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(v2.1.1.9140) WISCA fix

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dr. M.S. (Matthijs) Berends 2025-02-05 20:48:35 +01:00
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 2.1.1.9139
Date: 2025-02-01
Version: 2.1.1.9140
Date: 2025-02-05
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

1
NAMESPACE
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@ -316,6 +316,7 @@ export(rescale_mic)
export(reset_AMR_locale)
export(resistance)
export(resistance_predict)
export(retrieve_wisca_parameters)
export(rifamycins)
export(right_join_microorganisms)
export(scale_colour_mic)

2
NEWS.md
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@ -1,4 +1,4 @@
# AMR 2.1.1.9139
# AMR 2.1.1.9140
*(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).)*

2
PythonPackage/AMR/AMR.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.2
Name: AMR
Version: 2.1.1.9139
Version: 2.1.1.9140
Summary: A Python wrapper for the AMR R package
Home-page: https://github.com/msberends/AMR
Author: Matthijs Berends

1
PythonPackage/AMR/AMR/__init__.py
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@ -27,6 +27,7 @@ from .functions import age
from .functions import age_groups
from .functions import antibiogram
from .functions import wisca
from .functions import retrieve_wisca_parameters
from .functions import amr_class
from .functions import amr_selector
from .functions import aminoglycosides

3
PythonPackage/AMR/AMR/functions.py
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@ -111,6 +111,9 @@ def antibiogram(x, *args, **kwargs):
def wisca(x, *args, **kwargs):
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
return convert_to_python(amr_r.wisca(x, *args, **kwargs))
def retrieve_wisca_parameters(wisca_model, *args, **kwargs):
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
return convert_to_python(amr_r.retrieve_wisca_parameters(wisca_model, *args, **kwargs))
def amr_class(amr_class, *args, **kwargs):
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
return convert_to_python(amr_r.amr_class(amr_class, *args, **kwargs))

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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='2.1.1.9139',
version='2.1.1.9140',
packages=find_packages(),
install_requires=[
'rpy2',

2
R/aa_globals.R
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@ -113,7 +113,7 @@ TAXONOMY_VERSION <- list(
name = "Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT)",
accessed_date = as.Date("2024-07-16"),
citation = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12).",
url = "https://phinvads.cdc.gov"
url = "https://www.cdc.gov/phin/php/phinvads"
),
LOINC = list(
name = "Logical Observation Identifiers Names and Codes (LOINC)",

200
R/antibiogram.R
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@ -34,21 +34,21 @@
#'
#' Adhering to previously described approaches (see *Source*) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki *et al.*, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
#' @param x a [data.frame] containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see [as.sir()])
#' @param antibiotics vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_class_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be set to values separated with `"+"`, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antimicrobials exist in `x`. See *Examples*.
#' @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.
#' @param antibiotics vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_class_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be set to values separated with `"+"`, such as `"TZP+TOB"` or `"cipro + genta"`, given that columns resembling such antimicrobials exist in `x`. See *Examples*.
#' @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 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 susceptibility percentage
#' @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.
#' @param col_mo column name of the names or codes of the microorganisms (see [as.mo()]) - the default is the first column of class [`mo`]. Values will be coerced using [as.mo()].
#' @param language language to translate text, which defaults to the system language (see [get_AMR_locale()])
#' @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 hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set `simulations` to adjust.
#' @param simulations (for WISCA) a numerical value to set the number of Montecarlo simulations
#' @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 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"`
#' @param info a [logical] to indicate info should be printed - the default is `TRUE` only in interactive mode
@ -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 susceptibility percentage (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 (for WISCA: `4-6` indicates the confidence level), `15` the numerator, and `300` the denominator:
#'
#' 1. 5
#' 2. 15
@ -81,17 +81,17 @@
#'
#' Additional options for WISCA (using `antibiogram(..., wisca = TRUE)` or `wisca()`):
#' 13. 5 (4-6)
#' 14. 5% (4-6%)
#' 14. 5% (4-6%) - **default for WISCA**
#' 15. 5 (4-6,300)
#' 16. 5% (4-6%,300)
#' 17. 5 (4-6,N=300)
#' 18. 5% (4-6%,N=300) - **default for WISCA**
#' 18. 5% (4-6%,N=300)
#' 19. 5 (4-6,15/300)
#' 20. 5% (4-6%,15/300)
#' 21. 5 (4-6,N=15/300)
#' 22. 5% (4-6%,N=15/300)
#'
#' The default is `18` 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)`.
#' 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.
#'
#' Set `digits` (defaults to `0`) to alter the rounding of the susceptibility percentages.
#'
@ -99,7 +99,7 @@
#'
#' 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 *Why Use WISCA?* on this page.
#' **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.
#'
#' 1. **Traditional Antibiogram**
#'
@ -137,7 +137,7 @@
#'
#' 4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
#'
#' WISCA can be applied to any antibiogram, see the section *Why Use WISCA?* on this page for more information.
#' WISCA can be applied to any antibiogram, see the section *Explaining WISCA* on this page for more information.
#'
#' Code example:
#'
@ -153,36 +153,87 @@
#'
#' WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
#'
#' Grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
#' ### Grouped tibbles
#'
#' For any type of antibiogram, grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
#'
#' Code example:
#'
#' ```r
#' library(dplyr)
#' your_data %>%
#' group_by(has_sepsis, is_neonate, sex) %>%
#' wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
#' ```
#'
#' ### Inclusion in Combination Antibiogram and Syndromic Antibiogram
#' ### Stepped Approach for Clinical Insight
#'
#' Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the `only_all_tested` argument (default is `FALSE`). See this example for two antimicrobials, Drug A and Drug B, about how [antibiogram()] works to calculate the %SI:
#' In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
#'
#' 1. **Initial Empirical Therapy (Admission / Pre-Culture Data)**
#'
#' At admission, no pathogen information is available.
#'
#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = NA) # all pathogens set to `NA`
#' ```
#'
#' 2. **Refinement with Gram Stain Results**
#'
#' When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
#'
#' - Action: narrow coverage based on Gram stain-specific resistance patterns.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
#' ```
#'
#' 3. **Definitive Therapy Based on Species Identification**
#'
#' After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
#'
#' - Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
#' ```
#'
#' By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
#'
#' ### Inclusion in Combination Antibiograms
#'
#' Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the `only_all_tested` argument (default is `FALSE`). See this example for two antimicrobials, Drug A and Drug B, about how [antibiogram()] works to calculate the %SI:
#'
#' ```
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' ----------------------- -----------------------
#' Drug A Drug B include as include as include as include as
#' numerator denominator numerator denominator
#' -------- -------- ---------- ----------- ---------- -----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' Drug A Drug B considered considered considered considered
#' susceptible tested susceptible tested
#' -------- -------- ----------- ---------- ----------- ----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' --------------------------------------------------------------------
#' ```
#'
@ -194,7 +245,7 @@
#'
#' You can also use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. with `flextable::as_flextable()` or `gt::gt()`.
#'
#' @section Why Use WISCA?:
#' @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.
#'
@ -361,8 +412,8 @@ antibiogram <- function(x,
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
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,
@ -385,8 +436,8 @@ antibiogram.default <- function(x,
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
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,
@ -400,7 +451,7 @@ antibiogram.default <- function(x,
meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
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)
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)
}
if (!is.function(ab_transform)) {
meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE)
@ -429,7 +480,9 @@ antibiogram.default <- function(x,
# transform MOs
x$`.mo` <- x[, col_mo, drop = TRUE]
if (is.null(mo_transform)) {
# leave as is
# leave as is, no transformation
} else if (is.na(mo_transform)) {
x$`.mo` <- NA_character_
} else if (is.function(mo_transform)) {
x$`.mo` <- mo_transform(x$`.mo`)
} else if (mo_transform == "gramstain") {
@ -536,6 +589,7 @@ antibiogram.default <- function(x,
)
counts <- out
wisca_params <- NULL
if (wisca == TRUE) {
# WISCA ----
@ -547,9 +601,12 @@ antibiogram.default <- function(x,
title = "Calculating beta/gamma parameters for WISCA")
on.exit(close(progress))
out$percentage = NA_real_
out$lower = NA_real_
out$upper = NA_real_
out$coverage <- NA_real_
out$lower <- NA_real_
out$upper <- NA_real_
out$gamma_posterior <- NA_real_
out$beta_posterior_1 <- NA_real_
out$beta_posterior_2 <- NA_real_
for (i in seq_len(NROW(out))) {
if (out$total[i] == 0) {
@ -559,22 +616,29 @@ antibiogram.default <- function(x,
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
# Monte Carlo simulation
coverage_simulations <- replicate(simulations, {
# simulate pathogen incidence
# = Dirichlet (Gamma) parameters
simulated_incidence <- stats::rgamma(
n = length(priors$gamma_posterior),
random_incidence <- runif(1, min = 0, max = 1)
simulated_incidence <- stats::qgamma(
p = random_incidence,
shape = priors$gamma_posterior,
rate = 1 # Scale = 1 for gamma
scale = 1
)
# normalise
simulated_incidence <- simulated_incidence / sum(simulated_incidence)
# simulate susceptibility
# = Beta parameters
simulated_susceptibility <- stats::rbeta(
n = length(priors$beta_posterior_1),
random_susceptibity <- runif(1, min = 0, max = 1)
simulated_susceptibility <- stats::qbeta(
p = random_susceptibity,
shape1 = priors$beta_posterior_1,
shape2 = priors$beta_posterior_2
)
@ -592,7 +656,7 @@ antibiogram.default <- function(x,
}
coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
out$percentage[i] <- coverage_mean
out$coverage[i] <- coverage_mean
out$lower[i] <- coverage_ci[1]
out$upper[i] <- coverage_ci[2]
}
@ -640,19 +704,25 @@ antibiogram.default <- function(x,
if (wisca == TRUE) {
long_numeric <- out %pm>%
pm_summarise(percentage = percentage,
lower = lower,
upper = upper,
numerator = numerator,
total = total)
pm_summarise(coverage = coverage,
lower_ci = lower,
upper_ci = upper,
n_tested = total,
n_total = total_rows,
n_susceptible = numerator,
p_susceptible = numerator / total,
gamma_posterior = gamma_posterior,
beta_posterior1 = beta_posterior_1,
beta_posterior2 = beta_posterior_2)
} else {
long_numeric <- out %pm>%
pm_summarise(percentage = numerator / total,
pm_summarise(coverage = numerator / total,
numerator = numerator,
total = total)
}
out$digits <- digits # since pm_sumarise() cannot work with an object outside the current frame
# formatting type:
# 1. 5
# 2. 15
@ -688,12 +758,12 @@ antibiogram.default <- function(x,
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 == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ")"))
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%)"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",", total, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,", total, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",N=", total, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,N=", total, ")"))
if (formatting_type == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ")"))
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%)"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",", total, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,", total, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",N=", total, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,N=", total, ")"))
# transform names of antibiotics
ab_naming_function <- function(x, t, l, s) {
@ -812,8 +882,8 @@ antibiogram.grouped_df <- function(x,
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
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,
@ -910,8 +980,8 @@ wisca <- function(x,
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -938,9 +1008,15 @@ wisca <- function(x,
info = info)
}
#' @export
#' @param wisca_model the outcome of [wisca()] or [antibiogram(..., wisca = TRUE)]
#' @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
}
calculate_priors <- function(data, combine_SI = TRUE) {
# Ensure data has required columns
stopifnot(all(c("mo", "total_rows", "total", "S") %in% colnames(data)))
if (combine_SI == TRUE && "I" %in% colnames(data)) {
data$S <- data$S + data$I
}
@ -1013,7 +1089,7 @@ plot.antibiogram <- function(x, ...) {
df_sub <- df[df$mo == mo, , drop = FALSE]
bp <- barplot(
height = df_sub$percentage * 100,
height = df_sub$coverage * 100,
xlab = NULL,
ylab = ifelse(isTRUE(attributes(x)$combine_SI), "%SI", "%S"),
names.arg = df_sub$ab,
@ -1055,7 +1131,7 @@ autoplot.antibiogram <- function(object, ...) {
out <- ggplot2::ggplot(df,
mapping = ggplot2::aes(
x = ab,
y = percentage * 100,
y = coverage * 100,
fill = if ("syndromic_group" %in% colnames(df)) {
syndromic_group
} else {

26
R/proportion.R
View File

@ -46,6 +46,8 @@
#' @param collapse a [logical] to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing
#' @inheritSection as.sir Interpretation of SIR
#' @details
#' For a more automated and comprehensive analysis, consider using [antibiogram()] or [wisca()], which streamline many aspects of susceptibility reporting and, importantly, also support WISCA. The functions described here offer a more hands-on, manual approach for greater customisation.
#'
#' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set with one of the four available algorithms.
#'
#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()]. Since AMR v3.0, [proportion_SI()] and [proportion_I()] include dose-dependent susceptibility ('SDD').
@ -63,18 +65,18 @@
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' ----------------------- -----------------------
#' Drug A Drug B include as include as include as include as
#' numerator denominator numerator denominator
#' -------- -------- ---------- ----------- ---------- -----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' Drug A Drug B considered considered considered considered
#' susceptible tested susceptible tested
#' -------- -------- ----------- ---------- ----------- ----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' --------------------------------------------------------------------
#' ```
#'

180
data-raw/gpt_training_text_v2.1.1.9139.txt → data-raw/gpt_training_text_v2.1.1.9140.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 2.1.1.9139. Remember this whenever someone asks which AMR package version you’re at.
First and foremost, you are trained on version 2.1.1.9140. 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.
----------------------------------------------------------------------------------------------------
@ -327,6 +327,7 @@ export(rescale_mic)
export(reset_AMR_locale)
export(resistance)
export(resistance_predict)
export(retrieve_wisca_parameters)
export(rifamycins)
export(right_join_microorganisms)
export(scale_colour_mic)
@ -1625,6 +1626,7 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
\name{antibiogram}
\alias{antibiogram}
\alias{wisca}
\alias{retrieve_wisca_parameters}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{knit_print.antibiogram}
@ -1641,21 +1643,23 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
\usage{
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 18, 10)), col_mo = NULL, language = get_AMR_locale(),
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",
info = interactive())
wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
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,
info = interactive())
retrieve_wisca_parameters(wisca_model, ...)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
@ -1666,9 +1670,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\arguments{
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input.}
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
\item{ab_transform}{a character to transform antimicrobial input - must be one of the column names of the \link{antibiotics} data set (defaults to \code{"name"}): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be \code{NULL} to not transform the input.}
@ -1678,7 +1682,7 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
\item{digits}{number of digits to use for rounding the susceptibility percentage}
\item{digits}{number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise}
\item{formatting_type}{numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
@ -1692,9 +1696,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{sep}{a separating character for antimicrobial columns in combination antibiograms}
\item{wisca}{a \link{logical} to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is \code{FALSE}). This will use a Bayesian hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set \code{simulations} to adjust.}
\item{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{simulations}{(for WISCA) a numerical value to set the number of Montecarlo simulations}
\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations}
\item{conf_interval}{(for WISCA) a numerical value to set confidence interval (default is \code{0.95})}
@ -1702,6 +1706,8 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \link{antibiogram(..., wisca = TRUE)}}
\item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
@ -1725,7 +1731,7 @@ 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 susceptibility percentage (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 (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
\enumerate{
\item 5
\item 15
@ -1742,18 +1748,18 @@ The formatting of the 'cells' of the table can be set with the argument \code{fo
Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \code{wisca()}):
\item 5 (4-6)
\item 5\% (4-6\%)
\item 5\% (4-6\%) - \strong{default for WISCA}
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
\item 5\% (4-6\%,N=300) - \strong{default for WISCA}
\item 5\% (4-6\%,N=300)
\item 5 (4-6,15/300)
\item 5\% (4-6\%,15/300)
\item 5 (4-6,N=15/300)
\item 5\% (4-6\%,N=15/300)
}
The default is \code{18} for WISCA and \code{10} for non-WISCA, which can be set globally with the package option \code{\link[=AMR-options]{AMR_antibiogram_formatting_type}}, e.g. \code{options(AMR_antibiogram_formatting_type = 5)}.
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.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
@ -1762,7 +1768,7 @@ 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{Why Use WISCA?} on this page.
\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.
\enumerate{
\item \strong{Traditional Antibiogram}
@ -1794,7 +1800,7 @@ Code example:
}\if{html}{\out{</div>}}
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
WISCA can be applied to any antibiogram, see the section \emph{Why Use WISCA?} on this page for more information.
WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA} on this page for more information.
Code example:
@ -1809,36 +1815,88 @@ wisca(your_data,
WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
}
}
Grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
\subsection{Grouped tibbles}{
For any type of antibiogram, grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{your_data \%>\%
\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
your_data \%>\%
group_by(has_sepsis, is_neonate, sex) \%>\%
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{</div>}}
}
\subsection{Inclusion in Combination Antibiogram and Syndromic Antibiogram}{
\subsection{Stepped Approach for Clinical Insight}{
Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
\enumerate{
\item \strong{Initial Empirical Therapy (Admission / Pre-Culture Data)}
At admission, no pathogen information is available.
\itemize{
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
\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`
}\if{html}{\out{</div>}}
}
\item \strong{Refinement with Gram Stain Results}
When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
\itemize{
\item Action: narrow coverage based on Gram stain-specific resistance patterns.
\item Code example:
\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>}}
}
\item \strong{Definitive Therapy Based on Species Identification}
After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
\itemize{
\item Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
\item Code example:
\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>}}
}
}
By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
}
\subsection{Inclusion in Combination Antibiograms}{
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}
}
@ -1852,7 +1910,7 @@ The outcome of \code{\link[=antibiogram]{antibiogram()}} can also be used direct
You can also use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. with \code{flextable::as_flextable()} or \code{gt::gt()}.
}
}
\section{Why Use WISCA?}{
\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.
@ -3082,7 +3140,7 @@ This is based on:
\item Vincent, R \emph{et al} (2013). \strong{MycoBank gearing up for new horizons.} IMA Fungus, 4(2), 371-9; \doi{10.5598/imafungus.2013.04.02.16}. Accessed from \url{https://www.mycobank.org} on June 24th, 2024.
\item GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on June 24th, 2024.
\item Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://www.cdc.gov/phin/php/phinvads}
\item Bartlett A \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}
}
@ -4155,18 +4213,18 @@ When using more than one variable for \code{...} (= combination therapy), use \c
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}
@ -6441,7 +6499,7 @@ Furthermore, these sources were used for additional details:
\item BacDive:\cr\cr
Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT):\cr\cr
Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed from \url{https://phinvads.cdc.gov} on July 16th, 2024.
Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed from \url{https://www.cdc.gov/phin/php/phinvads} on July 16th, 2024.
\item Grimont \emph{et al.} (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on \emph{Salmonella} (WHOCC-SALM).
\item Bartlett \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}
@ -6897,7 +6955,7 @@ This function uses \code{\link[=as.mo]{as.mo()}} internally, which uses an advan
\item Vincent, R \emph{et al} (2013). \strong{MycoBank gearing up for new horizons.} IMA Fungus, 4(2), 371-9; \doi{10.5598/imafungus.2013.04.02.16}. Accessed from \url{https://www.mycobank.org} on June 24th, 2024.
\item GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on June 24th, 2024.
\item Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://www.cdc.gov/phin/php/phinvads}
\item Bartlett A \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}
}
@ -7603,6 +7661,8 @@ These functions can be used to calculate the (co-)resistance or susceptibility o
\code{\link[=resistance]{resistance()}} should be used to calculate resistance, \code{\link[=susceptibility]{susceptibility()}} should be used to calculate susceptibility.\cr
}
\details{
For a more automated and comprehensive analysis, consider using \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}, which streamline many aspects of susceptibility reporting and, importantly, also support WISCA. The functions described here offer a more hands-on, manual approach for greater customisation.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}. Since AMR v3.0, \code{\link[=proportion_SI]{proportion_SI()}} and \code{\link[=proportion_I]{proportion_I()}} include dose-dependent susceptibility ('SDD').
@ -7620,18 +7680,18 @@ When using more than one variable for \code{...} (= combination therapy), use \c
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}

121
man/antibiogram.Rd
View File

@ -3,6 +3,7 @@
\name{antibiogram}
\alias{antibiogram}
\alias{wisca}
\alias{retrieve_wisca_parameters}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{knit_print.antibiogram}
@ -19,21 +20,23 @@
\usage{
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 18, 10)), col_mo = NULL, language = get_AMR_locale(),
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",
info = interactive())
wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = 0,
formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
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,
info = interactive())
retrieve_wisca_parameters(wisca_model, ...)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
@ -44,9 +47,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\arguments{
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input.}
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
\item{ab_transform}{a character to transform antimicrobial input - must be one of the column names of the \link{antibiotics} data set (defaults to \code{"name"}): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be \code{NULL} to not transform the input.}
@ -56,7 +59,7 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}}
\item{digits}{number of digits to use for rounding the susceptibility percentage}
\item{digits}{number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise}
\item{formatting_type}{numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
@ -70,9 +73,9 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{sep}{a separating character for antimicrobial columns in combination antibiograms}
\item{wisca}{a \link{logical} to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is \code{FALSE}). This will use a Bayesian hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set \code{simulations} to adjust.}
\item{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{simulations}{(for WISCA) a numerical value to set the number of Montecarlo simulations}
\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations}
\item{conf_interval}{(for WISCA) a numerical value to set confidence interval (default is \code{0.95})}
@ -80,6 +83,8 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \link{antibiogram(..., wisca = TRUE)}}
\item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
@ -103,7 +108,7 @@ 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 susceptibility percentage (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 (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
\enumerate{
\item 5
\item 15
@ -120,18 +125,18 @@ The formatting of the 'cells' of the table can be set with the argument \code{fo
Additional options for WISCA (using \code{antibiogram(..., wisca = TRUE)} or \code{wisca()}):
\item 5 (4-6)
\item 5\% (4-6\%)
\item 5\% (4-6\%) - \strong{default for WISCA}
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
\item 5\% (4-6\%,N=300) - \strong{default for WISCA}
\item 5\% (4-6\%,N=300)
\item 5 (4-6,15/300)
\item 5\% (4-6\%,15/300)
\item 5 (4-6,N=15/300)
\item 5\% (4-6\%,N=15/300)
}
The default is \code{18} for WISCA and \code{10} for non-WISCA, which can be set globally with the package option \code{\link[=AMR-options]{AMR_antibiogram_formatting_type}}, e.g. \code{options(AMR_antibiogram_formatting_type = 5)}.
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.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
@ -140,7 +145,7 @@ 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{Why Use WISCA?} on this page.
\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.
\enumerate{
\item \strong{Traditional Antibiogram}
@ -172,7 +177,7 @@ Code example:
}\if{html}{\out{</div>}}
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
WISCA can be applied to any antibiogram, see the section \emph{Why Use WISCA?} on this page for more information.
WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA} on this page for more information.
Code example:
@ -187,36 +192,88 @@ wisca(your_data,
WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
}
}
Grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
\subsection{Grouped tibbles}{
For any type of antibiogram, grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{your_data \%>\%
\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
your_data \%>\%
group_by(has_sepsis, is_neonate, sex) \%>\%
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{</div>}}
}
\subsection{Inclusion in Combination Antibiogram and Syndromic Antibiogram}{
\subsection{Stepped Approach for Clinical Insight}{
Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
\enumerate{
\item \strong{Initial Empirical Therapy (Admission / Pre-Culture Data)}
At admission, no pathogen information is available.
\itemize{
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
\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`
}\if{html}{\out{</div>}}
}
\item \strong{Refinement with Gram Stain Results}
When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
\itemize{
\item Action: narrow coverage based on Gram stain-specific resistance patterns.
\item Code example:
\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>}}
}
\item \strong{Definitive Therapy Based on Species Identification}
After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
\itemize{
\item Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
\item Code example:
\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>}}
}
}
By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
}
\subsection{Inclusion in Combination Antibiograms}{
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}
}
@ -230,7 +287,7 @@ The outcome of \code{\link[=antibiogram]{antibiogram()}} can also be used direct
You can also use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. with \code{flextable::as_flextable()} or \code{gt::gt()}.
}
}
\section{Why Use WISCA?}{
\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.

2
man/as.mo.Rd
View File

@ -160,7 +160,7 @@ This is based on:
\item Vincent, R \emph{et al} (2013). \strong{MycoBank gearing up for new horizons.} IMA Fungus, 4(2), 371-9; \doi{10.5598/imafungus.2013.04.02.16}. Accessed from \url{https://www.mycobank.org} on June 24th, 2024.
\item GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on June 24th, 2024.
\item Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://www.cdc.gov/phin/php/phinvads}
\item Bartlett A \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}
}

24
man/count.Rd
View File

@ -90,18 +90,18 @@ When using more than one variable for \code{...} (= combination therapy), use \c
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}

2
man/microorganisms.Rd
View File

@ -44,7 +44,7 @@ Furthermore, these sources were used for additional details:
\item BacDive:\cr\cr
Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT):\cr\cr
Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed from \url{https://phinvads.cdc.gov} on July 16th, 2024.
Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed from \url{https://www.cdc.gov/phin/php/phinvads} on July 16th, 2024.
\item Grimont \emph{et al.} (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on \emph{Salmonella} (WHOCC-SALM).
\item Bartlett \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}

2
man/mo_property.Rd
View File

@ -222,7 +222,7 @@ This function uses \code{\link[=as.mo]{as.mo()}} internally, which uses an advan
\item Vincent, R \emph{et al} (2013). \strong{MycoBank gearing up for new horizons.} IMA Fungus, 4(2), 371-9; \doi{10.5598/imafungus.2013.04.02.16}. Accessed from \url{https://www.mycobank.org} on June 24th, 2024.
\item GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on June 24th, 2024.
\item Reimer, LC \emph{et al.} (2022). \strong{\emph{BacDive} in 2022: the knowledge base for standardized bacterial and archaeal data.} Nucleic Acids Res., 50(D1):D741-D74; \doi{10.1093/nar/gkab961}. Accessed from \url{https://bacdive.dsmz.de} on July 16th, 2024.
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://www.cdc.gov/phin/php/phinvads}
\item Bartlett A \emph{et al.} (2022). \strong{A comprehensive list of bacterial pathogens infecting humans} \emph{Microbiology} 168:001269; \doi{10.1099/mic.0.001269}
}
}

26
man/proportion.Rd
View File

@ -85,6 +85,8 @@ These functions can be used to calculate the (co-)resistance or susceptibility o
\code{\link[=resistance]{resistance()}} should be used to calculate resistance, \code{\link[=susceptibility]{susceptibility()}} should be used to calculate susceptibility.\cr
}
\details{
For a more automated and comprehensive analysis, consider using \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}, which streamline many aspects of susceptibility reporting and, importantly, also support WISCA. The functions described here offer a more hands-on, manual approach for greater customisation.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}. Since AMR v3.0, \code{\link[=proportion_SI]{proportion_SI()}} and \code{\link[=proportion_I]{proportion_I()}} include dose-dependent susceptibility ('SDD').
@ -102,18 +104,18 @@ When using more than one variable for \code{...} (= combination therapy), use \c
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}

2
pkgdown/extra.js
View File

@ -118,7 +118,7 @@ $(document).ready(function() {
x = x.replace("Christian", "Dr. Christian");
x = x.replace("Corinna", "Dr. Corinna");
x = x.replace("Dennis", "Dr. Dennis");
x = x.replace("Gwen", "Dr. Gwen");
x = x.replace("Gwen", "Prof. Gwen");
x = x.replace("Jason", "Dr. Jason");
x = x.replace("Javier", "Prof. Javier");
x = x.replace("Jonas", "Dr. Jonas");

6
tests/testthat/test-zzz.R
View File

@ -141,7 +141,11 @@ call_functions <- c(
import_functions <- c(import_functions, call_functions)
suggests <- strsplit(utils::packageDescription(pkg = ".", lib.loc = ".", fields = "Suggests"), "[,\n ]+")[[1]]
suggests <- tryCatch(strsplit(utils::packageDescription(pkg = ".", lib.loc = ".", fields = "Suggests"), "[,\n ]+")[[1]],
error = function(e) {
print(list.files())
return(import_functions)
})
for (i in seq_len(length(import_functions))) {
fn <- names(import_functions)[i]
pkg <- unname(import_functions[i])