible Mottaglig Duyarlı Чутливий
Incr. exposure TRUE FALSE FALSE FALSE 暴露增加 Zvýšená expozice Øget eksponering Verhoogde blootstelling Lisääntynyt altistuminen Exposition accrue Erhöhte Belastung Αυξημένη έκθεση Esposizione aumentata 曝露量増加 Økt eksponering Większe narażenie Exposição aumentada Expunere crescută Підвищена експозиція Mayor exposición Ökad exponering Artmış maruziyet Підвищена експозиція
Resistant TRUE FALSE FALSE FALSE 耐药性 Rezistentní Resistent Resistent Kestävä Résistant Resistent Ανθεκτικός Resistente 耐性 Resistent Odporny Resistente Rezistent Стійкий Resistente Resistent Dayanıklı Стійкий
Not interpretable TRUE FALSE FALSE FALSE 无法解释 Nelze interpretovat Ufortolkelig Niet interpreteerbaar Ei tulkittavissa Non interprétable Nicht interpretierbar Μη ερμηνεύσιμο Non interpretabile 解釈不可 Utolkelig ikke Niemożliwe do interpretacji Não interpretável Neinterpretabil Непереводимо No interpretable Inte tolkningsbar Yorumlanamaz Непридатний до інтерпретації
antibiotic TRUE TRUE FALSE FALSE 抗生素 antibiotikum antibiotikum antibioticum antibiootti antibiotique Antibiotikum αντιβιοτικό antibiotico 抗生物質 Antibiotikum antybiotyk antibiótico antibiotic антибиотик antibiótico antibiotika Antibiyotik антибіотик
Antibiotic TRUE TRUE FALSE FALSE 抗生素 Antibiotikum Antibiotikum Antibioticum Antibiootti Antibiotique Antibiotikum Αντιβιοτικό Antibiotico 抗生物質 Antibiotikum Antybiotyk Antibiótico Antibiotic Антибиотик Antibiótico Antibiotika Antibiyotik Антибіотик
Drug TRUE TRUE FALSE FALSE 药物 Lék Lægemiddel Middel Lääke Médicament Medikament Φάρμακο Droga 薬剤 Legemiddel Lek Droga Medicament Лекарство Fármaco Läkemedel İlaç Лікарський засіб

Can't render this file because it has a wrong number of fields in line 48.

81
man/antibiogram.Rd
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@ -21,18 +21,19 @@
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 14, 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",
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
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 = 1,
wisca(x, antibiotics = where(is.sir), ab_transform = "name",
syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE,
digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL, language = get_AMR_locale(), minimum = 30,
combine_SI = TRUE, sep = " + ", simulations = 1000,
conf_interval = 0.95, interval_side = "two-tailed",
info = interactive())
retrieve_wisca_parameters(wisca_model, ...)
@ -55,7 +56,7 @@ retrieve_wisca_parameters(wisca_model, ...)
\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{add_total_n}{a \link{logical} to indicate whether \code{n_tested} 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"). This option is unavailable when \code{wisca = TRUE}; in that case, use \code{\link[=retrieve_wisca_parameters]{retrieve_wisca_parameters()}} to get the parameters used for WISCA.}
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
@ -73,7 +74,7 @@ retrieve_wisca_parameters(wisca_model, ...)
\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 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.}
\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}, \code{conf_interval}, and \code{interval_side} to adjust.}
\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations}
@ -108,7 +109,7 @@ For estimating antimicrobial coverage, especially when creating a WISCA, the out
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.
\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 antimicrobial coverage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
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 (\code{4-6} indicates the confidence level), \code{15} the number of susceptible isolates, and \code{300} the number of tested (i.e., available) isolates:
\enumerate{
\item 5
\item 15
@ -119,13 +120,11 @@ The formatting of the 'cells' of the table can be set with the argument \code{fo
\item 5 (N=300)
\item 5\% (N=300)
\item 5 (15/300)
\item 5\% (15/300) - \strong{default for non-WISCA}
\item 5\% (15/300)
\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\%) - \strong{default for WISCA}
\item 5\% (4-6\%) - \strong{default}
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
@ -136,7 +135,7 @@ Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \co
\item 5\% (4-6\%,N=15/300)
}
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.
The default is \code{14}, 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 total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
@ -145,7 +144,7 @@ Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibi
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{Explaining WISCA} on this page.
For clinical coverage estimations, \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. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
\enumerate{
\item \strong{Traditional Antibiogram}
@ -215,13 +214,16 @@ In clinical practice, antimicrobial coverage decisions evolve as more microbiolo
At admission, no pathogen information is available.
\itemize{
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.
\item Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = NA) # all pathogens set to `NA`
# preferred: use WISCA
wisca(your_data,
antibiotics = selected_regimens)
}\if{html}{\out{</div>}}
}
\item \strong{Refinement with Gram Stain Results}
@ -233,7 +235,6 @@ When a blood culture becomes positive, the Gram stain provides an initial and cr
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
}\if{html}{\out{</div>}}
}
@ -246,7 +247,6 @@ After cultivation of the pathogen, full pathogen identification allows precise t
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = selected_regimens,
wisca = TRUE,
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
}\if{html}{\out{</div>}}
}
@ -290,7 +290,7 @@ You can also use functions from specific 'table reporting' packages to transform
\section{Explaining WISCA}{
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.
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 decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
@ -324,6 +324,8 @@ Stratified results can be provided based on covariates such as age, sex, and cli
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{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.
\strong{Note:} WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
}
\examples{
@ -341,14 +343,12 @@ antibiogram(example_isolates,
antibiogram(example_isolates,
antibiotics = aminoglycosides(),
ab_transform = "atc",
mo_transform = "gramstain"
)
mo_transform = "gramstain")
antibiogram(example_isolates,
antibiotics = carbapenems(),
ab_transform = "name",
mo_transform = "name"
)
mo_transform = "name")
# Combined antibiogram -------------------------------------------------
@ -356,16 +356,14 @@ antibiogram(example_isolates,
# combined antibiotics yield higher empiric coverage
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain"
)
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 = " & "
)
sep = " & ")
# Syndromic antibiogram ------------------------------------------------
@ -373,8 +371,7 @@ antibiogram(example_isolates,
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
)
syndromic_group = "ward")
# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -387,27 +384,24 @@ antibiogram(ex1,
syndromic_group = ifelse(ex1$ward == "ICU",
"UCI", "No UCI"
),
language = "es"
)
language = "es")
# WISCA antibiogram ----------------------------------------------------
# can be used for any of the above types - just add `wisca = TRUE`
# WISCA are not stratified by species, but rather on syndromes
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain",
wisca = TRUE
)
syndromic_group = "ward",
wisca = TRUE)
# Print the output for R Markdown / Quarto -----------------------------
ureido <- antibiogram(example_isolates,
antibiotics = ureidopenicillins(),
ab_transform = "name",
wisca = TRUE
)
syndromic_group = "name",
wisca = TRUE)
# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
@ -420,14 +414,11 @@ if (requireNamespace("knitr")) {
ab1 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
wisca = TRUE
)
mo_transform = "gramstain")
ab2 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
syndromic_group = "ward"
)
syndromic_group = "ward")
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab1)

25
man/plot.Rd
View File

@ -9,6 +9,7 @@
\alias{scale_fill_mic}
\alias{scale_x_sir}
\alias{scale_colour_sir}
\alias{scale_color_sir}
\alias{scale_fill_sir}
\alias{plot.mic}
\alias{autoplot.mic}
@ -19,6 +20,7 @@
\alias{facet_sir}
\alias{scale_y_percent}
\alias{scale_sir_colours}
\alias{scale_sir_colors}
\alias{theme_sir}
\alias{labels_sir_count}
\title{Plotting Helpers for AMR Data Analysis}
@ -29,8 +31,6 @@ scale_y_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_colour_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_color_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_x_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
@ -45,7 +45,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), eucast_I = getOption("AMR_guideline",
"EUCAST") == "EUCAST", ...)
\method{plot}{mic}(x, mo = NULL, ab = NULL, guideline = "EUCAST",
\method{plot}{mic}(x, mo = NULL, ab = NULL,
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(x)), ylab = translate_AMR("Frequency", language
= language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
@ -55,8 +56,9 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
\method{autoplot}{mic}(object, mo = NULL, ab = NULL,
guideline = "EUCAST", title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
guideline = getOption("AMR_guideline", "EUCAST"),
title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
language), colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
@ -66,7 +68,7 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
\method{plot}{disk}(x, main = deparse(substitute(x)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL, ab = NULL, guideline = "EUCAST",
mo = NULL, ab = NULL, guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
@ -75,8 +77,9 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
\method{autoplot}{disk}(object, mo = NULL, ab = NULL,
title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
language = language), xlab = translate_AMR("Disk diffusion diameter (mm)",
language = language), guideline = "EUCAST", colours_SIR = c("#3CAEA3",
"#F6D55C", "#ED553B"), language = get_AMR_locale(), expand = TRUE,
language = language), guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(), expand = TRUE,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
@ -169,12 +172,12 @@ Especially the \verb{scale_*_mic()} functions are relevant wrappers to plot MIC
\details{
\subsection{The \verb{scale_*_mic()} Functions}{
The functions \code{\link[=scale_x_mic]{scale_x_mic()}}, \code{\link[=scale_y_mic]{scale_y_mic()}}, \code{\link[=scale_colour_mic]{scale_colour_mic()}}, and \code{\link[=scale_fill_mic]{scale_fill_mic()}} functions allow to plot the \link[=as.mic]{mic} class (MIC values) on a continuous scale. They allow to rescale the MIC range, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
The functions \code{\link[=scale_x_mic]{scale_x_mic()}}, \code{\link[=scale_y_mic]{scale_y_mic()}}, \code{\link[=scale_colour_mic]{scale_colour_mic()}}, and \code{\link[=scale_fill_mic]{scale_fill_mic()}} functions allow to plot the \link[=as.mic]{mic} class (MIC values) on a continuous, logarithmic scale. They also allow to rescale the MIC range with an 'inside' or 'outside' range if required, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
}
\subsection{The \verb{scale_*_sir()} Functions}{
The functions \code{\link[=scale_x_sir]{scale_x_sir()}}, \code{\link[=scale_colour_sir]{scale_colour_sir()}}, and \code{\link[=scale_fill_sir]{scale_fill_sir()}} functions allow to plot the \link[=as.sir]{sir} class (S/I/R values). They can translate the S/I/R values to any of the 20 supported languages, and set colour-blind friendly colours to the \code{colour} and \code{fill} aesthetics.
The functions \code{\link[=scale_x_sir]{scale_x_sir()}}, \code{\link[=scale_colour_sir]{scale_colour_sir()}}, and \code{\link[=scale_fill_sir]{scale_fill_sir()}} functions allow to plot the \link[=as.sir]{sir} class in the right order (S < SDD < I < R < NI). At default, they translate the S/I/R values to an interpretative text ("Susceptible", "Resistant", etc.) in any of the 20 supported languages (use \code{language = NULL} to keep S/I/R). Also, except for \code{\link[=scale_x_sir]{scale_x_sir()}}, they set colour-blind friendly colours to the \code{colour} and \code{fill} aesthetics.
}
\subsection{Additional \code{ggplot2} Functions}{
@ -190,7 +193,7 @@ This package contains more functions that extend the \code{ggplot2} package, to
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2024", "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2024", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011". Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option \code{\link[=AMR-options]{AMR_guideline}} is set. See \code{\link[=as.sir]{as.sir()}} for more information.
}
}
\examples{

4
tests/testthat/test-ggplot_sir.R
View File

@ -121,13 +121,13 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE) &&
# support for manual colours
expect_inherits(
(ggplot(data.frame(
suppressWarnings((ggplot(data.frame(
x = c("Value1", "Value2", "Value3"),
y = c(1, 2, 3),
z = c("Value4", "Value5", "Value6")
)) +
geom_col(aes(x = x, y = y, fill = z)) +
scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R"))$data,
scale_sir_colours(aesthetics = "fill", Value4 = "S", Value5 = "I", Value6 = "R"))$data),
"data.frame"
)
}