P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2026-06-24 03:36:21 +02:00
Code Releases Activity

(v3.0.1.9059) Update taxonomy of microorganisms

Browse Source
This commit is contained in:
Matthijs Berends
2026-06-23 01:38:13 +02:00
committed by GitHub
parent 0af3f84655
commit 3f9f931777
123 changed files with 121928 additions and 94162 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
AMR.Rproj
View File

@@ -2,7 +2,7 @@ Version: 1.0
ProjectId: 5128c748-a412-44db-a5fb-45c68c93dd10
RestoreWorkspace: No
SaveWorkspace: No
SaveWorkspace: Ask
AlwaysSaveHistory: Yes
EnableCodeIndexing: Yes

4
DESCRIPTION
View File

@@ -1,6 +1,6 @@
Package: AMR
Version: 3.0.1.9057
Date: 2026-05-02
Version: 3.0.1.9059
Date: 2026-06-23
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
View File

@@ -300,6 +300,7 @@ export(mo_is_yeast)
export(mo_kingdom)
export(mo_lpsn)
export(mo_matching_score)
export(mo_morphology)
export(mo_mycobank)
export(mo_name)
export(mo_order)
@@ -385,6 +386,7 @@ export(translate_AMR)
export(trimethoprims)
export(ureidopenicillins)
export(wisca)
export(wisca_plot)
if(getRversion() >= "3.0.0") S3method(cleaner::freq, mo)
if(getRversion() >= "3.0.0") S3method(cleaner::freq, sir)
if(getRversion() >= "3.0.0") S3method(ggplot2::autoplot, antibiogram)

38
NEWS.md
View File

@@ -1,35 +1,55 @@
# AMR 3.0.1.9057
# AMR 3.0.1.9059
Planned as v3.1.0, May 2026.
Planned as v3.1.0, end of June 2026.
### Breaking Changes
* The former *kingdoms* Bacteria and Archaea are now each divided into four kingdoms with new top-level *domains* 'Bacteria' and 'Archaea' (Göker and Oren, 2024, DOI: 10.1099/ijsem.0.006242). Following this, a new `domain` column in the `microorganisms` data set was added, and more importantly, `mo_kingdom()` now returns the formal kingdom (e.g. `"Pseudomonadati"` instead of `"Bacteria"`). Use `mo_domain()` for the old behaviour. For non-prokaryotic kingdoms (Fungi, Protozoa, etc.), `kingdom` and `domain` are identical.
* Faster parallel computing via the `future` package for `as.sir()` and `wisca()`: a non-sequential plan (e.g. `future::plan(future::multisession)`) must be active before using `parallel = TRUE`.
### New
* EUCAST 2026 and CLSI 2026 breakpoints: over 5,700 new breakpoints added to the `clinical_breakpoints` data set; EUCAST 2026 is now the default for all MIC and disk diffusion interpretations
* Wildtype/Non-wildtype (WT/NWT) output when using ECOFF-based interpretation, by setting `breakpoint_type = "ECOFF"` in `as.sir()`; WT/NWT results are fully supported in all resistance/susceptibility functions and plots (#254)
* Faster parallel computing via the `future` package; **breaking change**: a non-sequential plan (e.g. `future::plan(future::multisession)`) must be active before using `parallel = TRUE`; `antibiogram()` and `wisca()` now also support `parallel = TRUE` (#281)
* *tidymodels* integration for using SIR, MIC and disk data in modelling pipelines: `step_mic_log2()`, `step_sir_numeric()`, and new column selectors `all_sir()`, `all_mic()`, `all_disk()`
* New `esbl_isolates` data set for practising AMR modelling
* New antimicrobial selectors: `ionophores()`, `peptides()`, `phosphonics()`, `spiropyrimidinetriones()`
* New antimicrobials: cefepime/taniborbactam (`FTA`), ceftibuten/avibactam (`CTA`), clorobiocin (`CLB`), kasugamycin (`KAS`), ostreogrycin (`OST`), taniborbactam (`TAN`), thiostrepton (`THS`), xeruborbactam (`XER`), zorbamycin (`ZOR`)
* New `interpretive_rules()`, a unified function for EUCAST and CLSI interpretive rules; `eucast_rules()` is now a wrapper around it (#235, #259)
* New `morphology` column in the `microorganisms` data set and corresponding `mo_morphology()` function, returning the cell shape of bacteria. Data sourced from BacDive; values prefixed with "likely" are extrapolated from genus-level consensus. New `add_morphology` argument was added to `mo_gramstain()` to return combined results such as `"Gram-negative rods"`.
* New `amr_course()` to download and unpack course or webinar materials from GitHub in one call
* Typed missing value constants `NA_ab_` and `NA_mo_`, for use in pipelines that need missing values of a specific class
* New function `wisca_plot()` to assess the susceptibility and incidence distributions from the Monte Carlo simulations
### Fixes
* `as.sir()` on data frames: already-converted SIR columns no longer dropped on re-run (#278); metadata columns (e.g. `patient`, `ward`) no longer misidentified as antibiotic columns; `info = FALSE` now suppresses all messages, including for columns without breakpoints
### Fixed
* `as.sir()`
* On data frames: already-converted SIR columns no longer dropped on re-run (#278)
* Metadata columns (e.g. `patient`, `ward`) no longer misidentified as antibiotic columns
* `info = FALSE` now suppresses all messages, including for columns without breakpoints
* Assumption of disk zones are now preferred over MIC values when input is only whole numbers (#291)
* `as.mic()`: values in scientific notation (e.g. `1e-3`) now handled correctly
* `as.ab()`: codes containing "PH" or "TH" (e.g. `ETH`, `PHE`) no longer return `NA` when mixed with unrecognised input (#245)
* Combined MIC/SIR input values (e.g. `"<= 0.002; S"` or `"S; 0.002"`) now parsed correctly (#252)
* `as.mo()`:
* Input of the form `"X complex"` now falls back to `"X"` when the complex is not a distinct taxon in the database, preventing `NA` results for valid clinical descriptions such as `"Proteus vulgaris complex"` (#287)
* Abbreviated-genus input (e.g. `"S. apiospermum"`) now correctly ranks candidates whose species epithet exactly matches the input above more-prevalent organisms whose species does not match; fixes `"S. apiospermum"` resolving to *Staphylococcus* instead of *Scedosporium apiospermum* (#288)
* `get_author_year()` in the microorganism reproduction script now strips `emend.` and everything after it, so `ref` reflects the combination authority rather than the emendation author (e.g. *Rhodococcus equi* now returns "Goodfellow et al., 1977" instead of "Nouioui et al., 2018")
* BRMO classification now includes bacterial complexes (#275)
* Translation fixes for Italian CoNS/CoPS names (#256), Dutch antimicrobials, and `sir_df()` foreign-language output (#272)
* Fixed some EUCAST Expert Rules, mostly on *S. pneumoniae*
### Updates
### Updated
* Taxonomic update for all microorganisms, now updated to June 2026
* `mo_kingdom()` now returns the formal taxonomic kingdom; a one-time note per session explains the change when querying bacterial or archaeal records.
* `mo_taxonomy()` and `mo_info()` gained `domain` for the list output
* `antibiogram()` and `wisca()` now also support parallel computing via the argument `parallel = TRUE` (#281)
* `custom_eucast_rules()` renamed to `custom_interpretive_rules()`; old name deprecated but still works (#268)
* `mdro()` can now infer resistance from a drug+inhibitor combination when the base drug column is absent (e.g. piperacillin inferred from piperacillin/tazobactam); controlled via new `infer_from_combinations` argument (default `TRUE`) (#209)
* `wisca()` now more strictly follows Bielicki et al. (2016) by using $\text{Beta}(1, 9999)$ for intrinsically resistant pairs, forcing near-zero susceptibility regardless of observed data (based on EUCAST Expected Resistant Phenotypes)
* `susceptibility()` / `resistance()`: new `guideline` argument (default EUCAST) to ensure the 'I' category is interpreted correctly per guideline
* Capped MIC handling in `as.sir()` reworked into four clearly defined options: `"none"`, `"conservative"` (new default), `"standard"`, `"lenient"` (#243)
* `as.mic()` / `rescale_mic()`: new `round_to_next_log2` argument to round values up to the nearest log2 dilution level (#255)
* `antimicrobials$group` now a `list`, so drugs belonging to multiple groups are fully represented; use `ab_group(all_groups = TRUE)` to retrieve all groups for a drug (#246)
* New antimicrobials added: cefepime/taniborbactam (`FTA`), ceftibuten/avibactam (`CTA`), clorobiocin (`CLB`), kasugamycin (`KAS`), ostreogrycin (`OST`), taniborbactam (`TAN`), thiostrepton (`THS`), xeruborbactam (`XER`), zorbamycin (`ZOR`)
* Improved console messages with clickable links throughout, powered by `cli` (#191, #265)
* `antimicrobials$group` is now a `list`, so that drugs belonging to multiple groups are fully represented; use `ab_group(all_groups = TRUE)` to retrieve all groups for a drug (#246)
* Improved console messages with clickable links throughout, powered by `cli` if it is installed (#191, #265)
* `as.disk()`: input validation is now more strict, rejecting values that are not recognisable as a numeric disk zone diameter
# AMR 3.0.1

2
R/aa_amr-package.R
View File

@@ -45,7 +45,7 @@
#' For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.
#'
#' Visit [our website for direct download links](https://amr-for-r.org/articles/datasets.html), or explore the actual files in [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw/datasets).
#' @source
#' @references
#' To cite AMR in publications use:
#'
#' Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). "AMR: An R Package for Working with Antimicrobial Resistance Data." _Journal of Statistical Software_, *104*(3), 1-31. \doi{10.18637/jss.v104.i03}

20
R/aa_globals.R
View File

@@ -104,26 +104,27 @@ EUCAST_VERSION_EXPECTED_PHENOTYPES <- list(
TAXONOMY_VERSION <- list(
GBIF = list(
name = "Global Biodiversity Information Facility (GBIF)",
accessed_date = as.Date("2024-06-24"),
citation = "GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
name = "Global Biodiversity Information Facility (GBIF), via Catalogue of Life (COL)",
accessed_date = as.Date("2026-05-07"),
# take the citation from https://www.gbif.org/dataset/7ddf754f-d193-4cc9-b351-99906754a03b#citation
citation = "Banki, O. *et al.* (2026). Catalogue of Life (2026-04-18 XR). Catalogue of Life Foundation, Amsterdam, Netherlands. \\doi{10.48580/dgxjw}.",
url = "https://www.gbif.org"
),
LPSN = list(
name = "List of Prokaryotic names with Standing in Nomenclature (LPSN)",
accessed_date = as.Date("2024-06-24"),
citation = "Parte, AC *et al.* (2020). **List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.** International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \\doi{10.1099/ijsem.0.004332}.",
accessed_date = as.Date("2026-05-07"),
citation = "Freese, HM *et al.* (2026). **TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity.** Nucleic Acids Research, 54, D884\u2013D891; \\doi{10.1093/nar/gkaf1110}.",
url = "https://lpsn.dsmz.de"
),
MycoBank = list(
name = "MycoBank",
accessed_date = as.Date("2024-06-24"),
accessed_date = as.Date("2026-05-07"),
citation = "Vincent, R *et al* (2013). **MycoBank gearing up for new horizons.** IMA Fungus, 4(2), 371-9; \\doi{10.5598/imafungus.2013.04.02.16}.",
url = "https://www.mycobank.org"
),
BacDive = list(
name = "BacDive",
accessed_date = as.Date("2024-07-16"),
accessed_date = as.Date("2026-05-07"),
citation = "Reimer, LC *et al.* (2022). ***BacDive* in 2022: the knowledge base for standardized bacterial and archaeal data.** Nucleic Acids Res., 50(D1):D741-D74; \\doi{10.1093/nar/gkab961}.",
url = "https://bacdive.dsmz.de"
),
@@ -148,10 +149,13 @@ TAXONOMY_VERSION <- list(
)
globalVariables(c(
".coverage",
".GenericCallEnv",
".lower",
".mo",
".rowid",
".syndromic_group",
".upper",
"ab",
"ab_txt",
"affect_ab_name",
@@ -187,6 +191,7 @@ globalVariables(c(
"hjust",
"host_index",
"host_match",
"incidence",
"input",
"input_given",
"intrinsic_resistant",
@@ -214,6 +219,7 @@ globalVariables(c(
"old",
"old_name",
"p_susceptible",
"pathogen",
"pattern",
"R",
"rank_index",

67
R/aa_helper_functions.R
View File

@@ -489,7 +489,11 @@ cli_to_plain <- function(msg, envir = parent.frame()) {
if (length(m) >= 2L) m[2L] else paste0("?", resolve(c))
})
msg <- apply_sub(msg, "\\{\\.url (\\{[^}]+\\}|[^}]+)\\}", function(c) resolve(c))
msg <- apply_sub(msg, "\\{\\.href ([^}]+)\\}", function(c) strsplit(resolve(c), " ", fixed = TRUE)[[1L]][1L])
msg <- apply_sub(msg, "\\{\\.href ([^}]+)\\}", function(c) {
# Handle [display text](url) markdown link format: extract just the URL
m <- regmatches(c, regexec("^\\[.*\\]\\(([^)]+)\\)$", c))[[1L]]
if (length(m) >= 2L) m[2L] else resolve(c)
})
# bare {variable} or {expression} -> evaluate in caller's environment
while (grepl("\\{[^{}]+\\}", msg)) {
@@ -551,7 +555,7 @@ word_wrap <- function(...,
indentation <- 0L + extra_indent
}
if (indentation > 0L) {
wrapped <- gsub("\n", paste0("\n", strrep(" ", indentation)), wrapped, fixed = TRUE)
wrapped <- gsub("\n", paste0("\n", strrep("\u00a0", indentation)), wrapped, fixed = TRUE)
}
gsub("(\n| )+$", "", wrapped)
}
@@ -583,13 +587,27 @@ simplify_help_markup <- function(msg) {
message_ <- function(...,
appendLF = TRUE,
as_note = TRUE) {
as_note = TRUE,
as_check = FALSE,
extra_indent = 0,
with_bullet = FALSE) {
msg <- paste0(c(...), collapse = "")
if (with_bullet == TRUE) {
as_note <- FALSE
msg <- paste0(AMR_env$bullet_icon, "\u00a0", msg)
}
if (extra_indent > 0) {
msg <- paste0(strrep("\u00a0", extra_indent), msg)
}
if (has_cli_rlang()) {
msg <- paste0(c(...), collapse = "")
# prevent errors with single opening curly brackets, we don't evaluate cli's/glue's {} in AMR anyway
msg <- gsub("\\{(?!\\.)", "", msg, perl = TRUE)
if (!cli::ansi_has_hyperlink_support()) {
msg <- simplify_help_markup(msg)
}
if (isTRUE(as_note)) {
if (isTRUE(as_check)) {
cli::cli_inform(c("v" = msg), .envir = parent.frame())
} else if (isTRUE(as_note)) {
cli::cli_inform(c("i" = msg), .envir = parent.frame())
} else if (isTRUE(appendLF)) {
cli::cli_inform(msg, .envir = parent.frame())
@@ -598,22 +616,28 @@ message_ <- function(...,
cat(format_inline_(msg), file = stderr())
}
} else {
plain_msg <- cli_to_plain(paste0(c(...), collapse = ""), envir = parent.frame())
plain_msg <- cli_to_plain(msg, envir = parent.frame())
message(word_wrap(plain_msg, as_note = as_note), appendLF = appendLF)
}
}
warning_ <- function(...,
immediate = FALSE,
call = FALSE) {
call = FALSE,
extra_indent = 0) {
msg <- paste0(c(...), collapse = "")
if (extra_indent > 0) {
msg <- paste0(strrep("\u00a0", extra_indent), msg)
}
if (has_cli_rlang()) {
msg <- paste0(c(...), collapse = "")
# prevent errors with single opening curly brackets, we don't evaluate cli's/glue's {} in AMR anyway
msg <- gsub("\\{(?!\\.)", "", msg, perl = TRUE)
if (!cli::ansi_has_hyperlink_support()) {
msg <- simplify_help_markup(msg)
}
cli::cli_warn(msg, .envir = parent.frame())
} else {
plain_msg <- cli_to_plain(paste0(c(...), collapse = ""), envir = parent.frame())
plain_msg <- cli_to_plain(msg, envir = parent.frame())
warning(trimws2(word_wrap(plain_msg, as_note = FALSE)), immediate. = immediate, call. = call)
}
}
@@ -621,8 +645,15 @@ warning_ <- function(...,
# this alternative to the stop() function:
# - adds the function name where the error was thrown (plain-text fallback)
# - wraps text to never break lines within words (plain-text fallback)
stop_ <- function(..., call = TRUE) {
stop_ <- function(...,
call = TRUE,
extra_indent = 0) {
msg <- paste0(c(...), collapse = "")
if (extra_indent > 0) {
msg <- paste0(strrep("\u00a0", extra_indent), msg)
}
# prevent errors with single opening curly brackets, we don't evaluate cli's/glue's {} in AMR anyway
msg <- gsub("\\{(?!\\.)", "", msg, perl = TRUE)
if (!cli::ansi_has_hyperlink_support()) {
msg <- simplify_help_markup(msg)
}
@@ -727,7 +758,7 @@ documentation_date <- function(d) {
suffix[day %in% c(1, 21, 31)] <- "st"
suffix[day %in% c(2, 22)] <- "nd"
suffix[day %in% c(3, 23)] <- "rd"
paste0(month.name[as.integer(format(d, "%m"))], " ", day, suffix, ", ", format(d, "%Y"))
paste0(day, suffix, " of ", month.name[as.integer(format(d, "%m"))], ", ", format(d, "%Y"))
}
format_included_data_number <- function(data) {
@@ -1635,14 +1666,14 @@ add_MO_lookup_to_AMR_env <- function() {
if (is.null(AMR_env$MO_lookup)) {
MO_lookup <- AMR::microorganisms
MO_lookup$kingdom_index <- NA_real_
MO_lookup[which(MO_lookup$kingdom == "Bacteria" | as.character(MO_lookup$mo) == "UNKNOWN"), "kingdom_index"] <- 1
MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 1.25
MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 1.5
MO_lookup[which(MO_lookup$kingdom == "Chromista"), "kingdom_index"] <- 1.75
MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 2
MO_lookup$domain_index <- NA_real_
MO_lookup[which(MO_lookup$domain == "Bacteria" | as.character(MO_lookup$mo) == "UNKNOWN"), "domain_index"] <- 1
MO_lookup[which(MO_lookup$domain == "Fungi"), "domain_index"] <- 1.25
MO_lookup[which(MO_lookup$domain == "Protozoa"), "domain_index"] <- 1.5
MO_lookup[which(MO_lookup$domain == "Chromista"), "domain_index"] <- 1.75
MO_lookup[which(MO_lookup$domain == "Archaea"), "domain_index"] <- 2
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 3
MO_lookup[which(is.na(MO_lookup$domain_index)), "domain_index"] <- 3
# the fullname lowercase, important for the internal algorithms in as.mo()
MO_lookup$fullname_lower <- tolower(trimws2(paste(

2
R/ab.R
View File

@@ -54,7 +54,7 @@
#' @section Source:
#' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://atcddd.fhi.no/atc_ddd_index/}
#'
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/reg_hum_atc.htm}
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/index_en.htm}
#' @aliases ab
#' @return A [character] [vector] with additional class [`ab`]
#' @seealso

538
R/antibiogram.R
View File

@@ -61,7 +61,9 @@
#' @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 sort_columns A [logical] to indicate whether the antimicrobial columns must be sorted on name.
#' @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 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). Per \doi{10.1093/jac/dkv397}, susceptibility priors are \eqn{\beta(0.5, 0.5)} (Jeffreys) and intrinsically resistant pairs (based on [intrinsic_resistant]) use \eqn{\beta(1, 9999)}.
#'
#' 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 A numerical value to set confidence interval (default is `0.95`).
#' @param interval_side The side of the confidence interval, either `"two-tailed"` (default), `"left"` or `"right"`.
@@ -166,6 +168,10 @@
#'
#' 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 data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
#'
#' **Prior Distributions**
#'
#' When `wisca = TRUE` or when using `wisca()`, pathogen incidence is modelled with a non-informative \eqn{Dirichlet(1, 1, \ldots, 1)} prior. Susceptibility proportions use the Jeffreys prior, \eqn{\beta(0.5, 0.5)}, except for bug-drug combinations with known intrinsic resistance, which use a strongly informative \eqn{\beta(1, 9999)} prior that forces near-zero susceptibility regardless of observed data (Bielicki *et al.*, 2016). Intrinsic resistance is determined using the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))`.
#'
#' ### Grouped tibbles
#'
#' For any type of antibiogram, grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
@@ -266,7 +272,7 @@
#' It weights susceptibility by pathogen prevalence within a clinical syndrome and provides credible intervals around the expected coverage.
#'
#' For more background, interpretation, and examples, see [the WISCA vignette](https://amr-for-r.org/articles/WISCA.html).
#' @source
#' @references
#' * 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}
#' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
@@ -446,11 +452,11 @@ antibiogram.default <- function(x,
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 (wisca) {
if (!is.null(mo_transform) && !missing(mo_transform)) {
warning_("WISCA must be based on the species level as WISCA parameters are based on this. For that reason, {.arg 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))))
mo_transform <- function(x) suppressMessages(suppressWarnings(as.mo(x, keep_synonyms = TRUE, language = NULL, info = FALSE)))
}
if ("antibiotics" %in% names(list(...))) {
deprecation_warning("antibiotics", "antimicrobials", fn = "antibiogram", is_argument = TRUE)
@@ -484,11 +490,39 @@ antibiogram.default <- function(x,
meet_criteria(parallel, allow_class = "logical", has_length = 1)
# parallel gate - identical pattern to as.sir()
if (requireNamespace("future.apply", quietly = TRUE) && !inherits(future::plan(), "sequential")) {
if (isFALSE(parallel)) {
message_("Assuming {.code parallel = TRUE} since parallel computing has been set up using the {.pkg future} package before. Set {.help [{.fun plan}](future::plan)} to sequential to prevent this.")
if (requireNamespace("future.apply", quietly = TRUE)) {
if (!inherits(future::plan(), "sequential")) {
if (isFALSE(parallel)) {
message_("Assuming {.code parallel = TRUE} since parallel computing has been set up using the {.pkg future} package before. Set {.help [{.fun plan}](future::plan)} to sequential to prevent this.")
}
parallel <- TRUE
}
if (wisca && interactive() && inherits(future::plan(), "sequential") && isFALSE(parallel) && simulations > 100) {
advised_multi <- ifelse(.Platform$OS.type == "windows" || in_rstudio(), "multisession", "multicore")
message_("Are you sure you want to run in non-parallel (=sequential) mode?", as_note = FALSE)
message_("WISCA can take a long time for the ", simulations * length(antimicrobials), " simulations you require, and you already have the {.pkg future} package installed.", as_note = FALSE)
q <- utils::menu(c(
"Yes, still run in sequential mode",
format_inline_("No, run in parallel mode and set {.help [future::plan(", advised_multi, ")](future::plan)}, and reset after WISCA finishes"),
format_inline_("No, run in parallel mode and set {.help [future::plan(", advised_multi, ")](future::plan)}, and do not reset afterwards"),
"Cancel WISCA calculation"
), graphics = FALSE, title = "")
if (q %in% c(4, 0)) {
return(invisible(NULL))
} else if (q %in% c(2, 3)) {
parallel <- TRUE
obj <- get(advised_multi, envir = asNamespace("future"))
future::plan(obj)
if (q == 2) {
on.exit({
# clean-up parallel setting
message_("Resetting {.fn future::plan}...", as_note = FALSE)
future::plan(future::sequential)
message_("Parallel setting was reset to `future::plan(future::sequential)`.", as_check = TRUE)
})
}
}
}
parallel <- TRUE
}
if (isTRUE(parallel)) {
stop_ifnot(
@@ -519,21 +553,26 @@ antibiogram.default <- function(x,
# transform MOs
x$`.mo` <- x[, col_mo, drop = TRUE]
if (is.null(mo_transform)) {
# leave as is, no transformation
} else if (is.function(mo_transform)) {
x$`.mo` <- mo_transform(x$`.mo`)
} else if (is.na(mo_transform)) {
x$`.mo` <- NA_character_
} else if (mo_transform == "gramstain") {
x$`.mo` <- mo_gramstain(x$`.mo`, language = language)
} else if (mo_transform == "shortname") {
x$`.mo` <- mo_shortname(x$`.mo`, language = language)
} else if (mo_transform == "name") {
x$`.mo` <- mo_name(x$`.mo`, language = language)
# leave as is, no transformation, but do add backup
x$`.mo.bak` <- x$`.mo`
} else {
x$`.mo` <- mo_property(x$`.mo`, property = mo_transform, language = language)
x$`.mo` <- as.mo(x$`.mo`, keep_synonyms = TRUE, info = FALSE)
x$`.mo.bak` <- x$`.mo`
if (is.function(mo_transform)) {
x$`.mo` <- mo_transform(x$`.mo`)
} else if (is.na(mo_transform)) {
x$`.mo` <- NA_character_
} else if (mo_transform == "gramstain") {
x$`.mo` <- mo_gramstain(x$`.mo`, language = language)
} else if (mo_transform == "shortname") {
x$`.mo` <- mo_shortname(x$`.mo`, language = language)
} else if (mo_transform == "name") {
x$`.mo` <- mo_name(x$`.mo`, language = language)
} else {
x$`.mo` <- mo_property(x$`.mo`, property = mo_transform, language = language)
}
}
x$`.mo`[is.na(x$`.mo`)] <- "(??)"
x$`.mo`[x$`.mo` %in% c(NA, "UNKNOWN")] <- "(??)"
# get syndromic groups
if (!is.null(syndromic_group)) {
@@ -702,7 +741,7 @@ antibiogram.default <- function(x,
wisca_parameters <- data.frame()
# WISCA START
# WISCA START ----
if (wisca == TRUE) {
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
@@ -736,6 +775,8 @@ antibiogram.default <- function(x,
}
wisca_parameters <- out
wisca_draws <- list()
wisca_components <- list()
# quantile probabilities are constant across all groups
probs <- if (interval_side == "two-tailed") {
@@ -751,6 +792,7 @@ antibiogram.default <- function(x,
use_parallel_wisca <- isTRUE(parallel) && n_workers > 1L && length(unique_groups) > 0L
if (use_parallel_wisca) {
## WISCA parallel ----
if (isTRUE(info)) {
message_("Running WISCA in parallel mode using ", n_workers, " workers...", as_note = FALSE, appendLF = FALSE)
}
@@ -759,13 +801,17 @@ antibiogram.default <- function(x,
chunks_per_group <- max(1L, ceiling(n_workers / length(unique_groups)))
chunk_sizes <- diff(c(0L, round(seq_len(chunks_per_group) * simulations / chunks_per_group)))
params_g_lookup <- list()
# precompute priors per group and build (group, chunk) job list
jobs <- unlist(lapply(unique_groups, function(g) {
params_g <- wisca_parameters[wisca_parameters$group == g, , drop = FALSE]
if (sum(params_g$n_tested, na.rm = TRUE) == 0L) {
return(NULL)
}
priors_g <- create_wisca_priors(params_g)
# store for later reassembly
params_g_lookup[[g]] <<- params_g
priors_g <- create_wisca_priors(params_g, sep = sep)
lapply(seq_along(chunk_sizes), function(ch) {
list(group = g, priors = priors_g, n_sims = chunk_sizes[ch])
})
@@ -773,24 +819,46 @@ antibiogram.default <- function(x,
jobs <- Filter(Negate(is.null), jobs)
flat <- future.apply::future_lapply(jobs, function(job) {
vapply(FUN.VALUE = double(1), seq_len(job$n_sims), function(i) {
simulate_coverage(job$priors)
})
n_p <- length(job$priors$gamma_posterior)
n_s <- job$n_sims
inc_mat <- matrix(NA_real_, nrow = n_s, ncol = n_p)
susc_mat <- matrix(NA_real_, nrow = n_s, ncol = n_p)
cov_vec <- numeric(n_s)
for (i in seq_len(n_s)) {
inc_raw <- stats::rgamma(n_p, shape = job$priors$gamma_posterior, scale = 1)
inc_norm <- inc_raw / sum(inc_raw)
susc <- stats::rbeta(n_p,
shape1 = job$priors$beta_posterior_1,
shape2 = job$priors$beta_posterior_2
)
inc_mat[i, ] <- inc_norm
susc_mat[i, ] <- susc
cov_vec[i] <- sum(inc_norm * susc)
}
list(coverage = cov_vec, incidence = inc_mat, susceptibility = susc_mat)
}, future.seed = TRUE)
# reassemble per group: concatenate chunks, then summarise
for (g in unique_groups) {
g_idx <- vapply(jobs, function(j) identical(j$group, g), logical(1))
if (!any(g_idx)) next
sims <- unlist(flat[g_idx], use.names = FALSE)
chunks <- flat[g_idx]
sims <- unlist(lapply(chunks, `[[`, "coverage"), use.names = FALSE)
inc_combined <- do.call(rbind, lapply(chunks, `[[`, "incidence"))
susc_combined <- do.call(rbind, lapply(chunks, `[[`, "susceptibility"))
colnames(inc_combined) <- as.character(params_g_lookup[[g]]$mo)
colnames(susc_combined) <- as.character(params_g_lookup[[g]]$mo)
wisca_draws[[g]] <- sims
wisca_components[[g]] <- list(incidence = inc_combined, susceptibility = susc_combined)
out_wisca$coverage[out_wisca$group == g] <- mean(sims)
ci_vals <- unname(stats::quantile(sims, probs = probs))
out_wisca$lower_ci[out_wisca$group == g] <- ci_vals[1]
out_wisca$upper_ci[out_wisca$group == g] <- ci_vals[2]
}
if (isTRUE(info)) message_(font_green_bg(" DONE "), as_note = FALSE)
if (isTRUE(info)) message_(font_green_bg("\u00a0DONE\u00a0"), as_note = FALSE)
} else {
## WISCA sequential ----
progress <- progress_ticker(
n = length(unique_groups) * simulations,
n_min = 25,
@@ -802,16 +870,35 @@ antibiogram.default <- function(x,
for (group in unique_groups) {
params_current <- wisca_parameters[wisca_parameters$group == group, , drop = FALSE]
if (sum(params_current$n_tested, na.rm = TRUE) == 0) next
priors_current <- create_wisca_priors(params_current)
coverage_simulations <- vapply(
FUN.VALUE = double(1),
seq_len(simulations), function(i) {
progress$tick()
simulate_coverage(priors_current)
}
priors_current <- create_wisca_priors(params_current, sep = sep)
# replace the vapply block in the sequential branch with:
n_pathogens_g <- length(priors_current$gamma_posterior)
sim_coverage <- numeric(simulations)
sim_incidence <- matrix(NA_real_, nrow = simulations, ncol = n_pathogens_g)
sim_susceptibility <- matrix(NA_real_, nrow = simulations, ncol = n_pathogens_g)
colnames(sim_incidence) <- as.character(params_current$mo)
colnames(sim_susceptibility) <- as.character(params_current$mo)
for (i in seq_len(simulations)) {
progress$tick()
inc_raw <- stats::rgamma(n_pathogens_g, shape = priors_current$gamma_posterior, scale = 1)
inc_norm <- inc_raw / sum(inc_raw)
susc <- stats::rbeta(n_pathogens_g,
shape1 = priors_current$beta_posterior_1,
shape2 = priors_current$beta_posterior_2
)
sim_incidence[i, ] <- inc_norm
sim_susceptibility[i, ] <- susc
sim_coverage[i] <- sum(inc_norm * susc)
}
wisca_draws[[group]] <- sim_coverage
wisca_components[[group]] <- list(
incidence = sim_incidence,
susceptibility = sim_susceptibility
)
out_wisca$coverage[out_wisca$group == group] <- mean(coverage_simulations)
ci_vals <- unname(stats::quantile(coverage_simulations, probs = probs))
out_wisca$coverage[out_wisca$group == group] <- mean(sim_coverage)
ci_vals <- unname(stats::quantile(sim_coverage, probs = probs))
out_wisca$lower_ci[out_wisca$group == group] <- ci_vals[1]
out_wisca$upper_ci[out_wisca$group == group] <- ci_vals[2]
}
@@ -1039,14 +1126,23 @@ antibiogram.default <- function(x,
}
}
if (wisca) {
names(wisca_draws) <- out$ab
names(wisca_components) <- out$ab
}
out <- structure(as_original_data_class(new_df, class(x), extra_class = "antibiogram"),
has_syndromic_group = has_syndromic_group,
combine_SI = combine_SI,
wisca = wisca,
conf_interval = conf_interval,
simulations = if (isFALSE(wisca)) NULL else simulations,
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x))
sep = sep,
wisca_parameters = if (isFALSE(wisca)) NULL else as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x)),
wisca_draws = if (isFALSE(wisca)) NULL else wisca_draws,
wisca_components = if (isFALSE(wisca)) NULL else wisca_components
)
rownames(out) <- NULL
out
@@ -1079,6 +1175,7 @@ antibiogram.grouped_df <- function(x,
stop_ifnot(is.null(mo_transform), "{.arg mo_transform} must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, which could include the pathogen information (though not necessary). Nonetheless, this makes {.arg mo_transform} redundant.", call = FALSE)
stop_ifnot(is.null(syndromic_group), "{.arg syndromic_group} must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making {.arg syndromic_group} redundant.", call = FALSE)
meet_criteria(parallel, allow_class = "logical", has_length = 1)
meet_criteria(wisca, allow_class = "logical", has_length = 1)
groups <- attributes(x)$groups
n_groups <- NROW(groups)
@@ -1191,7 +1288,7 @@ antibiogram.grouped_df <- function(x,
new_out[, col_name] <- col_value
new_out <- new_out[, c(col_name, setdiff(names(new_out), col_name))] # set place to 1st col
if (isTRUE(wisca)) {
if (wisca) {
new_wisca_parameters[, col_name] <- col_value
new_wisca_parameters <- new_wisca_parameters[, c(col_name, setdiff(names(new_wisca_parameters), col_name))] # set place to 1st col
}
@@ -1211,14 +1308,25 @@ antibiogram.grouped_df <- function(x,
}
}
wisca_draws_all <- NULL
wisca_components_all <- NULL
if (wisca) {
wisca_draws_all <- unlist(lapply(results_raw, function(r) attributes(r)$wisca_draws), recursive = FALSE)
wisca_components_all <- unlist(lapply(results_raw, function(r) attributes(r)$wisca_components), recursive = FALSE)
}
out <- structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
has_syndromic_group = FALSE,
combine_SI = isTRUE(combine_SI),
wisca = isTRUE(wisca),
wisca = wisca,
conf_interval = conf_interval,
simulations = if (isFALSE(wisca)) NULL else simulations,
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x))
sep = sep,
wisca_parameters = if (isFALSE(wisca)) NULL else as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x)),
wisca_draws = if (isFALSE(wisca)) NULL else wisca_draws_all,
wisca_components = if (isFALSE(wisca)) NULL else wisca_components_all
)
rownames(out) <- NULL
out
@@ -1269,7 +1377,7 @@ wisca <- function(x,
)
}
create_wisca_priors <- function(data) {
create_wisca_priors <- function(data, sep) {
pathogens <- unique(data$mo)
n_pathogens <- length(pathogens)
@@ -1278,9 +1386,28 @@ create_wisca_priors <- function(data) {
multinomial_obs <- data$n_total
gamma_posterior <- gamma_prior + multinomial_obs
# beta priors
beta_prior_alpha <- rep(1, times = n_pathogens)
beta_prior_beta <- rep(1, times = n_pathogens)
# Beta priors: Jeffreys prior Beta(0.5, 0.5) by default (Bielicki et al., 2016)
beta_prior_alpha <- rep(0.5, n_pathogens)
beta_prior_beta <- rep(0.5, n_pathogens)
# strongly informative Beta(1, 9999) for intrinsically resistant bug-drug pairs (Bielicki et al., 2016)
is_intrinsic <- vapply(
FUN.VALUE = logical(1),
seq_len(nrow(data)),
function(i) {
# split by " + ", or wherever `sep` is set to
ab_components <- as.ab(trimws(strsplit(as.character(data$ab[i]), trimws(sep), fixed = TRUE)[[1]]))
ab_components <- ab_components[!is.na(ab_components)]
length(ab_components) > 0 &&
all(vapply(
FUN.VALUE = logical(1),
ab_components,
function(ab) any(AMR::intrinsic_resistant$mo == data$mo[i] & AMR::intrinsic_resistant$ab == ab)
))
}
)
beta_prior_alpha[is_intrinsic] <- 1
beta_prior_beta[is_intrinsic] <- 9999
r <- data$n_susceptible
n <- data$n_tested
@@ -1334,9 +1461,9 @@ retrieve_wisca_parameters <- function(wisca_model, ...) {
#' @rawNamespace if(getRversion() >= "3.0.0") S3method(pillar::tbl_sum, antibiogram)
tbl_sum.antibiogram <- function(x, ...) {
dims <- paste(format(NROW(x), big.mark = ","), AMR_env$cross_icon, format(NCOL(x), big.mark = ","))
names(dims) <- "An antibiogram"
names(dims) <- "An Antibiogram"
if (isTRUE(attributes(x)$wisca)) {
dims <- c(dims, Type = paste0("WISCA with ", attributes(x)$conf_interval * 100, "% CI"))
dims <- c(dims, Type = paste0("WISCA with ", attributes(x)$conf_interval * 100, "% CI, ", attributes(x)$simulations, " simulations"))
} else if (isTRUE(attributes(x)$formatting_type >= 13)) {
dims <- c(dims, Type = paste0("Non-WISCA with ", attributes(x)$conf_interval * 100, "% CI"))
} else {
@@ -1352,9 +1479,14 @@ tbl_format_footer.antibiogram <- function(x, ...) {
if (NROW(x) == 0) {
return(footer)
}
wisca_text <- ifelse(isTRUE(attributes(x)$wisca),
paste0("\n# ", font_bold("Be aware"), " that in a WISCA, overlapping CIs indicate ", font_bold("non-inferiority"), "."),
""
)
c(footer, font_subtle(paste0(
"# Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,\n",
"# or use it directly in R Markdown or Quarto, see ", word_wrap("?antibiogram")
"# or use it directly in R Markdown or Quarto, see ", word_wrap("?antibiogram"), ".",
wisca_text
)))
}
@@ -1415,8 +1547,29 @@ barplot.antibiogram <- function(height, ...) {
#' @rdname antibiogram
# this prevents the requirement for putting the dependency in Imports:
#' @rawNamespace if(getRversion() >= "3.0.0") S3method(ggplot2::autoplot, antibiogram)
autoplot.antibiogram <- function(object, ...) {
#' @param geom The plotting style for the point estimate. One of `"pointrange"` (default), `"point"`, `"col"`/`"bar"`, or `"errorbar"`. `"pointrange"` is recommended for coverage data: bars imply a meaningful baseline at zero, which coverage estimates rarely have.
#' @param ci Logical, whether to draw the credible/confidence interval. Defaults to `TRUE`. Ignored (forced `TRUE`) when `geom = "pointrange"` or `"errorbar"`, since the interval is intrinsic to those geoms.
#' @param sort Logical, whether to order regimens by coverage. Defaults to `TRUE`. When faceted (per pathogen) or grouped (syndromic), ordering is applied within each panel/group.
#' @param flip Logical, whether to draw regimens on the y-axis (horizontal). Defaults to `NULL`, which flips automatically when any regimen label exceeds 20 characters (long combination names read poorly on the x-axis). Set `TRUE`/`FALSE` to override.
#' @param caption Text to show as caption, will explain non-inferiority for WISCA models.
autoplot.antibiogram <- function(object,
geom = c("pointrange", "point", "col", "bar", "errorbar"),
ci = TRUE,
sort = TRUE,
flip = NULL,
caption = NULL,
...) {
geom <- match.arg(geom)
if (geom == "bar") geom <- "col"
meet_criteria(ci, allow_class = "logical", has_length = 1)
meet_criteria(sort, allow_class = "logical", has_length = 1)
meet_criteria(flip, allow_class = "logical", has_length = 1, allow_NULL = TRUE)
meet_criteria(caption, allow_class = "logical", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE)
df <- attributes(object)$long_numeric
combine_SI <- isTRUE(attributes(object)$combine_SI)
is_wisca <- isTRUE(attributes(object)$wisca)
if (!"mo" %in% colnames(df)) {
df$mo <- ""
}
@@ -1429,36 +1582,273 @@ autoplot.antibiogram <- function(object, ...) {
} else if ("syndromic_group" %in% colnames(df)) {
group_name <- colnames(object)[1]
}
out <- ggplot2::ggplot(df,
has_syndromic <- "syndromic_group" %in% colnames(df)
has_facet <- !all(as.character(df$mo) == "", na.rm = TRUE)
# coverage on the percentage scale
df$.coverage <- df$coverage * 100
df$.lower <- df$lower_ci * 100
df$.upper <- df$upper_ci * 100
# decide orientation: auto-flip when labels are long
if (is.null(flip)) {
flip <- max(nchar(as.character(df$ab)), na.rm = TRUE) > 20
}
# ordering by coverage, applied within facet/group so each panel ranks correctly
if (isTRUE(sort)) {
split_keys <- interaction(
if (has_facet) as.character(df$mo) else rep("", nrow(df)),
if (has_syndromic) df$syndromic_group else rep("", nrow(df)),
drop = TRUE
)
# build a within-group rank, then a global ordered factor whose level order
# respects that rank; reorder_within-style without the tidytext dependency
ord <- order(split_keys, df$.coverage)
df <- df[ord, , drop = FALSE]
df$ab <- factor(df$ab, levels = unique(df$ab[order(split_keys[ord], df$.coverage[ord])]))
# note: with multiple facets the level order is a compromise (one global
# axis), acceptable because each facet shows its own subset in coverage order
}
fill_var <- if (has_syndromic) "syndromic_group" else NULL
out <- ggplot2::ggplot(
df,
mapping = ggplot2::aes(
x = ab,
y = coverage * 100,
fill = if ("syndromic_group" %in% colnames(df)) {
syndromic_group
} else {
NULL
}
y = .coverage,
fill = if (has_syndromic) syndromic_group else NULL,
colour = if (has_syndromic) syndromic_group else NULL
)
) +
ggplot2::geom_col(position = ggplot2::position_dodge2(preserve = "single")) +
ggplot2::geom_errorbar(
mapping = ggplot2::aes(ymin = lower_ci * 100, ymax = upper_ci * 100),
position = ggplot2::position_dodge2(preserve = "single", width = 1)
)
dodge <- ggplot2::position_dodge2(preserve = "single", width = 0.6)
if (geom == "col") {
out <- out + ggplot2::geom_col(position = ggplot2::position_dodge2(preserve = "single"))
if (isTRUE(ci)) {
out <- out + ggplot2::geom_errorbar(
mapping = ggplot2::aes(ymin = .lower, ymax = .upper),
position = ggplot2::position_dodge2(preserve = "single", width = 1),
width = 0.7
)
}
} else if (geom == "point") {
out <- out + ggplot2::geom_point(position = dodge, size = 2)
if (isTRUE(ci)) {
out <- out + ggplot2::geom_errorbar(
mapping = ggplot2::aes(ymin = .lower, ymax = .upper),
position = dodge, width = 0.4
)
}
} else if (geom == "errorbar") {
out <- out + ggplot2::geom_errorbar(
mapping = ggplot2::aes(ymin = .lower, ymax = .upper),
position = dodge, width = 0.4
)
} else {
# pointrange (default)
out <- out + ggplot2::geom_pointrange(
mapping = ggplot2::aes(ymin = .lower, ymax = .upper),
position = dodge, size = 0.5
)
}
if (is.null(caption)) {
if (is_wisca) {
out <- out + ggplot2::labs(caption = "Overlapping credible intervals:\nclinically non-inferior (Bielicki 2020)")
}
} else if (!caption %in% c(FALSE, NA)) {
out <- out + ggplot2::labs(caption = caption)
}
out <- out +
ggplot2::labs(
y = ifelse(combine_SI, "%SI", "%S"),
x = NULL,
fill = if (has_syndromic) group_name else NULL,
colour = if (has_syndromic) group_name else NULL
)
if (isTRUE(flip)) {
out <- out + ggplot2::coord_flip()
}
if (has_facet) {
out <- out + ggplot2::facet_wrap("mo")
}
out
}
#' @param wisca_plot_type Either `"susceptibility_incidence"` (default) or `"posterior_coverage"`.
#' @param ... Currently unused.
#' @rdname antibiogram
#' @export
wisca_plot <- function(wisca_model,
wisca_plot_type = c("susceptibility_incidence", "posterior_coverage"),
...) {
stop_ifnot_installed("ggplot2")
stop_ifnot(
isTRUE(attributes(wisca_model)$wisca),
"This function only applies to WISCA models."
)
wisca_plot_type <- match.arg(wisca_plot_type)
sep <- attributes(wisca_model)$sep %||% " + "
if (wisca_plot_type == "posterior_coverage") {
plot_wisca_posterior_coverage(wisca_model, sep = sep)
} else {
plot_wisca_susceptibility_incidence(wisca_model, sep = sep)
}
}
# ---- posterior_coverage ----
plot_wisca_posterior_coverage <- function(wisca_model, sep) {
draws <- attributes(wisca_model)$wisca_draws
stop_if(
is.null(draws),
"No simulation draws found. Re-run {.fun wisca} with the latest AMR version to retain draws."
)
if (!is.null(sep)) {
names(draws) <- gsub(sep, paste0(trimws(sep, which = "right"), "\n"), names(draws), fixed = TRUE)
}
df <- do.call(rbind, lapply(names(draws), function(nm) {
data.frame(regimen = nm, coverage = draws[[nm]] * 100, stringsAsFactors = FALSE)
}))
medians <- tapply(df$coverage, df$regimen, stats::median)
df$regimen <- factor(df$regimen, levels = names(sort(medians, decreasing = TRUE)))
ggplot2::ggplot(df, ggplot2::aes(x = coverage, fill = regimen, colour = regimen)) +
ggplot2::geom_density(alpha = 0.15, linewidth = 0.7) +
ggplot2::scale_y_continuous(n.breaks = 5, expand = ggplot2::expansion(c(0, 0.05))) +
ggplot2::scale_x_continuous(
labels = function(x) paste0(x, "%"),
n.breaks = 5,
limits = c(NA, 100)
) +
ggplot2::labs(
y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
x = NULL,
fill = if ("syndromic_group" %in% colnames(df)) {
group_name
} else {
NULL
}
title = "WISCA",
subtitle = "Posteriors coverage",
x = "Coverage",
y = "Relative likelihood",
fill = translate_AMR("Regimen", language = get_AMR_locale()),
colour = translate_AMR("Regimen", language = get_AMR_locale())
) +
ggplot2::theme(
legend.position = "right",
legend.key.spacing.y = ggplot2::unit(0.25, "lines"),
plot.title = ggplot2::element_text(size = 12, face = "bold")
)
if (!all(as.character(df$mo) == "", na.rm = TRUE)) {
out <- out +
ggplot2::facet_wrap("mo")
}
# ---- susceptibility_incidence scatter, faceted by regimen ----
plot_wisca_susceptibility_incidence <- function(wisca_model, sep) {
components <- attributes(wisca_model)$wisca_components
stop_if(
is.null(components),
"No simulation components found. Re-run {.fun wisca} with the latest AMR version to retain draws."
)
df_list <- lapply(names(components), function(g) {
comp <- components[[g]]
n_sims <- nrow(comp$incidence)
n_path <- ncol(comp$incidence)
mo_names <- colnames(comp$incidence)
reg_label <- g
if (!is.null(sep)) {
reg_label <- gsub(sep, paste0(trimws(sep, which = "right"), "\n"), g, fixed = TRUE)
}
data.frame(
regimen = rep(reg_label, n_sims * n_path),
pathogen = rep(mo_names, each = n_sims),
incidence = as.vector(comp$incidence) * 100,
susceptibility = as.vector(comp$susceptibility) * 100,
stringsAsFactors = FALSE
)
})
df <- do.call(rbind, df_list)
df$pathogen <- mo_shortname(df$pathogen, keep_synonyms = TRUE, info = FALSE)
# order pathogens by median incidence across all regimens
med_inc <- tapply(df$incidence, df$pathogen, stats::median)
df$pathogen <- factor(df$pathogen, levels = names(sort(med_inc, decreasing = TRUE)))
# order regimens by median coverage (from wisca_draws)
draws <- attributes(wisca_model)$wisca_draws
if (!is.null(draws)) {
med_cov <- vapply(names(draws), function(g) stats::median(draws[[g]]), double(1))
reg_labels <- unique(df$regimen)
# match order: draws names -> display labels
draw_order <- names(sort(med_cov, decreasing = TRUE))
label_order <- vapply(draw_order, function(g) {
if (!is.null(sep)) gsub(sep, paste0(trimws(sep, which = "right"), "\n"), g, fixed = TRUE) else g
}, character(1))
label_order <- label_order[label_order %in% reg_labels]
df$regimen <- factor(df$regimen, levels = label_order)
}
out
# retrieve coverage + CI
coverage <- attributes(wisca_model)$long_numeric
if (!is.null(sep)) {
coverage$ab <- gsub(sep, paste0(trimws(sep, which = "right"), "\n"), coverage$ab, fixed = TRUE)
}
df$coverage <- coverage$coverage[match(df$regimen, coverage$ab)] * 100
df$lower_ci <- coverage$lower_ci[match(df$regimen, coverage$ab)] * 100
df$upper_ci <- coverage$upper_ci[match(df$regimen, coverage$ab)] * 100
ci_df <- df[!duplicated(df$regimen), c("regimen", "coverage", "lower_ci", "upper_ci"), drop = FALSE]
ggplot2::ggplot(df, ggplot2::aes(x = susceptibility, y = incidence, colour = pathogen)) +
ggplot2::geom_rect(
data = ci_df,
ggplot2::aes(xmin = lower_ci, xmax = upper_ci, ymin = -Inf, ymax = Inf),
fill = "grey50", alpha = 0.15, colour = NA,
inherit.aes = FALSE
) +
ggplot2::geom_vline(
data = ci_df,
ggplot2::aes(xintercept = coverage),
linewidth = 0.5,
linetype = 2,
colour = "grey50",
inherit.aes = FALSE
) +
ggplot2::geom_point(size = 0.5, alpha = 0.2, shape = 16) +
ggplot2::facet_wrap(~regimen) +
ggplot2::scale_y_continuous(
labels = function(x) paste0(x, "%"),
n.breaks = 5
) +
ggplot2::scale_x_continuous(
labels = function(x) paste0(x, "%"),
limits = c(0, 100)
) +
ggplot2::labs(
title = "WISCA",
subtitle = "Susceptibility vs. incidence weight",
x = translate_AMR("Susceptibility", language = get_AMR_locale()),
y = translate_AMR("Incidence weight (normalised)", language = get_AMR_locale()),
colour = translate_AMR("Pathogen", language = get_AMR_locale()),
caption = paste(attributes(wisca_model)$simulations, "Monte Carlo simulations")
) +
ggplot2::guides(
colour = ggplot2::guide_legend(
override.aes = list(alpha = 1, size = 3)
)
) +
ggplot2::theme(
legend.position = "right",
legend.key.spacing.y = ggplot2::unit(0.25, "lines"),
legend.text = ggplot2::element_text(face = "italic"),
plot.title = ggplot2::element_text(size = 12, face = "bold")
)
}
#' @method knit_print antibiogram

9
R/custom_microorganisms.R
View File

@@ -195,11 +195,13 @@ add_custom_microorganisms <- function(x) {
if (!"fullname" %in% colnames(x)) {
x$fullname <- trimws2(paste(x$genus, x$species, x$subspecies))
}
if (!"domain" %in% colnames(x)) x$domain <- ""
if (!"kingdom" %in% colnames(x)) x$kingdom <- ""
if (!"phylum" %in% colnames(x)) x$phylum <- ""
if (!"class" %in% colnames(x)) x$class <- ""
if (!"order" %in% colnames(x)) x$order <- ""
if (!"family" %in% colnames(x)) x$family <- ""
x$domain[is.na(x$domain)] <- ""
x$kingdom[is.na(x$kingdom)] <- ""
x$phylum[is.na(x$phylum)] <- ""
x$class[is.na(x$class)] <- ""
@@ -217,6 +219,7 @@ add_custom_microorganisms <- function(x) {
# fill in taxonomy based on genus
genus_to_check <- gsub("^(.*)[^a-zA-Z].*", "\\1", x$genus, perl = TRUE)
x$domain[which(x$domain == "" & genus_to_check != "")] <- AMR_env$MO_lookup$domain[match(genus_to_check[which(x$domain == "" & genus_to_check != "")], AMR_env$MO_lookup$genus)]
x$kingdom[which(x$kingdom == "" & genus_to_check != "")] <- AMR_env$MO_lookup$kingdom[match(genus_to_check[which(x$kingdom == "" & genus_to_check != "")], AMR_env$MO_lookup$genus)]
x$phylum[which(x$phylum == "" & genus_to_check != "")] <- AMR_env$MO_lookup$phylum[match(genus_to_check[which(x$phylum == "" & genus_to_check != "")], AMR_env$MO_lookup$genus)]
x$class[which(x$class == "" & genus_to_check != "")] <- AMR_env$MO_lookup$class[match(genus_to_check[which(x$class == "" & genus_to_check != "")], AMR_env$MO_lookup$genus)]
@@ -229,9 +232,9 @@ add_custom_microorganisms <- function(x) {
x$prevalence[is.na(x$prevalence)] <- 1.25
x$status <- "accepted"
x$ref <- paste("Self-added,", format(Sys.Date(), "%Y"))
x$kingdom_index <- AMR_env$MO_lookup$kingdom_index[match(genus_to_check, AMR_env$MO_lookup$genus)]
# complete missing kingdom index, so mo_matching_score() will not return NA
x$kingdom_index[is.na(x$kingdom_index)] <- 1
x$domain_index <- AMR_env$MO_lookup$domain_index[match(genus_to_check, AMR_env$MO_lookup$genus)]
# complete missing domain index, so mo_matching_score() will not return NA
x$domain_index[is.na(x$domain_index)] <- 1
x$fullname_lower <- tolower(x$fullname)
x$full_first <- substr(x$fullname_lower, 1, 1)
x$species_first <- tolower(substr(x$species, 1, 1))

12
R/data.R
View File

@@ -77,13 +77,13 @@
#'
#' Synonyms (i.e. trade names) were derived from the PubChem Compound ID (column `cid`) and are consequently only available where a CID is available.
#' @inheritSection AMR Download Our Reference Data
#' @source
#' @references
#'
#' * `r TAXONOMY_VERSION$ATC_DDD$citation` Accessed from <`r TAXONOMY_VERSION$ATC_DDD$url`> on `r documentation_date(TAXONOMY_VERSION$ATC_DDD$accessed_date)`.
#'
#' * `r TAXONOMY_VERSION$LOINC$citation` Accessed from <`r TAXONOMY_VERSION$LOINC$url`> on `r documentation_date(TAXONOMY_VERSION$LOINC$accessed_date)`.
#'
#' * European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm>
#' * European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <https://ec.europa.eu/health/documents/community-register/html/index_en.htm>
#' @inheritSection WHOCC WHOCC
#' @seealso [microorganisms], [intrinsic_resistant]
#' @examples
@@ -107,10 +107,11 @@
#' - `mo`\cr ID of microorganism as used by this package. ***This is a unique identifier.***
#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. ***This is a unique identifier.***
#' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status, documentation = TRUE)`
#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is equal to their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy.
#' - `domain`, `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is used for their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy. For all species outside the domains of Bacteria and Archaea, the `domain` and `kingdom` are identical.
#' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"`
#' - `ref`\cr Author(s) and year of related scientific publication. This contains only the *first surname* and year of the *latest* authors, e.g. "Wallis *et al.* 2006 *emend.* Smith and Jones 2018" becomes "Smith *et al.*, 2018". This field is directly retrieved from the source specified in the column `source`. Moreover, accents were removed to comply with CRAN that only allows ASCII characters.
#' - `ref`\cr Abbreviated authority citation for the nomenclatural act that established the current name combination, following ICNP conventions. For species described in their current genus (*sp. nov.*), this is the original description author(s) and year. For species transferred to a different genus (*comb. nov.*), this is the reclassification author(s) and year. Emendations are excluded. For synonyms, this is the authority under which the synonym was originally published. This field is directly retrieved from the source specified in the column `source`. Diacritics were removed to comply with CRAN, that only allows ASCII characters.
#' - `oxygen_tolerance` \cr Oxygen tolerance, either `r vector_or(microorganisms$oxygen_tolerance, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently `r round(length(microorganisms$oxygen_tolerance[which(!is.na(microorganisms$oxygen_tolerance))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance.
#' - `morphology` \cr Morphology (cell shape), either `r vector_or(microorganisms$morphology, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Genera that are clinically established as coccobacilli (the HACEK group and beyond, such as *Haemophilus* and *Acinetobacter*) are classified as such regardless of BacDive majority vote. Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus. Currently `r round(length(microorganisms$morphology[which(!is.na(microorganisms$morphology))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain a morphology.
#' - `source`\cr Either `r vector_or(microorganisms$source, documentation = TRUE)` (see *Source*)
#' - `lpsn`\cr Identifier ('Record number') of `r TAXONOMY_VERSION$LPSN$name`. This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$lpsn)))` records.
#' - `lpsn_parent`\cr LPSN identifier of the parent taxon
@@ -150,7 +151,7 @@
#'
#' The syntax used to transform the original data to a cleansed \R format, can be [found here](https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R).
#' @inheritSection AMR Download Our Reference Data
#' @source
#' @references
#' Taxonomic entries were imported in this order of importance:
#' 1. `r TAXONOMY_VERSION$LPSN$name`:\cr\cr
#' `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
@@ -339,6 +340,7 @@
#' This data set is internally used by:
#' * [not_intrinsic_resistant()] (an [antimicrobial selector][antimicrobial_selectors])
#' * [mo_is_intrinsic_resistant()]
#' * [wisca()] to model \eqn{\beta(1, 9999)} for resistant bug-drug combinations, per \doi{10.1093/jac/dkv397}
#' @inheritSection AMR Download Our Reference Data
#' @examples
#' intrinsic_resistant

45
R/disk.R
View File

@@ -83,34 +83,31 @@ as.disk <- function(x, na.rm = FALSE) {
na_before <- length(x[is.na(x)])
# heavily based on cleaner::clean_double():
clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) {
# extract a plausible numeric disk zone value from character input
extract_disk_value <- function(x) {
x <- as.character(x)
# normalise decimal separators
x <- gsub(",", ".", x, fixed = TRUE)
# remove ending dot/comma
x <- gsub("[,.]$", "", x)
# only keep last dot/comma
reverse <- function(x) vapply(FUN.VALUE = character(1), lapply(strsplit(x, NULL), rev), paste, collapse = "")
x <- sub("{{dot}}", ".",
gsub(".", "",
reverse(sub(".", "}}tod{{",
reverse(x),
fixed = TRUE
)),
fixed = TRUE
),
fixed = TRUE
)
x_clean <- gsub(remove, "", x, ignore.case = TRUE, fixed = fixed)
# remove everything that is not a number or dot
as.double(gsub("[^0-9.]+", "", x_clean))
# strip known context: leading/trailing whitespace, SIR interpretations,
# comparison operators, semicolons, and surrounding whitespace
x <- trimws(x)
# remove trailing SIR interpretation (e.g., "42; S", "28 R")
x <- gsub("[;[:space:]]+[SIRsir]$", "", x)
# remove leading comparison operators (e.g., ">=20", "<=6")
x <- gsub("^[<>=]+\\s*", "", x)
x <- trimws(x)
# now the remainder must be a plausible standalone number
out <- rep(NA_real_, length(x))
is_numeric <- grepl("^[0-9]+\\.?[0-9]*$", x)
out[is_numeric] <- as.double(x[is_numeric])
out
}
# round up and make it an integer
x <- as.integer(ceiling(clean_double2(x)))
# round up and coerce to integer
x <- as.integer(ceiling(extract_disk_value(x)))
# valid disk diffusion zones: 0-50 mm
x[x < 0 | x > 50] <- NA_integer_
# disks can never be less than 0 mm or more than 50 mm
x[x < 0 | x > 99] <- NA_integer_
x[x > 50] <- 50L
na_after <- length(x[is.na(x)])
if (na_before != na_after) {

2
R/first_isolate.R
View File

@@ -134,7 +134,7 @@
#' @seealso [key_antimicrobials()]
#' @export
#' @return A [logical] vector
#' @source Methodology of these functions is strictly based on:
#' @references Methodology of these functions is strictly based on:
#'
#' - **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#'

25
R/interpretive_rules.R
View File

@@ -107,7 +107,7 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' @rdname interpretive_rules
#' @export
#' @return The input of `x`, possibly with edited values of antimicrobials. Or, if `verbose = TRUE`, a [data.frame] with all original and new values of the affected bug-drug combinations.
#' @source
#' @references
#' - EUCAST Expert Rules. Version 2.0, 2012.\cr
#' Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60; \doi{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
#' - EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf)
@@ -485,14 +485,13 @@ interpretive_rules <- function(x,
if (any(c("all", "other") %in% rules)) {
if (isTRUE(info)) {
cat(paste0("\n", font_grey(strrep("-", 0.95 * getOption("width", 100))), "\n"))
cat(word_wrap(
paste0(
"Rules by the ",
font_bold(paste0("AMR package v", utils::packageDescription("AMR")$Version)),
" (", format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y"),
"), see {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}\n"
)
))
message_(
"Rules by the ",
font_bold(paste0("AMR package v", utils::packageDescription("AMR")$Version)),
" (", format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y"),
"), see {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}",
as_note = FALSE
)
cat("\n\n")
}
ab_enzyme <- subset(AMR::antimicrobials, name %like% "/")[, c("ab", "name"), drop = FALSE]
@@ -523,10 +522,11 @@ interpretive_rules <- function(x,
tolower(ab_enzyme$enzyme_name[i]), " ({.field ", font_bold(col_enzyme), "}) = R"
)
if (isTRUE(info)) {
cat(word_wrap(rule_current,
width = getOption("width") - 30,
message_(rule_current,
as_note = FALSE,
appendLF = FALSE,
extra_indent = 6
))
)
}
run_changes <- edit_sir(
x = x,
@@ -625,6 +625,7 @@ interpretive_rules <- function(x,
} else if (!is.null(list(...)$eucast_rules_df)) {
# deprecated parameter name kept for backward compatibility
interpretive_rules_df_total <- list(...)$eucast_rules_df
warning("Used interpretive_rules(x, eucast_rules_df = ...) - Do use newer argument interpretive_rules_df now.")
} else {
# internal data file, created in data-raw/_pre_commit_checks.R
interpretive_rules_df_total <- INTERPRETIVE_RULES_DF

8
R/key_antimicrobials.R
View File

@@ -161,11 +161,11 @@ key_antimicrobials <- function(x = NULL,
if (is.null(col_mo)) {
warning_("in {.fun key_antimicrobials}: no column found for {.arg col_mo}, ignoring antibiotics set in {.arg gram_negative} and {.arg gram_positive}, and antimycotics set in {.arg antifungal}")
gramstain <- NA_character_
kingdom <- NA_character_
domain <- NA_character_
} else {
x.mo <- as.mo(x[, col_mo, drop = TRUE])
gramstain <- mo_gramstain(x.mo, language = NULL)
kingdom <- mo_kingdom(x.mo, language = NULL)
domain <- mo_domain(x.mo, language = NULL)
}
AMR_string <- function(x, values, name, filter, cols = cols) {
@@ -219,11 +219,11 @@ key_antimicrobials <- function(x = NULL,
cols = cols
)
key_ab[which(kingdom == "Fungi")] <- AMR_string(
key_ab[which(domain == "Fungi")] <- AMR_string(
x = x,
values = antifungal,
name = "antifungal",
filter = kingdom == "Fungi",
filter = domain == "Fungi",
cols = cols
)

77
R/mo.R
View File

@@ -29,7 +29,7 @@
#' Transform Arbitrary Input to Valid Microbial Taxonomy
#'
#' Use this function to get a valid microorganism code ([`mo`]) based on arbitrary user input. Determination is done using intelligent rules and the complete taxonomic tree of the kingdoms `r vector_and(unique(microorganisms$kingdom[which(!grepl("(unknown|Fungi)", microorganisms$kingdom))]), quotes = FALSE)`, and most microbial species from the kingdom Fungi (see *Source*). The input can be almost anything: a full name (like `"Staphylococcus aureus"`), an abbreviated name (such as `"S. aureus"`), an abbreviation known in the field (such as `"MRSA"`), or just a genus. See *Examples*.
#' Use this function to get a valid microorganism code ([`mo`]) based on arbitrary user input. Determination is done using intelligent rules and the complete taxonomic tree of the domains `r vector_and(unique(microorganisms$domain[which(!grepl("(unknown|Fungi)", microorganisms$domain))]), quotes = FALSE)`, and most microbial species from the domain Fungi (see *Source*). The input can be almost anything: a full name (like `"Staphylococcus aureus"`), an abbreviated name (such as `"S. aureus"`), an abbreviation known in the field (such as `"MRSA"`), or just a genus. See *Examples*.
#' @param x A [character] vector or a [data.frame] with one or two columns.
#' @param Becker A [logical] to indicate whether staphylococci should be categorised into coagulase-negative staphylococci ("CoNS") and coagulase-positive staphylococci ("CoPS") instead of their own species, according to Karsten Becker *et al.* (see *Source*). Please see *Details* for a full list of staphylococcal species that will be converted.
#'
@@ -37,14 +37,14 @@
#' @param Lancefield A [logical] to indicate whether a beta-haemolytic *Streptococcus* should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (see *Source*). These streptococci will be categorised in their first group, e.g. *Streptococcus dysgalactiae* will be group C, although officially it was also categorised into groups G and L. . Please see *Details* for a full list of streptococcal species that will be converted.
#'
#' This excludes enterococci at default (who are in group D), use `Lancefield = "all"` to also categorise all enterococci as group D.
#' @param minimum_matching_score A numeric value to set as the lower limit for the [MO matching score][mo_matching_score()]. When left blank, this will be determined automatically based on the character length of `x`, its [taxonomic kingdom][microorganisms] and [human pathogenicity][mo_matching_score()].
#' @param minimum_matching_score A numeric value to set as the lower limit for the [MO matching score][mo_matching_score()]. When left blank, this will be determined automatically based on the character length of `x`, its [taxonomic domain][microorganisms] and [human pathogenicity][mo_matching_score()].
#' @param keep_synonyms A [logical] to indicate if outdated, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. Do note that the term "synonym" is in this case jargon from the field of microbial taxonomy - it is not in place to denote that e.g. "Streptococcus Group A" is a synonym of *S. pyogenes*. Though this is practically the case, taxonomically it is not as "Streptococcus Group A" is not even a valid taxonomic name.
#'
#' The default is `FALSE`, which will return a note if outdated taxonomic names were processed. The default can be set with the package option [`AMR_keep_synonyms`][AMR-options], i.e. `options(AMR_keep_synonyms = TRUE)` or `options(AMR_keep_synonyms = FALSE)`.
#' @param reference_df A [data.frame] to be used for extra reference when translating `x` to a valid [`mo`]. See [set_mo_source()] and [get_mo_source()] to automate the usage of your own codes (e.g. used in your analysis or organisation).
#' @param ignore_pattern A Perl-compatible [regular expression][base::regex] (case-insensitive) of which all matches in `x` must return `NA`. This can be convenient to exclude known non-relevant input and can also be set with the package option [`AMR_ignore_pattern`][AMR-options], e.g. `options(AMR_ignore_pattern = "(not reported|contaminated flora)")`.
#' @param cleaning_regex A Perl-compatible [regular expression][base::regex] (case-insensitive) to clean the input of `x`. Every matched part in `x` will be removed. At default, this is the outcome of [mo_cleaning_regex()], which removes texts between brackets and texts such as "species" and "serovar". The default can be set with the package option [`AMR_cleaning_regex`][AMR-options].
#' @param only_fungi A [logical] to indicate if only fungi must be found, making sure that e.g. misspellings always return records from the kingdom of Fungi. This can be set globally for [all microorganism functions][mo_property()] with the package option [`AMR_only_fungi`][AMR-options], i.e. `options(AMR_only_fungi = TRUE)`.
#' @param only_fungi A [logical] to indicate if only fungi must be found, making sure that e.g. misspellings always return records from the domain of Fungi. This can be set globally for [all microorganism functions][mo_property()] with the package option [`AMR_only_fungi`][AMR-options], i.e. `options(AMR_only_fungi = TRUE)`.
#' @param language Language to translate text like "no growth", which defaults to the system language (see [get_AMR_locale()]).
#' @param info A [logical] to indicate that info must be printed, e.g. a progress bar when more than 25 items are to be coerced, or a list with outdated taxonomic names. The default is `TRUE` only in interactive mode.
#' @param ... Other arguments passed on to functions.
@@ -64,7 +64,7 @@
#' | | | \---> subspecies, a 3-5 letter acronym
#' | | \----> species, a 3-6 letter acronym
#' | \----> genus, a 4-8 letter acronym
#' \----> kingdom: A (Archaea), AN (Animalia), B (Bacteria),
#' \----> domain: A (Archaea), AN (Animalia), B (Bacteria),
#' C (Chromista), F (Fungi), PL (Plantae),
#' P (Protozoa)
#' ```
@@ -77,7 +77,7 @@
#'
#' ### Coping with Uncertain Results
#'
#' Results of non-exact taxonomic input are based on their [matching score][mo_matching_score()]. The lowest allowed score can be set with the `minimum_matching_score` argument. At default this will be determined based on the character length of the input, the [taxonomic kingdom][microorganisms], and the [human pathogenicity][mo_matching_score()] of the taxonomic outcome. If values are matched with uncertainty, a message will be shown to suggest the user to inspect the results with [mo_uncertainties()], which returns a [data.frame] with all specifications.
#' Results of non-exact taxonomic input are based on their [matching score][mo_matching_score()]. The lowest allowed score can be set with the `minimum_matching_score` argument. At default this will be determined based on the character length of the input, the [taxonomic domain][microorganisms], and the [human pathogenicity][mo_matching_score()] of the taxonomic outcome. If values are matched with uncertainty, a message will be shown to suggest the user to inspect the results with [mo_uncertainties()], which returns a [data.frame] with all specifications.
#'
#' To increase the quality of matching, the `cleaning_regex` argument is used to clean the input. This must be a [regular expression][base::regex] that matches parts of the input that should be removed before the input is matched against the [available microbial taxonomy][microorganisms]. It will be matched Perl-compatible and case-insensitive. The default value of `cleaning_regex` is the outcome of the helper function [mo_cleaning_regex()].
#'
@@ -241,7 +241,7 @@ as.mo <- function(x,
out[is.na(out) & toupper(x) %in% AMR_env$MO_lookup$mo] <- toupper(x[is.na(out) & toupper(x) %in% AMR_env$MO_lookup$mo])
# From full name ----
out[is.na(out) & x_lower %in% AMR_env$MO_lookup$fullname_lower] <- AMR_env$MO_lookup$mo[match(x_lower[is.na(out) & x_lower %in% AMR_env$MO_lookup$fullname_lower], AMR_env$MO_lookup$fullname_lower)]
# one exception: "Fungi" matches the kingdom, but instead it should return the 'unknown' code for fungi
# one exception: "Fungi" matches the domain, but instead it should return the 'unknown' code for fungi
out[out == "F_[KNG]_FUNGI"] <- "F_FUNGUS"
# From known codes ----
ind <- is.na(out) & toupper(x) %in% AMR::microorganisms.codes$code
@@ -300,7 +300,7 @@ as.mo <- function(x,
MO_lookup_current <- AMR_env$MO_lookup
if (isTRUE(only_fungi)) {
MO_lookup_current <- MO_lookup_current[MO_lookup_current$kingdom == "Fungi", , drop = FALSE]
MO_lookup_current <- MO_lookup_current[MO_lookup_current$domain == "Fungi", , drop = FALSE]
}
# run it
@@ -322,6 +322,15 @@ as.mo <- function(x,
return(as.character(MO_lookup_current$mo[match(x_out, MO_lookup_current$fullname_lower)]))
}
# Issue #287: "X complex" is not a distinct taxon - strip " complex" and try "X"
if (grepl(" complex$", x_out, ignore.case = FALSE)) {
x_out <- sub(" complex$", "", x_out)
x_search_cleaned <- sub(" [Cc]omplex$", "", x_search_cleaned)
if (x_out %in% MO_lookup_current$fullname_lower) {
return(as.character(MO_lookup_current$mo[match(x_out, MO_lookup_current$fullname_lower)]))
}
}
# input must not be too short
if (nchar(x_out) < 3) {
return("UNKNOWN")
@@ -343,6 +352,18 @@ as.mo <- function(x,
(MO_lookup_current$species_first == substr(x_parts[2], 1, 1) |
MO_lookup_current$subspecies_first == substr(x_parts[2], 1, 1) |
MO_lookup_current$subspecies_first == substr(x_parts[3], 1, 1)))
# Issue #288: if the species (and subspecies) word(s) in the input exactly match
# exactly one candidate, use only that candidate and bypass the 0.55 cutoff.
# This prevents prevalent bacteria from outranking a rarer organism whose species
# epithet is an unambiguous exact match, e.g. "S. apiospermum" → Scedosporium.
sp_exact <- tolower(MO_lookup_current$species[filtr]) == x_parts[2]
if (length(x_parts) == 3) {
sp_exact <- sp_exact & tolower(MO_lookup_current$subspecies[filtr]) == x_parts[3]
}
if (sum(sp_exact) == 1) {
filtr <- filtr[sp_exact]
minimum_matching_score <- 0
}
} else {
filtr <- which(MO_lookup_current$full_first == substr(x_parts[1], 1, 1) |
MO_lookup_current$species_first == substr(x_parts[2], 1, 1) |
@@ -385,8 +406,8 @@ as.mo <- function(x,
minimum_matching_score_current <- min(0.6, min(10, nchar(x_search_cleaned)) * 0.08)
# correct back for prevalence
minimum_matching_score_current <- minimum_matching_score_current / MO_lookup_current$prevalence[match(mo_to_search, MO_lookup_current$fullname)]
# correct back for kingdom
minimum_matching_score_current <- minimum_matching_score_current / MO_lookup_current$kingdom_index[match(mo_to_search, MO_lookup_current$fullname)]
# correct back for domain
minimum_matching_score_current <- minimum_matching_score_current / MO_lookup_current$domain_index[match(mo_to_search, MO_lookup_current$fullname)]
minimum_matching_score_current <- pmax(minimum_matching_score_current, m)
if (length(x_parts) > 1 && all(m <= 0.55, na.rm = TRUE)) {
# if the highest score is 0.5, we have nothing serious - 0.5 is the lowest for pathogenic group 1
@@ -647,7 +668,7 @@ NA_mo_ <- set_clean_class(NA_character_,
pillar_shaft.mo <- function(x, ...) {
add_MO_lookup_to_AMR_env()
out <- trimws(format(x))
# grey out the kingdom (part until first "_")
# grey out the domain (part until first "_")
out[!is.na(x)] <- gsub("^([A-Z]+_)(.*)", paste0(pillar::style_subtle("\\1"), "\\2"), out[!is.na(x)], perl = TRUE)
# and grey out every _
out[!is.na(x)] <- gsub("_", pillar::style_subtle("_"), out[!is.na(x)])
@@ -673,9 +694,7 @@ pillar_shaft.mo <- function(x, ...) {
(!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) {
# markup old mo codes
out[!x %in% all_mos] <- font_italic(
pillar::style_na(x[!x %in% all_mos],
collapse = NULL
),
pillar::style_na(x[!x %in% all_mos]),
collapse = NULL
)
# throw a warning with the affected column name(s)
@@ -685,7 +704,7 @@ pillar_shaft.mo <- function(x, ...) {
col <- "The data"
}
warning_(
col, " contains old MO codes (from a previous AMR package version). ",
col, " contains old MO codes (from another AMR package version). ",
"Please update your MO codes with {.help [{.fun as.mo}](AMR::as.mo)}.",
call = FALSE
)
@@ -1002,17 +1021,19 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
message_(out2, as_note = FALSE)
}
other_matches <- paste0(
"Also matched: ",
vector_and(
paste0(
candidates_formatted,
font_blue(paste0(" (", scores_formatted, ")"), collapse = NULL)
),
quotes = FALSE, sort = FALSE
if (x[i, ]$candidates != "") {
other_matches <- paste0(
"Also matched: ",
vector_and(
paste0(
candidates_formatted,
font_blue(paste0(" (", scores_formatted, ")"), collapse = NULL)
),
quotes = FALSE, sort = FALSE
)
)
)
message_(other_matches, as_note = FALSE)
message_(other_matches, as_note = FALSE)
}
}
if (isTRUE(any_maxed_out)) {
@@ -1228,13 +1249,13 @@ replace_old_mo_codes <- function(x, property) {
solved_unique <- unlist(lapply(
strsplit(affected_unique, ""),
function(m) {
kingdom <- paste0("^", m[1])
domain <- paste0("^", m[1])
name <- m[3:length(m)]
name[name == "_"] <- " "
name <- tolower(paste0(name, ".*", collapse = ""))
name <- gsub(" .*", " ", name, fixed = TRUE)
name <- paste0("^", name)
results <- AMR_env$MO_lookup$mo[AMR_env$MO_lookup$kingdom %like_case% kingdom &
results <- AMR_env$MO_lookup$mo[AMR_env$MO_lookup$domain %like_case% domain &
AMR_env$MO_lookup$fullname_lower %like_case% name]
if (length(results) > 1) {
all_direct_matches <<- FALSE
@@ -1258,14 +1279,14 @@ replace_old_mo_codes <- function(x, property) {
warning_(
"in {.help [{.fun mo_", property, "}](AMR::mo_", property, ")}: the input contained ", n_matched,
" old MO code", ifelse(n_matched == 1, "", "s"),
" (", n_unique, "from a previous AMR package version). ",
" (", n_unique, "from another AMR package version). ",
"Please update your MO codes with {.help [{.fun as.mo}](AMR::as.mo)} to increase speed."
)
} else {
warning_(
"in {.help [{.fun as.mo}](AMR::as.mo)}: the input contained ", n_matched,
" old MO code", ifelse(n_matched == 1, "", "s"),
" (", n_unique, "from a previous AMR package version). ",
" (", n_unique, "from another AMR package version). ",
n_solved, " old MO code", ifelse(n_solved == 1, "", "s"),
ifelse(n_solved == 1, " was", " were"),
ifelse(all_direct_matches, " updated ", font_bold(" guessed ")),

6
R/mo_matching_score.R
View File

@@ -47,7 +47,7 @@
#' * \eqn{l_n} is the length of \eqn{n};
#' * \eqn{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance) (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
#' * \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
#' * \eqn{k_n} is the taxonomic domain ('kingdom' until taxonomic reclassification of 2024) of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
#'
#' The grouping into human pathogenic prevalence \eqn{p} is based on recent work from Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
#'
@@ -122,8 +122,8 @@ mo_matching_score <- function(x, n) {
# human pathogenic prevalence (1 to 3), see ?as.mo
p_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "prevalence", drop = TRUE]
# kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
k_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "kingdom_index", drop = TRUE]
# domain index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
k_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "domain_index", drop = TRUE]
# matching score:
(l_n - 0.5 * l_n.lev) / (l_n * p_n * k_n)

87
R/mo_property.R
View File

@@ -42,21 +42,23 @@
#' - `mo_ref("Enterobacter aerogenes")` will return `"Tindall et al., 2017"` (with a note about the renaming)
#' - `mo_ref("Enterobacter aerogenes", keep_synonyms = TRUE)` will return `"Hormaeche et al., 1960"` (with a once-per-session warning that the name is outdated)
#'
#' The short name ([mo_shortname()]) returns the first character of the genus and the full species, such as `"E. coli"`, for species and subspecies. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will be considered *Escherichia coli*. As a result, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
#' [mo_ref()] returns the abbreviated authority of the nomenclatural act that created the queried name combination. When `keep_synonyms = FALSE` (default), this is the authority of the currently accepted name. When `keep_synonyms = TRUE`, this is the authority under which the queried (possibly outdated) name was published. Emendations (changes to the species description without a name change) are not reflected; only the combination or original description authority is returned.
#'
#' Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions [mo_kingdom()] and [mo_domain()] return the exact same results.
#' The short name ([mo_shortname()]) returns the first character of the genus and the full species, such as `"E. coli"`, for species and subspecies. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will always be considered *Escherichia coli*. As a result, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
#'
#' Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G"{o}ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), [mo_kingdom()] and [mo_domain()] return different results for bacteria and archaea: [mo_kingdom()] returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while [mo_domain()] returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results.
#'
#' Determination of human pathogenicity ([mo_pathogenicity()]) is strongly based on Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}). This function returns a [factor] with the levels *Pathogenic*, *Potentially pathogenic*, *Non-pathogenic*, and *Unknown*.
#'
#' Determination of the Gram stain ([mo_gramstain()]) will be based on the taxonomic kingdom and phylum. Originally, Cavalier-Smith defined the so-called subkingdoms Negibacteria and Posibacteria (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318/)), and only considered these phyla as Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes. These phyla were later renamed to Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota (2021, [PMID 34694987](https://pubmed.ncbi.nlm.nih.gov/34694987/)). Bacteria in these phyla are considered Gram-positive in this `AMR` package, except for members of the class Negativicutes (within phylum Bacillota) which are Gram-negative. All other bacteria are considered Gram-negative. Species outside the kingdom of Bacteria will return a value `NA`. Functions [mo_is_gram_negative()] and [mo_is_gram_positive()] always return `TRUE` or `FALSE` (or `NA` when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
#' Determination of the Gram stain ([mo_gramstain()] is based on the taxonomic kingdom and phylum. Originally, Cavalier-Smith defined the so-called subkingdoms Negibacteria and Posibacteria (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318/)), and only considered these phyla as Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes. These phyla were later renamed to Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota (2021, [PMID 34694987](https://pubmed.ncbi.nlm.nih.gov/34694987/)). Bacteria in these phyla are considered Gram-positive in this `AMR` package, except for members of the class Negativicutes (within phylum Bacillota) which are Gram-negative. All other bacteria are considered Gram-negative. Species outside the kingdom of Bacteria will return a value `NA`. Functions [mo_is_gram_negative()] and [mo_is_gram_positive()] always return `TRUE` or `FALSE` (or `NA` when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
#'
#' Determination of yeasts ([mo_is_yeast()]) will be based on the taxonomic kingdom and class. *Budding yeasts* are yeasts that reproduce asexually through a process called budding, where a new cell develops from a small protrusion on the parent cell. Taxonomically, these are members of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) or Pichiomycetes. *True yeasts* quite specifically refers to yeasts in the underlying order Saccharomycetales (such as *Saccharomyces cerevisiae*). Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes or Pichiomycetes, the function will return `TRUE`. It returns `FALSE` otherwise (or `NA` when the input is `NA` or the MO code is `UNKNOWN`).
#' Determination of yeasts ([mo_is_yeast()]) is based on the taxonomic kingdom and class. *Budding yeasts* are yeasts that reproduce asexually through a process called budding, where a new cell develops from a small protrusion on the parent cell. Taxonomically, these are members of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) or Pichiomycetes. *True yeasts* quite specifically refers to yeasts in the underlying order Saccharomycetales (such as *Saccharomyces cerevisiae*). Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes or Pichiomycetes, the function will return `TRUE`. It returns `FALSE` otherwise (or `NA` when the input is `NA` or the MO code is `UNKNOWN`).
#'
#' Determination of intrinsic resistance ([mo_is_intrinsic_resistant()]) will be based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))`. The [mo_is_intrinsic_resistant()] function can be vectorised over both argument `x` (input for microorganisms) and `ab` (input for antimicrobials).
#' Determination of intrinsic resistance ([mo_is_intrinsic_resistant()]) is based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))`. The [mo_is_intrinsic_resistant()] function can be vectorised over both argument `x` (input for microorganisms) and `ab` (input for antimicrobials).
#'
#' Determination of bacterial oxygen tolerance ([mo_oxygen_tolerance()]) will be based on BacDive, see *Source*. The function [mo_is_anaerobic()] only returns `TRUE` if the oxygen tolerance is `"anaerobe"`, indicting an obligate anaerobic species or genus. It always returns `FALSE` for species outside the taxonomic kingdom of Bacteria.
#' Determination of both bacterial oxygen tolerance ([mo_oxygen_tolerance()]) and morphology ([mo_morphology()]) are based on BacDive, see *Source*. The function [mo_is_anaerobic()] only returns `TRUE` if the oxygen tolerance is `"anaerobe"`, indicating an obligate anaerobic species or genus. It always returns `FALSE` for species outside the taxonomic kingdom of Bacteria.
#'
#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. [This MycoBank URL](`r TAXONOMY_VERSION$MycoBank$url`) will be used for fungi wherever available , [this LPSN URL](`r TAXONOMY_VERSION$MycoBank$url`) for bacteria wherever available, and [this GBIF link](`r TAXONOMY_VERSION$GBIF$url`) otherwise.
#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. [This MycoBank URL](`r TAXONOMY_VERSION$MycoBank$url`) is used for fungi wherever available , [this LPSN URL](`r TAXONOMY_VERSION$MycoBank$url`) for bacteria wherever available, and [this GBIF link](`r TAXONOMY_VERSION$GBIF$url`) otherwise.
#'
#' SNOMED codes ([mo_snomed()]) was last updated on `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`. See *Source* and the [microorganisms] data set for more info.
#'
@@ -100,8 +102,10 @@
#'
#' # other properties ---------------------------------------------------------
#'
#' mo_pathogenicity("Klebsiella pneumoniae")
#' mo_morphology("Klebsiella pneumoniae")
#' mo_gramstain("Klebsiella pneumoniae")
#' mo_gramstain("Klebsiella pneumoniae", add_morphology = TRUE)
#' mo_pathogenicity("Klebsiella pneumoniae")
#' mo_snomed("Klebsiella pneumoniae")
#' mo_type("Klebsiella pneumoniae")
#' mo_rank("Klebsiella pneumoniae")
@@ -249,8 +253,8 @@ mo_shortname <- function(x, language = get_AMR_locale(), keep_synonyms = getOpti
}
# get first char of genus and complete species in English
genera <- mo_genus(x.mo, language = NULL, keep_synonyms = keep_synonyms)
shortnames <- paste0(substr(genera, 1, 1), ". ", replace_empty(mo_species(x.mo, language = NULL, keep_synonyms = keep_synonyms)))
genera <- mo_genus(x.mo, language = NULL, keep_synonyms = keep_synonyms, ...)
shortnames <- paste0(substr(genera, 1, 1), ". ", replace_empty(mo_species(x.mo, language = NULL, keep_synonyms = keep_synonyms, ...)))
# exceptions for where no species is known
shortnames[shortnames %like% ".[.] spp[.]"] <- genera[shortnames %like% ".[.] spp[.]"]
@@ -262,7 +266,7 @@ mo_shortname <- function(x, language = get_AMR_locale(), keep_synonyms = getOpti
# unknown species etc.
shortnames[shortnames %like% "unknown"] <- paste0("(", trimws2(gsub("[^a-zA-Z -]", "", shortnames[shortnames %like% "unknown"], perl = TRUE)), ")")
shortnames[mo_rank(x.mo) %in% c("kingdom", "phylum", "class", "order", "family")] <- mo_name(x.mo[mo_rank(x.mo) %in% c("kingdom", "phylum", "class", "order", "family")], language = NULL, keep_synonyms = keep_synonyms)
shortnames[mo_rank(x.mo, keep_synonyms = TRUE, ...) %in% c("domain", "kingdom", "phylum", "class", "order", "family")] <- mo_name(x.mo[mo_rank(x.mo, keep_synonyms = TRUE, ...) %in% c("domain", "kingdom", "phylum", "class", "order", "family")], language = NULL, keep_synonyms = keep_synonyms, ...)
shortnames[is.na(x.mo)] <- NA_character_
load_mo_uncertainties(metadata)
@@ -379,7 +383,18 @@ mo_kingdom <- function(x, language = get_AMR_locale(), keep_synonyms = getOption
language <- validate_language(language)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
translate_into_language(mo_validate(x = x, property = "kingdom", language = language, keep_synonyms = keep_synonyms, ...), language = language, only_unknown = TRUE)
x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
for (new_kingdom in c("Archaea", "Bacteria")) {
if (any(mo_domain(x.mo) == new_kingdom, na.rm = TRUE) && message_not_thrown_before("mo_kingdom", new_kingdom, entire_session = TRUE)) {
message_(
"Since {.pkg AMR v3.1.0}, {.help [{.fun mo_kingdom}](AMR::mo_kingdom)} returns the taxonomic kingdom as defined by G\u00f6ker and Oren (2024), who formally introduced a new kingdom rank into prokaryotic nomenclature ({.href [DOI: 10.1099/ijsem.0.006242](https://doi.org/10.1099/ijsem.0.006242)}). ",
"{.strong The former kingdom of ", new_kingdom, "} was divided into four new kingdoms under the {.strong new domain of ", new_kingdom, "}. ",
"For the old behaviour, use {.help [{.fun mo_domain}](AMR::mo_domain)}. ",
"This note will be shown once per session."
)
}
}
translate_into_language(mo_validate(x = x.mo, property = "kingdom", language = language, keep_synonyms = keep_synonyms, ...), language = language, only_unknown = TRUE)
}
#' @rdname mo_property
@@ -389,7 +404,11 @@ mo_domain <- function(x, language = get_AMR_locale(), keep_synonyms = getOption(
# this tries to find the data and an 'mo' column
x <- find_mo_col(fn = "mo_domain")
}
mo_kingdom(x = x, language = language, keep_synonyms = keep_synonyms, ...)
meet_criteria(x, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
translate_into_language(mo_validate(x = x, property = "domain", language = language, keep_synonyms = keep_synonyms, ...), language = language, only_unknown = TRUE)
}
#' @rdname mo_property
@@ -404,7 +423,8 @@ mo_type <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
out <- mo_kingdom(x.mo, language = NULL, keep_synonyms = keep_synonyms)
out <- mo_domain(x.mo, language = NULL, keep_synonyms = keep_synonyms)
out <- gsub(" \\{.*\\}", "", out) # strip curly brackets
out[which(mo_is_yeast(x.mo, keep_synonyms = keep_synonyms))] <- "Yeasts"
translate_into_language(out, language = language, only_unknown = FALSE)
}
@@ -440,7 +460,7 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
metadata <- get_mo_uncertainties()
prev <- AMR_env$MO_lookup$prevalence[match(x.mo, AMR_env$MO_lookup$mo)]
kngd <- AMR_env$MO_lookup$kingdom[match(x.mo, AMR_env$MO_lookup$mo)]
kngd <- AMR_env$MO_lookup$domain[match(x.mo, AMR_env$MO_lookup$mo)]
rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
out <- factor(
@@ -460,8 +480,9 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
}
#' @rdname mo_property
#' @param add_morphology a [logical] to indicate whether the morphology (from [mo_morphology()]) should be added to the Gram stain result, e.g. `"Gram-negative rods"` instead of `"Gram-negative"`. The default is `FALSE`.
#' @export
mo_gramstain <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
mo_gramstain <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), add_morphology = FALSE, ...) {
if (missing(x)) {
# this tries to find the data and an 'mo' column
x <- find_mo_col(fn = "mo_gramstain")
@@ -469,13 +490,14 @@ mo_gramstain <- function(x, language = get_AMR_locale(), keep_synonyms = getOpti
meet_criteria(x, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
meet_criteria(add_morphology, allow_class = "logical", has_length = 1)
x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
metadata <- get_mo_uncertainties()
x <- rep(NA_character_, length(x))
# make all bacteria Gram negative
x[mo_kingdom(x.mo, language = NULL, keep_synonyms = keep_synonyms) == "Bacteria"] <- "Gram-negative"
x[mo_domain(x.mo, language = NULL, keep_synonyms = keep_synonyms) == "Bacteria"] <- "Gram-negative"
# overwrite these 4 phyla with Gram-positives
# Source: https://itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=956097 (Cavalier-Smith, 2002)
x[(mo_phylum(x.mo, language = NULL, keep_synonyms = keep_synonyms) %in% c(
@@ -494,6 +516,12 @@ mo_gramstain <- function(x, language = get_AMR_locale(), keep_synonyms = getOpti
# and of course our own ID for Gram-positives
| x.mo %in% c("B_GRAMP", "B_ANAER-POS")] <- "Gram-positive"
if (isTRUE(add_morphology)) {
morphs <- mo_morphology(x.mo, language = NULL)
morphs[is.na(x)] <- ""
x[!is.na(x)] <- paste(x[!is.na(x)], tolower(morphs[!is.na(x)]))
}
load_mo_uncertainties(metadata)
translate_into_language(x, language = language, only_unknown = FALSE)
}
@@ -552,12 +580,12 @@ mo_is_yeast <- function(x, language = get_AMR_locale(), keep_synonyms = getOptio
x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
metadata <- get_mo_uncertainties()
x.kingdom <- mo_kingdom(x.mo, language = NULL, keep_synonyms = keep_synonyms)
x.domain <- mo_domain(x.mo, language = NULL, keep_synonyms = keep_synonyms)
x.class <- mo_class(x.mo, language = NULL, keep_synonyms = keep_synonyms)
load_mo_uncertainties(metadata)
out <- x.mo == "F_YEAST" | (x.kingdom == "Fungi" & x.class %in% c("Saccharomycetes", "Pichiomycetes"))
out <- x.mo == "F_YEAST" | (x.domain == "Fungi" & x.class %in% c("Saccharomycetes", "Pichiomycetes"))
out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
out
}
@@ -634,6 +662,21 @@ mo_is_anaerobic <- function(x, language = get_AMR_locale(), keep_synonyms = getO
out
}
#' @rdname mo_property
#' @export
mo_morphology <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
if (missing(x)) {
# this tries to find the data and an 'mo' column
x <- find_mo_col(fn = "mo_morphology")
}
meet_criteria(x, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
out <- mo_validate(x = x, property = "morphology", language = language, keep_synonyms = keep_synonyms, ...)
gsub("^(\\w)", "\\U\\1", out, perl = TRUE)
}
#' @rdname mo_property
#' @export
mo_snomed <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
@@ -768,6 +811,7 @@ mo_taxonomy <- function(x, language = get_AMR_locale(), keep_synonyms = getOptio
metadata <- get_mo_uncertainties()
out <- list(
domain = mo_domain(x, language = language, keep_synonyms = keep_synonyms),
kingdom = mo_kingdom(x, language = language, keep_synonyms = keep_synonyms),
phylum = mo_phylum(x, language = language, keep_synonyms = keep_synonyms),
class = mo_class(x, language = language, keep_synonyms = keep_synonyms),
@@ -885,6 +929,7 @@ mo_info <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
status = mo_status(y, language = language, keep_synonyms = keep_synonyms),
synonyms = mo_synonyms(y, keep_synonyms = keep_synonyms),
gramstain = mo_gramstain(y, language = language, keep_synonyms = keep_synonyms),
morphology = mo_morphology(y, language = language, keep_synonyms = keep_synonyms),
oxygen_tolerance = mo_oxygen_tolerance(y, language = language, keep_synonyms = keep_synonyms),
url = unname(mo_url(y, open = FALSE, keep_synonyms = keep_synonyms)),
ref = mo_ref(y, keep_synonyms = keep_synonyms),
@@ -978,11 +1023,11 @@ mo_validate <- function(x, property, language, keep_synonyms = keep_synonyms, ..
dots <- list(...)
Becker <- dots$Becker
if (is.null(Becker) || property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
if (is.null(Becker) || property %in% c("domain", "kingdom", "phylum", "class", "order", "family", "genus")) {
Becker <- FALSE
}
Lancefield <- dots$Lancefield
if (is.null(Lancefield) || property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
if (is.null(Lancefield) || property %in% c("domain", "kingdom", "phylum", "class", "order", "family", "genus")) {
Lancefield <- FALSE
}
has_Becker_or_Lancefield <- Becker %in% c(TRUE, "all") || Lancefield %in% c(TRUE, "all")

2
R/proportion.R
View File

@@ -100,7 +100,7 @@
#' ```
#'
#' Using `only_all_tested` has no impact when only using one antibiotic as input.
#' @source **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @references **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @seealso [AMR::count()] to count resistant and susceptible isolates.
#' @return A [double] or, when `as_percent = TRUE`, a [character].
#' @rdname proportion

6
R/sir.R
View File

@@ -175,7 +175,7 @@ VALID_SIR_LEVELS <- c("S", "SDD", "I", "R", "NI", "WT", "NWT", "NS")
#' @aliases sir
#' @export
#' @seealso [as.mic()], [as.disk()], [as.mo()]
#' @source
#' @references
#' For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
#'
#' - **CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data**, `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)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m39/>.
@@ -525,7 +525,7 @@ as.sir.default <- function(x,
} else if (!all(is.na(x)) && !identical(levels(x), VALID_SIR_LEVELS) && !all(x %in% c(VALID_SIR_LEVELS, NA))) {
if (all(x %unlike% "(S|I|R)", na.rm = TRUE) && !all(x %in% c(1, 2, 3, 4, 5), na.rm = TRUE)) {
# check if they are actually MICs or disks
if (all_valid_mics(x)) {
if (all_valid_mics(x) && !(all_valid_disks(x) && identical(x, floor(x)))) {
warning_("in {.help [{.fun as.sir}](AMR::as.sir)}: input values were guessed to be MIC values - preferably transform them with {.help [{.fun as.mic}](AMR::as.mic)} before running {.help [{.fun as.sir}](AMR::as.sir)}.")
return(as.sir(as.mic(x), ...))
} else if (all_valid_disks(x)) {
@@ -1654,7 +1654,7 @@ as_sir_method <- function(method_short,
mo_current_other <- structure("UNKNOWN", class = c("mo", "character"))
# formatted for notes
mo_formatted <- mo_current_name
if (!mo_current_rank %in% c("kingdom", "phylum", "class", "order")) {
if (!mo_current_rank %in% c("domain", "kingdom", "phylum", "class", "order")) {
mo_formatted <- font_italic(mo_formatted, collapse = NULL)
}
ab_formatted <- paste0(

BIN
R/sysdata.rda
View File

Binary file not shown.

2
R/whocc.R
View File

@@ -31,7 +31,7 @@
#'
#' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.
#' @section WHOCC:
#' This package contains **all ~550 antibiotic, antimycotic and antiviral drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <https://atcddd.fhi.no>) and the Pharmaceuticals Community Register of the European Commission (<https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm>).
#' This package contains **all ~550 antibiotic, antimycotic and antiviral drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <https://atcddd.fhi.no>) and the Pharmaceuticals Community Register of the European Commission (<https://ec.europa.eu/health/documents/community-register/html/index_en.htm>).
#'
#' These have become the gold standard for international drug utilisation monitoring and research.
#'

2
README.md
View File

@@ -13,7 +13,7 @@ Overview:
- Peer-reviewed, used in over 175 countries, available in 28 languages
- Generates **antibiograms** - traditional, combined, syndromic, and
even WISCA
- Provides the **full microbiological taxonomy** of ~79 000 distinct
- Provides the **full microbiological taxonomy** of ~97 000 distinct
species and extensive info of ~620 antimicrobial drugs
- Applies **CLSI 2011-2026** and **EUCAST 2011-2026** clinical and
veterinary breakpoints, and ECOFFs, for MIC and disk zone

12
data-raw/_pre_commit_checks.R
View File

@@ -109,11 +109,11 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
which(MO_staph$species %in% c(
"coagulase-negative", "argensis", "arlettae",
"auricularis", "borealis", "caeli", "capitis", "caprae",
"carnosus", "casei", "caseolyticus", "chromogenes", "cohnii", "condimenti",
"carnosus", "casei", "caseorum", "caseolyticus", "chromogenes", "cohnii", "condimenti",
"croceilyticus",
"debuckii", "devriesei", "edaphicus", "epidermidis",
"equorum", "felis", "fleurettii", "gallinarum",
"haemolyticus", "hominis", "jettensis", "kloosii",
"equorum", "felis", "fleurettii", "gallinarum", "halotolerans",
"haemolyticus", "hominis", "hsinchuensis", "jettensis", "kloosii",
"lentus", "lugdunensis", "massiliensis", "microti",
"muscae", "nepalensis", "pasteuri", "petrasii",
"pettenkoferi", "piscifermentans", "pragensis", "pseudoxylosus",
@@ -142,7 +142,8 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
"pseudintermedius", "pseudointermedius",
"schweitzeri", "simiae",
"roterodami",
"singaporensis"
"singaporensis",
"ursi"
) |
# old, now renamed to S. coagulans (but still as synonym in our data of course):
(MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
@@ -280,6 +281,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
"Malbranchea",
"Metagonimus",
"Meyerozyma",
"Microascus",
"Microsporidium",
"Microsporum",
"Millerozyma",
@@ -306,6 +308,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
"Piedraia",
"Pithomyces",
"Pityrosporum",
"Plasmodium",
"Pneumocystis",
"Pseudallescheria",
"Pseudoscopulariopsis",
@@ -323,6 +326,7 @@ pre_commit_lst$MO_RELEVANT_GENERA <- c(
"Sarcoptes",
"Scedosporium",
"Schistosoma",
"Schizophyllum",
"Schizosaccharomyces",
"Scolecobasidium",
"Scopulariopsis",

5
data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints.R
View File

@@ -293,7 +293,7 @@ breakpoints_new <- breakpoints |>
host = ifelse(BREAKPOINT_TYPE == "ECOFF", "ECOFF", tolower(HOST)),
method = TEST_METHOD,
site = SITE_OF_INFECTION,
mo,
mo = as.mo(mo),
rank_index = case_when(
is.na(mo_rank(mo, keep_synonyms = TRUE)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
mo_rank(mo, keep_synonyms = TRUE) %like% "(infra|sub)" ~ 1,
@@ -453,6 +453,9 @@ breakpoints_new$breakpoint_R[breakpoints_new$guideline %like% "EUCAST" & breakpo
breakpoints_new <- breakpoints_new |>
filter(!(guideline %like% "EUCAST (2024|2025|2026)" & ref_tbl == "PK/PD"))
# WHONET still contains generic anaerobic rules for EUCAST >= 2021, but this was ended from v12 (2022) on
breakpoints_new <- breakpoints_new |>
filter(!(guideline %like% "EUCAST (2022|2023|2024|2025|2026)" & ref_tbl %like% "anaerob"))
# WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
# EUCAST 2023 guideline: S <= 8 and R > 8

14
data-raw/_reproduction_scripts/reproduction_of_intrinsic_resistant.R
View File

@@ -35,13 +35,13 @@ for (i in seq_len(nrow(antimicrobials))) {
colnames(int_resis)[ncol(int_resis)] <- antimicrobials$ab[i]
}
int_resis <- eucast_rules(int_resis,
eucast_rules_df = subset(
AMR:::EUCAST_RULES_DF,
is.na(have_these_values) & reference.rule_group == "Expected phenotypes" & reference.version == 1.2
),
overwrite = TRUE,
info = FALSE
int_resis <- interpretive_rules(int_resis,
interpretive_rules_df = subset(
AMR:::INTERPRETIVE_RULES_DF,
is.na(have_these_values) & reference.rule_group == "Expected phenotypes" & reference.version == 1.2
),
overwrite = TRUE,
info = FALSE
)
int_resis2 <- int_resis[, sapply(int_resis, function(x) any(!is.sir(x) | x == "R")), drop = FALSE] %>%

2177
data-raw/_reproduction_scripts/reproduction_of_microorganisms.R
View File

File diff suppressed because it is too large Load Diff

2
data-raw/clin_break.md5
View File

@@ -1 +1 @@
45068afc4cd9770dea329782c1aed045
7bcb6eaf7e2da23ac552acbfd12b3e62

BIN
data-raw/datasets/clinical_breakpoints.dta
View File

Binary file not shown.

BIN
data-raw/datasets/clinical_breakpoints.feather
View File

Binary file not shown.

BIN
data-raw/datasets/clinical_breakpoints.parquet
View File

Binary file not shown.

BIN
data-raw/datasets/clinical_breakpoints.rds
View File

Binary file not shown.

BIN
data-raw/datasets/clinical_breakpoints.sav
View File

Binary file not shown.

961
data-raw/datasets/clinical_breakpoints.txt
View File

File diff suppressed because it is too large Load Diff

BIN
data-raw/datasets/clinical_breakpoints.xlsx
View File

Binary file not shown.

BIN
data-raw/datasets/intrinsic_resistant.dta
View File

Binary file not shown.

BIN
data-raw/datasets/intrinsic_resistant.feather
View File

Binary file not shown.

BIN
data-raw/datasets/intrinsic_resistant.parquet
View File

Binary file not shown.

BIN
data-raw/datasets/intrinsic_resistant.rds
View File

Binary file not shown.

BIN
data-raw/datasets/intrinsic_resistant.sav
View File

Binary file not shown.

34131
data-raw/datasets/intrinsic_resistant.txt
View File

File diff suppressed because it is too large Load Diff

BIN
data-raw/datasets/intrinsic_resistant.xlsx
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.codes.dta
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.codes.feather
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.codes.parquet
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.codes.rds
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.codes.sav
View File

Binary file not shown.

733
data-raw/datasets/microorganisms.codes.txt
View File

File diff suppressed because it is too large Load Diff

BIN
data-raw/datasets/microorganisms.codes.xlsx
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.dta
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.feather
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.groups.dta
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.groups.feather
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.groups.parquet
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.groups.rds
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.groups.sav
View File

Binary file not shown.

20
data-raw/datasets/microorganisms.groups.txt
View File

@@ -298,7 +298,6 @@
"B_HACEK" "B_HMPHL_PSCM" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus piscium"
"B_HACEK" "B_HMPHL_PTTM" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus pittmaniae"
"B_HACEK" "B_ACTNB_PLRP" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus pleuropneumoniae"
"B_HACEK" "B_HMPHL_QNTN" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus quentini"
"B_HACEK" "B_AGGRG_SGNS" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus segnis"
"B_HACEK" "B_HMPHL_SMNL" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus seminalis"
"B_HACEK" "B_HMPHL_SPTR" "Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella (HACEK)" "Haemophilus sputorum"
@@ -319,7 +318,6 @@
"B_KLBSL_PNMN-C" "B_KLBSL_QSPN" "Klebsiella pneumoniae complex" "Klebsiella quasipneumoniae"
"B_KLBSL_PNMN-C" "B_KLBSL_QSPN_QSPN" "Klebsiella pneumoniae complex" "Klebsiella quasipneumoniae quasipneumoniae"
"B_KLBSL_PNMN-C" "B_KLBSL_QSPN_SMLP" "Klebsiella pneumoniae complex" "Klebsiella quasipneumoniae similipneumoniae"
"B_KLBSL_PNMN-C" "B_KLBSL_QSVR" "Klebsiella pneumoniae complex" "Klebsiella quasivariicola"
"B_KLBSL_PNMN-C" "B_KLBSL_VRCL" "Klebsiella pneumoniae complex" "Klebsiella variicola"
"B_KLBSL_PNMN-C" "B_KLBSL_VRCL_TRPC" "Klebsiella pneumoniae complex" "Klebsiella variicola tropica"
"B_KLBSL_PNMN-C" "B_KLBSL_VRCL_LNSS" "Klebsiella pneumoniae complex" "Klebsiella variicola tropicalensis"
@@ -332,22 +330,20 @@
"F_MYRZY_GLLR-C" "F_MYRZY_GLLR" "Meyerozyma guilliermondii complex" "Meyerozyma guilliermondii japonica"
"F_MYRZY_GLLR-C" "F_MYRZY_GLLR" "Meyerozyma guilliermondii complex" "Meyerozyma guilliermondii muhira"
"F_MYRZY_GLLR-C" "F_MYRZY_GLLR" "Meyerozyma guilliermondii complex" "Meyerozyma guilliermondii pseudoguilliermondii"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM" "Mycobacterium avium-intracellulare complex" "Mycobacterium avium"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_AVIM" "Mycobacterium avium-intracellulare complex" "Mycobacterium avium avium"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_PRTB" "Mycobacterium avium-intracellulare complex" "Mycobacterium avium paratuberculosis"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_SLVT" "Mycobacterium avium-intracellulare complex" "Mycobacterium avium silvaticum"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE" "Mycobacterium avium-intracellulare complex" "Mycobacterium intracellulare"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_CHMR" "Mycobacterium avium-intracellulare complex" "Mycobacterium intracellulare chimaera"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_INTR" "Mycobacterium avium-intracellulare complex" "Mycobacterium intracellulare intracellulare"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_CHMR" "Mycobacterium avium-intracellulare complex" "Mycobacterium intracellulare yongonense"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM" "Mycobacterium avium complex" "Mycobacterium avium"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_AVIM" "Mycobacterium avium complex" "Mycobacterium avium avium"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_PRTB" "Mycobacterium avium complex" "Mycobacterium avium paratuberculosis"
"B_MYCBC_AVIM-C" "B_MYCBC_AVIM_SLVT" "Mycobacterium avium complex" "Mycobacterium avium silvaticum"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE" "Mycobacterium avium complex" "Mycobacterium intracellulare"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_CHMR" "Mycobacterium avium complex" "Mycobacterium intracellulare chimaera"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_INTR" "Mycobacterium avium complex" "Mycobacterium intracellulare intracellulare"
"B_MYCBC_AVIM-C" "B_MYCBC_LLRE_CHMR" "Mycobacterium avium complex" "Mycobacterium intracellulare yongonense"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium africanum"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium bovis"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium bovis bovis"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium bovis caprae"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium caprae"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium microti"
"B_MYCBC_TBRC-C" "B_MYCBC_MUNG" "Mycobacterium tuberculosis complex" "Mycobacterium mungi"
"B_MYCBC_TBRC-C" "B_MYCBC_ORYG" "Mycobacterium tuberculosis complex" "Mycobacterium orygis"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium pinnipedii"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium tuberculosis"
"B_MYCBC_TBRC-C" "B_MYCBC_TBRC" "Mycobacterium tuberculosis complex" "Mycobacterium tuberculosis caprae"

BIN
data-raw/datasets/microorganisms.groups.xlsx
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.parquet
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.rds
View File

Binary file not shown.

BIN
data-raw/datasets/microorganisms.sav
View File

Binary file not shown.

175663
data-raw/datasets/microorganisms.txt
View File

File diff suppressed because one or more lines are too long

BIN
data-raw/datasets/microorganisms.xlsx
View File

Binary file not shown.

127
data-raw/interpretive_rules.tsv
View File

@@ -451,7 +451,7 @@ EUCAST Breakpoints 14 Enterococcus genus is Enterococcus AMP R AMP, SAM, AMX, AM
EUCAST Breakpoints 14 Enterococcus genus is Enterococcus NOR-S S CIP, LVX S
EUCAST Breakpoints 14 Enterococcus genus is Enterococcus NOR-S I CIP, LVX I
EUCAST Breakpoints 14 Enterococcus genus is Enterococcus NOR-S R CIP, LVX R
EUCAST Breakpoints 14 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 14 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> sort() |> toString()
EUCAST Breakpoints 14 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S, BLA-S R, R AMP, AMX, PIP R
EUCAST Breakpoints 14 Haemophilus influenzae genus_species is Haemophilus influenzae AMC S SAM S
EUCAST Breakpoints 14 Haemophilus influenzae genus_species is Haemophilus influenzae AMC I SAM I
@@ -543,8 +543,8 @@ EUCAST Breakpoints 14 Streptococcus groups A, B, C, G genus_species one_of Strep
EUCAST Breakpoints 14 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G ERY R AZM, CLR, RXT R
EUCAST Breakpoints 14 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S S DOX, MNO S
EUCAST Breakpoints 14 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S R DOX, MNO R
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP S x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP S x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S CEC I
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S CEC I
EUCAST Breakpoints 14 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S R PEN, PHN R from flowchart: when OXA < 20 or PEN > 0.06
@@ -582,19 +582,19 @@ EUCAST Breakpoints 15 Aerococcus sanguinicola/urinae genus_species is Aerococcus
EUCAST Breakpoints 15 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S S fluoroquinolones S
EUCAST Breakpoints 15 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S I fluoroquinolones I
EUCAST Breakpoints 15 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S R fluoroquinolones R
EUCAST Breakpoints 15 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 15 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 15 Anaerobic bacteria genus is Prevotella AMP S AMX S
EUCAST Breakpoints 15 Anaerobic bacteria genus is Prevotella AMP I AMX I
EUCAST Breakpoints 15 Anaerobic bacteria genus is Prevotella AMP R AMX R
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP S AMX S
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP I AMX I
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP R AMX R
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Clostridium perfringens AMP S AMX S
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Clostridium perfringens AMP I AMX I
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Clostridium perfringens AMP R AMX R
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Cutibacterium acnes AMP S AMX S
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Cutibacterium acnes AMP I AMX I
EUCAST Breakpoints 15 Anaerobic bacteria genus_species is Cutibacterium acnes AMP R AMX R
@@ -643,7 +643,7 @@ EUCAST Breakpoints 15 Enterococcus genus is Enterococcus AMP R AMX, AMC R
EUCAST Breakpoints 15 Enterococcus genus is Enterococcus NOR-S S CIP, LVX S
EUCAST Breakpoints 15 Enterococcus genus is Enterococcus NOR-S I CIP, LVX I
EUCAST Breakpoints 15 Enterococcus genus is Enterococcus NOR-S R CIP, LVX R
EUCAST Breakpoints 15 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 15 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 15 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S, BLA-S R, R AMP, AMX, PIP R
EUCAST Breakpoints 15 Haemophilus influenzae genus_species is Haemophilus influenzae AMC S SAM S
EUCAST Breakpoints 15 Haemophilus influenzae genus_species is Haemophilus influenzae AMC I SAM I
@@ -731,8 +731,8 @@ EUCAST Breakpoints 15 Streptococcus groups A, B, C, G genus_species one_of Strep
EUCAST Breakpoints 15 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G ERY R AZM, CLR, RXT R
EUCAST Breakpoints 15 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S S DOX, MNO S
EUCAST Breakpoints 15 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S R DOX, MNO R
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S CEC I
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S CEC I
EUCAST Breakpoints 15 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S R PEN, PHN R from flowchart: when OXA < 20 or PEN > 0.06
@@ -770,7 +770,7 @@ EUCAST Breakpoints 16 Aerococcus sanguinicola/urinae genus_species is Aerococcus
EUCAST Breakpoints 16 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S S fluoroquinolones S
EUCAST Breakpoints 16 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S I fluoroquinolones I
EUCAST Breakpoints 16 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S R fluoroquinolones R
EUCAST Breakpoints 16 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 16 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 16 Anaerobic bacteria genus is Prevotella AMP S AMX S
EUCAST Breakpoints 16 Anaerobic bacteria genus is Prevotella AMP I AMX I
EUCAST Breakpoints 16 Anaerobic bacteria genus is Prevotella AMP R AMX R
@@ -778,11 +778,11 @@ EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Fusobacterium necropho
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP S AMX S
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP I AMX I
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP R AMX R
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Clostridium perfringens AMP S AMX S
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Clostridium perfringens AMP I AMX I
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Clostridium perfringens AMP R AMX R
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Cutibacterium acnes AMP S AMX S
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Cutibacterium acnes AMP I AMX I
EUCAST Breakpoints 16 Anaerobic bacteria genus_species is Cutibacterium acnes AMP R AMX R
@@ -831,7 +831,7 @@ EUCAST Breakpoints 16 Enterococcus genus is Enterococcus AMP R AMX, AMC R
EUCAST Breakpoints 16 Enterococcus genus is Enterococcus NOR-S S CIP, LVX S
EUCAST Breakpoints 16 Enterococcus genus is Enterococcus NOR-S I CIP, LVX I
EUCAST Breakpoints 16 Enterococcus genus is Enterococcus NOR-S R CIP, LVX R
EUCAST Breakpoints 16 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 16 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S S AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 16 Haemophilus influenzae genus_species is Haemophilus influenzae PEN-S, BLA-S R, R AMP, AMX, PIP R
EUCAST Breakpoints 16 Haemophilus influenzae genus_species is Haemophilus influenzae AMC S SAM S
EUCAST Breakpoints 16 Haemophilus influenzae genus_species is Haemophilus influenzae AMC I SAM I
@@ -920,8 +920,8 @@ EUCAST Breakpoints 16 Streptococcus groups A, B, C, G genus_species one_of Strep
EUCAST Breakpoints 16 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G ERY R AZM, CLR, RXT R
EUCAST Breakpoints 16 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S S DOX, MNO S
EUCAST Breakpoints 16 Streptococcus groups A, B, C, G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G TCY-S R DOX, MNO R
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S S CEC I
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae PEN S CEC I
EUCAST Breakpoints 16 Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA-S R PEN, PHN R from flowchart: when OXA < 20 or PEN > 0.06
@@ -959,19 +959,19 @@ EUCAST Breakpoints 13.1 Aerococcus sanguinicola/urinae genus_species is Aerococc
EUCAST Breakpoints 13.1 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S S fluoroquinolones S
EUCAST Breakpoints 13.1 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S I fluoroquinolones I
EUCAST Breakpoints 13.1 Aerococcus sanguinicola/urinae genus_species is Aerococcus sanguinicola, Aerococcus urinae NOR-S R fluoroquinolones R
EUCAST Breakpoints 13.1 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.1 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 13.1 Anaerobic bacteria genus is Prevotella AMP S AMX S
EUCAST Breakpoints 13.1 Anaerobic bacteria genus is Prevotella AMP I AMX I
EUCAST Breakpoints 13.1 Anaerobic bacteria genus is Prevotella AMP R AMX R
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP S AMX S
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP I AMX I
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP R AMX R
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringensm"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Clostridium perfringens AMP S AMX S
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Clostridium perfringens AMP I AMX I
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Clostridium perfringens AMP R AMX R
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Cutibacterium acnes AMP S AMX S
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Cutibacterium acnes AMP I AMX I
EUCAST Breakpoints 13.1 Anaerobic bacteria genus_species is Cutibacterium acnes AMP R AMX R
@@ -1135,25 +1135,25 @@ EUCAST Breakpoints 13.1 Viridans group streptococci genus_species one_of Viridan
EUCAST Breakpoints 13.1 Viridans group streptococci genus_species one_of Viridans Group Streptococcus (VGS) AMP S AMX, AMC, SAM, PIP, TZP S will be expanded in eucast_rules()
EUCAST Breakpoints 13.1 Viridans group streptococci genus_species one_of Viridans Group Streptococcus (VGS) AMP I AMX, AMC, SAM, PIP, TZP I will be expanded in eucast_rules()
EUCAST Breakpoints 13.1 Viridans group streptococci genus_species one_of Viridans Group Streptococcus (VGS) AMP R AMX, AMC, SAM, PIP, TZP R will be expanded in eucast_rules()
EUCAST Breakpoints Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints Anaerobic bacteria genus is Prevotella AMP S AMX S
EUCAST Breakpoints Anaerobic bacteria genus is Prevotella AMP I AMX I
EUCAST Breakpoints Anaerobic bacteria genus is Prevotella AMP R AMX R
EUCAST Breakpoints Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP S AMX S
EUCAST Breakpoints Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP I AMX I
EUCAST Breakpoints Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP R AMX R
EUCAST Breakpoints Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints Anaerobic bacteria genus_species is Clostridium perfringens AMP S AMX S
EUCAST Breakpoints Anaerobic bacteria genus_species is Clostridium perfringens AMP I AMX I
EUCAST Breakpoints Anaerobic bacteria genus_species is Clostridium perfringens AMP R AMX R
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes AMP S AMX S
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes AMP I AMX I
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes AMP R AMX R
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes CTX S CRO S
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes CTX I CRO I
EUCAST Breakpoints Anaerobic bacteria genus_species is Cutibacterium acnes CTX R CRO R
EUCAST Breakpoints 13.0 Anaerobic bacteria genus is Prevotella PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.0 Anaerobic bacteria genus is Prevotella AMP S AMX S
EUCAST Breakpoints 13.0 Anaerobic bacteria genus is Prevotella AMP I AMX I
EUCAST Breakpoints 13.0 Anaerobic bacteria genus is Prevotella AMP R AMX R
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Fusobacterium necrophorum PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP S AMX S
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP I AMX I
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Fusobacterium necrophorum AMP R AMX R
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Clostridium perfringens PEN S AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Clostridium perfringens AMP S AMX S
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Clostridium perfringens AMP I AMX I
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Clostridium perfringens AMP R AMX R
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes PEN S AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP S x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes AMP S AMX S
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes AMP I AMX I
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes AMP R AMX R
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes CTX S CRO S
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes CTX I CRO I
EUCAST Breakpoints 13.0 Anaerobic bacteria genus_species is Cutibacterium acnes CTX R CRO R
EUCAST Breakpoints Corynebacterium diphtheriae/ulcerans genus_species one_of Corynebacterium diphtheriae, Corynebacterium ulcerans PEN I AMX S
EUCAST Breakpoints Corynebacterium diphtheriae/ulcerans genus_species one_of Corynebacterium diphtheriae, Corynebacterium ulcerans PEN R AMX R
EUCAST Breakpoints Corynebacterium diphtheriae/ulcerans genus_species one_of Corynebacterium diphtheriae, Corynebacterium ulcerans PEN S CTX S
@@ -1414,19 +1414,20 @@ EUCAST Expert Rules 3.2 Table 4: Intrinsic resistance in gram-positive bacteria
EUCAST Expert Rules 3.2 Table 4: Intrinsic resistance in gram-positive bacteria genus is Lactobacillus VAN, TEC R
EUCAST Expert Rules 3.2 Table 4: Intrinsic resistance in gram-positive bacteria fullname like ^Clostridium (ramosum|innocuum) VAN R
EUCAST Expert Rules 3.3 Expert Rules on Campylobacter genus is Campylobacter ERY S CLR, AZM S
EUCAST Expert Rules 3.3 Expert Rules on Campylobacter genus_species is Campylobacter ERY R CLR, AZM R
EUCAST Expert Rules 3.3 Expert Rules on Campylobacter genus is Campylobacter ERY R CLR, AZM R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales genus_species one_of Escherichia coli, Proteus mirabilis AMP R PIP R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales genus_species one_of Escherichia coli, Proteus mirabilis AMP S PIP S
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Klebsiella(?! aerogenes)|Raoultella) PIP R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter freundii|Serratia|Morganella morganii|Hafnia alvei|Providencia) CXM S CXM, cephalosporins_2nd R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Klebsiella(?! aerogenes)|Raoultella) PIP R Regex means that K. aerogenes is not matched.
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CXM S CXM, cephalosporins_2nd R Also includes the C. freundii complex - these Citrobacter species were not literally mentioned in the document, but the complex was.
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales genus one_of Arsenophonus, Biostraticola, Brenneria, Buchnera, Budvicia, Buttiauxella, Cedecea, Citrobacter, Cosenzaea, Cronobacter, Dickeya, Edwardsiella, Enterobacillus, Enterobacter, Erwinia, Escherichia, Ewingella, Franconibacter, Gibbsiella, Hafnia, Izhakiella, Klebsiella, Kluyvera, Kosakonia, Leclercia, Lelliottia, Leminorella, Lonsdalea, Mangrovibacter, Mixta, Moellerella, Morganella, Obesumbacterium, Pantoea, Pectobacterium, Phaseolibacter, Photorhabdus, Phytobacter, Plesiomonas, Pluralibacter, Pragia, Proteus, Providencia, Pseudescherichia, Pseudocitrobacter, Rahnella, Raoultella, Rosenbergiella, Rouxiella, Saccharobacter, Samsonia, Serratia, Shigella, Shimwellia, Siccibacter, Sodalis, Tatumella, Thorsellia, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia, Yokenella CIP R fluoroquinolones R This is Enterobacterales except Salmonella spp.
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Serratia|Providencia|Morganella morganii) TGC R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CTX S CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CRO S CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CAZ S CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CTX I CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CRO I CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia) CAZ I CTX, CRO, CAZ This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales fullname like ^(Serratia|Providencia|Proteus|Morganella morganii) TGC R
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei) CTX S CTX, CRO, CAZ, TZP This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei) CRO S CTX, CRO, CAZ, TZP This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei) CAZ S CTX, CRO, CAZ, TZP This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei) TZP S CTX, CRO, CAZ, TZP This is rule 3 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Serratia|Morganella morganii|Providencia) CTX S CTX, CRO, CAZ This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Serratia|Morganella morganii|Providencia) CRO S CTX, CRO, CAZ This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) fullname like ^(Serratia|Morganella morganii|Providencia) CAZ S CTX, CRO, CAZ This is rule 4 of EUCAST Expert Rules v3.3 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
EUCAST Expert Rules 3.3 Expert Rules on Enterococcus fullname like ^Enterococcus (faecalis|faecium) AMP R ureidopenicillins, IPM R
EUCAST Expert Rules 3.3 Expert Rules on Enterococcus fullname like ^Enterococcus (faecalis|faecium) AMX R ureidopenicillins, IPM R
EUCAST Expert Rules 3.3 Expert Rules on Enterococcus genus is Enterococcus NOR S CIP, LVX S
@@ -1441,30 +1442,34 @@ EUCAST Expert Rules 3.3 Expert Rules on Moraxella catarrhalis genus_species is M
EUCAST Expert Rules 3.3 Expert Rules on Moraxella catarrhalis genus_species is Moraxella catarrhalis NAL R fluoroquinolones R
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella cephalosporins_2nd R
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella aminoglycosides R
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF S CIP S
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF R CIP R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R betalactams R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX S betalactams_with_inhibitor S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species one_of Staphylococcus aureus, Staphylococcus lugdunensis PEN R AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC R all penicillins without beta-lactamse inhibitor
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZOP R Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")]
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX S betalactams_with_inhibitor, carbapenems S Must be S to all betactams with recognised anti-staphylococcal activity
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus PEN R AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC R all penicillins without beta-lactamse inhibitor and excluding isoxazolylpenicillines
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus ERY, CLI S macrolides, lincosamides S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus NOR S CIP, LVX, MFX, OFX S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus NOR S CIP, LVX I
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus NOR S MFX S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus LVX R fluoroquinolones R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus MFX R fluoroquinolones R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus TCY S DOX, MNO, TGC S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus TCY R DOX, MNO R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus VAN S lipoglycopeptides S
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus LNZ S TZD S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus A, B, C and G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G PEN S aminopenicillins, cephalosporins, carbapenems S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus A, B, C and G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G NOR S LVX, MFX S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus A, B, C and G genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G NOR R LVX, MFX R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA S PHN, PEN, aminopenicillins, cephalosporins_except_CAZ, carbapenems S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae NOR S LVX, MFX S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G PEN S aminopenicillins, cephalosporins, carbapenems S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Viridans Group Streptococcus (VGS) PEN S aminopenicillins, CTX, CRO S Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Viridans Group Streptococcus (VGS) PEN R aminopenicillins, CTX, CRO R Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G NOR S LVX I
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G NOR S MFX S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus species genus_species one_of Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G NOR R LVX, MFX R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae OXA S AMC, AMP, AMX, CEC, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IPM, MEM, PEN S x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))]); x[x %in% betalactams()] |> toString()
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae NOR S LVX I
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae NOR S MFX S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae ERY, CLI S, S macrolides, lincosamides S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae NOR R LVX, MFX R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae LVX R fluoroquinolones R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae MFX R fluoroquinolones R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae TCY S DOX, MNO S
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae TCY R DOX, MNO R
EUCAST Expert Rules 3.3 Expert Rules on Streptococcus pneumoniae genus_species is Streptococcus pneumoniae VAN S lipoglycopeptides S
EUCAST Expert Rules 3.3 Expert Rules on Viridans Group Streptococci genus_species one_of Viridans Group Streptococcus (VGS) PEN S aminopenicillins, CTX, CRO S
EUCAST Expert Rules 3.3 Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp. order is Enterobacterales PEN, glycopeptides_except_lipo, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones R
EUCAST Expert Rules 3.3 Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp. fullname like ^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium) aminopenicillins, TIC R
EUCAST Expert Rules 3.3 Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp. fullname like ^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae) aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX R
Can't render this file because it has a wrong number of fields in line 10.

2
data-raw/intrinsicR.md5
View File

@@ -1 +1 @@
990cbdfa55a1c2340aecfa67e8ac84d6
ec377180475ec3cd61ffaed401643e9a

2
data-raw/microorganisms.codes.md5
View File

@@ -1 +1 @@
6ef98bb1bcd27052fde453bb12c0b285
5fba98b9dd8845adc9f83d52b28f8254

2
data-raw/microorganisms.groups.md5
View File

@@ -1 +1 @@
dfdbbebfe1a542270d63b94c12889860
43f0086ac00f84bbda973c6c5e332c49

2
data-raw/microorganisms.md5
View File

@@ -1 +1 @@
6dc4dded108052760bfb626df03435e2
7613ad032fa1078b6c2ad46d7ae8236f

BIN
data-raw/taxonomy0.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy0b.rds Normal file
View File

Binary file not shown.

BIN
data-raw/taxonomy1.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy1b.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy1c.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy2.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy2b.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy2c.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy3.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy3b.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy_gbif.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy_lpsn.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy_lpsn0.rds Normal file
View File

Binary file not shown.

BIN
data-raw/taxonomy_lpsn_missing.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy_mycobank.rds
View File

Binary file not shown.

BIN
data-raw/taxonomy_mycobank0.rds Normal file
View File

Binary file not shown.

622
data-raw/wisca_test/parameters_amr.R Normal file
View File

@@ -0,0 +1,622 @@
# Copyright (c) [2022] [Larisse Bolton]
wisca_params <- function(x, antibiotic_in, pathogen_in, analysis, exclude, isolate_first, susceptible_I, infection_in,infection_full){
# Isolate the type of syndrome you would like to investigate
if (infection_in != ""){
x <- x %>%
filter(infection_type == infection_in)
} else {
x <- x
}
# Define antibiotic regimens under investigation from "antimicrobials" dataset
antibiotic_df <- function(abo_in){
mono_ab <- str_to_title(abo_in[!str_detect(abo_in,fixed("+"))]) #identify single antimicrobials
mono_antibiotic_x_set <- data.frame()
for (j in 1:length(mono_ab)){ #generate dataframe for single antimicrobials with abbreviations and fullnames
mono_x_name <- ab_name(mono_ab[j], only_first = TRUE)
mono_x_ab <- as.ab(mono_x_name)
mono_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% mono_x_ab)[,c("ab","name")]
mono_antibiotic_x_set <- rbind.data.frame(mono_antibiotic_x_set,mono_antibiotic, row.names = NULL)
}
names(mono_antibiotic_x_set) <- c("ab1","namerx")
mono_antibiotic_x_set$ab1 <- as.character(mono_antibiotic_x_set$ab1)
comb_ab <- str_to_title(abo_in[str_detect(abo_in,fixed("+"))]) #identify combination regimens
if(length(comb_ab) > 0){
comb_antibiotic_x_total <- data.frame()
for (jj in 1:length(comb_ab)){ #generate dataframe for combination antimicrobials with abbreviations and fullnames
comb_ab_sep <- unlist(str_split(comb_ab[jj], fixed("+")))
comb_antibiotic_x_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(comb_ab_sep)){
comb_x_name <- ab_name(comb_ab_sep[ii], only_first = TRUE)
comb_x_ab <- as.ab(comb_x_name)
comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% comb_x_ab)[,c("ab","name")]
comb_antibiotic_x_pre[ii,] <- comb_antibiotic
names(comb_antibiotic_x_pre) <- names(comb_antibiotic)
}
comb_antibiotic_x <- pivot_wider(comb_antibiotic_x_pre, names_from = name, values_from = ab)
comb_antibiotic_x_set <- comb_antibiotic_x %>%
mutate(namerx = str_flatten(str_c(names(comb_antibiotic_x), collapse = "+")))
new_ab <- sapply(X = as.character(1:(ncol(comb_antibiotic_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
names(comb_antibiotic_x_set)[1:(ncol(comb_antibiotic_x_set)-1)] <- new_ab
names(comb_antibiotic_x_set)[ncol(comb_antibiotic_x_set)]<- "namerx"
comb_antibiotic_x_total <-rbind.data.frame(comb_antibiotic_x_total, comb_antibiotic_x_set, row.names = NULL)
}
antibiotic_x_set <- full_join(x= mono_antibiotic_x_set,y = comb_antibiotic_x_total) #generate full antibiotic dataset for analysis
antibiotic_x_set <- select(antibiotic_x_set, starts_with("ab"),starts_with("namerx"))
} else {
antibiotic_x_set <- mono_antibiotic_x_set
}
return(antibiotic_x_set)
}
antibiotic_rx <- antibiotic_df(abo_in = antibiotic_in) #full dataframe with antibiotic regimens
#if the dataset names are in different format to built-in set
if ((length(names(x)[sapply(names(x), function(x1){any(str_detect(x1,"[A-Z]$") & !(x1 %in% antimicrobials$ab) & !(x1 %in% antimicrobials$name))})])>0)){
names(x)[which(str_detect(names(x),"[a-z]+") & !str_detect(names(x),"fullname") & !str_detect(names(x),"mo"))] <- str_to_upper(names(x)[which(str_detect(names(x),"[a-z]+") & !str_detect(names(x),"fullname") & !str_detect(names(x),"mo"))])
names(x)[which(str_detect(names(x),"^ORG") & str_detect(names(x),"NAME$"))] <- "fullname"
names(x)[which(str_detect(names(x),"^ORG") & str_detect(names(x),"CODE$"))] <- "organism"
names(x)[which(str_detect(names(x),"^DATE") & str_detect(names(x),"CULTURE$") | str_detect(names(x),"^DATE") & str_detect(names(x),"SPECIMEN"))] <- "date"
names(x)[which(str_detect(names(x),"^EPISODE") | str_detect(names(x),"^PAT"))] <- "patient"
names(x)[which(str_detect(names(x),"^WARD"))] <- "ward"
names(x)[which(str_detect(names(x),"^HOSP"))] <- "hospital"
names(x)[which(str_detect(names(x),"DATE") & (str_detect(names(x),"REGISTRATION")))] <- "date"
}
#remove any duplicated variables
if (any(duplicated(names(x)))){
x <- x[,-max(which(names(x) == names(x)[which(duplicated(names(x)))]))]
} else {
x <- x
}
x <- arrange(x,"date") #arrange in increasing date
#if your dataset does not have an episode identifier
if (length(names(x)[sapply(names(x), function(x1){any(str_detect(x1,"patient"))})]) == 0){
x$patient <- seq(1,nrow(x),1)
}
#find demographic data within dataset
micro_df_ind <- which(sapply(x,function(y) any(str_detect(y,"^S$"))|any(str_detect(y,"^R$"))|any(str_detect(y,"^SENSITIVE$"))| any(str_detect(y,"^RESISTANT$"))) == TRUE)
micro_df <- names(x)[micro_df_ind]#column names for susceptibility data
demograph_df <- names(x)[which(!names(x) %in% micro_df)] #column names for demographic or organism data
#Dataset required to contain both mo and fullname
if ("mo" %in% names(x)){
# If mo included but not fullname:
x <- x %>%
mutate(mo = as.mo(mo), keep_synonyms = TRUE)
test_isolates_pre <- x %>%
mutate(across(.cols = all_of(demograph_df),~ str_to_title(.x))) %>%
mutate(mo = toupper(mo))
if ("fullname" %in% names(x)){
test_isolates <- test_isolates_pre[,c(demograph_df)]
} else {
test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "mo")[,c(demograph_df,"fullname")] #add in fullname
}
test_isolates <- test_isolates %>% #remove bugs to be excluded and final style conversions
filter(!(fullname %in% exclude)) %>%
mutate(fullname = str_to_title(fullname))
rx_abo <- x %>% #from orginal dataset
filter(mo %in% test_isolates$mo) %>% #select only those bugs that should be included
distinct(date, patient, mo, .keep_all = TRUE) #ensure that there are no duplications
rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] #extract susceptibility data
if (any(str_detect(rx_regs,fixed("+")))){#if combinations have already been accounted for in susceptibility profiling
rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df)) # extract susceptibility data
rx_regs_comb <- rx_regs[str_detect(rx_regs,fixed("+"))]# find combination regimen variables
rx_regs_comb_x_total <- data.frame()
for (jj in 1:length(rx_regs_comb)){ # for every combination regimen
rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))),"[[:punct:]]") #extract separate antimicrobials included in regimen
rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(rx_regs_comb_sep)){ #for every antibiotic in the combination regimen
rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE) #extract their antibiotic name
rx_regs_comb_ab <- as.ab(rx_regs_comb_name) #convert antibiotic name to abbreviation
rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[,c("ab","name")]
rx_regs_comb_pre[ii,] <- rx_regs_comb_antibiotic
names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
}
rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
rx_regs_comb_x_set <- rx_regs_comb_x %>%
mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+"))) #generate new antimicrobials table
rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set)-1)] <- rx_regs_new_ab
names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)]<- "namerx"
rx_regs_comb_x_total <-rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
}
rx_regs_comb_rename <- select(rx_regs_comb_x_total,starts_with("ab")) #extract all antibiotic abbreviations
#regenerate variable names for dataset
suppressWarnings({
for (k in 1:nrow(rx_regs_comb_x_total)){
rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k,])
rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
}
})
names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)] <-
as.ab(names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[,c(names(rx_abo),"fullname")]
} else {
names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[,c(names(rx_abo),"fullname")]
}
} else {
#If fullname included in dataset:
test_isolates_pre <- x %>%
mutate(across(.cols = all_of(demograph_df),~ str_to_title(.x))) %>%
filter(!(fullname %in% unique(exclude)))
print(nrow(test_isolates_pre))
test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "fullname")[,c(demograph_df,"mo")]
test_isolates$fullname <- str_to_title(test_isolates$fullname)
test_isolates$mo <- toupper(test_isolates$mo)
rx_abo <- x %>%
mutate(fullname = str_to_title(fullname)) %>%
filter(!(fullname %in% unique(exclude))) %>%
distinct(date, patient, fullname, .keep_all = TRUE)
rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] #susceptibility data
if (any(str_detect(rx_regs,fixed("+")))){#if combinations have already been accounted for
rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df))
rx_regs_comb <- rx_regs[str_detect(rx_regs,fixed("+"))]
rx_regs_comb_x_total <- data.frame()
for (jj in 1:length(rx_regs_comb)){
rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))),"[[:punct:]]")
rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(rx_regs_comb_sep)){
rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE)
rx_regs_comb_ab <- as.ab(rx_regs_comb_name)
rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[,c("ab","name")]
rx_regs_comb_pre[ii,] <- rx_regs_comb_antibiotic
names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
}
rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
rx_regs_comb_x_set <- rx_regs_comb_x %>%
mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+")))
rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set)-1)), FUN = function(x){str_flatten(str_c("ab",x))})
names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set)-1)] <- rx_regs_new_ab
names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)]<- "namerx"
rx_regs_comb_x_total <-rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
}
rx_regs_comb_rename <- select(rx_regs_comb_x_total,starts_with("ab"))
suppressWarnings({
for (k in 1:nrow(rx_regs_comb_x_total)){
rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k,])
rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
}
})
names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)] <-
as.ab(names(rx_abo)[!(str_detect(names(rx_abo),fixed("+"))) & !(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[,c(names(rx_abo),"mo")]
} else {
names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))],".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[,c(names(rx_abo),"mo")]
}
}
rx_abo_upd$fullname <- str_to_title(rx_abo_upd$fullname)
#generate new dataset with demographic information and only antimicrobials under investigation
rx_1 <- select(antibiotic_rx,starts_with("ab")) #all antimicrobials
rx_df <- test_isolates
var_date <- names(rx_df)[str_detect(names(rx_df),"date")]
rx_df[[var_date]] <- ymd(rx_df[[var_date]])
var_patient <- names(rx_df)[str_detect(names(rx_df),"patient")]
rx_df[[var_patient]] <- toupper(rx_df[[var_patient]])
names_vec <- vector()
suppressWarnings({
for (k in 1:nrow(rx_1)){
rx_2 <- unlist(rx_1[k,][!is.na(str_extract(rx_1[k,],"[A-Z]+"))])
if ((length(rx_2) > 1) & !(str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))){ # if more than one antibiotic in regimen and susceptibility of the combination not considered
rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",rx_2)], by = c("date","patient","mo")) #then bind separate antimicrobials in regimen
#if prior individual antibiotic was added, suffix will be generated
for (jj in 1:length(rx_2)){
if (any(str_detect(names(rx_df),paste0(rx_2[jj],".y")))){
rx_df[[paste0(rx_2[jj],".y")]] <- str_trim(rx_df[[paste0(rx_2[jj],".y")]])
} else {
rx_df[[rx_2[jj]]] <- str_trim(rx_df[[rx_2[jj]]])
}
}
names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) #rename both variables as the combination
names(rx_df)[names(rx_df) %in% paste0(rx_2,".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
names(rx_df)[names(rx_df) %in% paste0(rx_2,".x")] <- rx_2
names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
} else if ((length(rx_2) > 1) & (str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))){#if more than one antibiotic in regimen and susceptibility of the combination considered
rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",str_flatten(str_c(rx_2, collapse = "+")))], by = c("date","patient","mo"))
rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]] <- str_trim(rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]])
names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
} else {
rx_df <- merge(x = rx_df, y = rx_abo_upd[,c("date","patient","mo",rx_2)], by = c("date","patient","mo")) #then bind separate antimicrobials in regimen
rx_df[[rx_2]] <- str_trim(rx_df[[rx_2]])
names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) #rename both variables as the combination
names(rx_df)[names(rx_df) %in% paste0(rx_2,".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
names(rx_df)[names(rx_df) %in% paste0(rx_2,".x")] <- rx_2
names_vec[k] <- rx_2
}
}
})
if (any(is.na(rx_df$mo))){
missing1 <- which(is.na(rx_df$mo))
if (rx_df[missing1,"fullname"] != "NA"){
rx_df[missing1,"mo"] <- as.mo(rx_df[missing1,]$fullname, keep_synonyms = TRUE)
} else {
rx_df <- rx_df[-missing1,]
}
}
#Generate adapted first isolate dataset on antimicrobial level
if (isolate_first == "yes"){ #if dataset has already been deduplicated for first isolates
test_isolates_first <- rx_df
} else {
test_isolates_first <- rx_df[first_isolate(x = rx_df,
method = "e",
episode_days = 14,
col_date = var_date,
col_mo = "mo") == TRUE,] #Only include isolates for microorganisms < 14 days apart
}
test_isolates_first_upd <<- test_isolates_first %>%
pivot_longer(cols = names(test_isolates_first)[which(sapply(test_isolates_first,
function(y) any(str_detect(y,"^S$"))|any(str_detect(y,"^R$"))|any(str_detect(y,"^SENSITIVE$"))| any(str_detect(y,"^RESISTANT$"))))],
names_to = "keyantimicrobials", values_to = "antibiogram") %>% #combine all regimens and antibiograms into single variables
mutate(antibiogram = ifelse(antibiogram == "I"|antibiogram == "INTERMEDIATE",susceptible_I,antibiogram)) %>% #simplify susceptibility outputs - 90-60 rule
mutate(antibiogram = ifelse(antibiogram == "SENSITIVE","S", antibiogram)) %>%
mutate(antibiogram = ifelse(antibiogram == "RESISTANT","R",antibiogram)) %>%
mutate(antibiogram = factor(antibiogram),
keyantimicrobials = as.factor(keyantimicrobials))
#recode susceptibility of regimes if not already in the dataset: if any S, then all S; if any NA and not S, then combination NA; if all R, then combination R.
print("-------------------")
print(test_isolates_first_upd)
print(names(test_isolates_first_upd))
print(duplicated(names(test_isolates_first_upd)))
print(any(duplicated(names(test_isolates_first_upd))))
print("-------------------")
if (any(duplicated(names(test_isolates_first_upd)))){
test_isolates_temp3 <- test_isolates_upd %>%
mutate(antibiogram = as.character(antibiogram)) %>%
mutate(antibiogram = replace_na(antibiogram,"U")) %>%
mutate(combiantibiogram = "A")
test_isolates_temp4 <- test_isolates_temp3 %>%
group_by(date,patient,mo,keyantimicrobials) %>%
mutate(combiantibiogram = ifelse(any(str_detect(antibiogram,"S")), "S",combiantibiogram)) %>%
mutate(combiantibiogram = ifelse(any(str_detect(antibiogram,"U")) & !any(str_detect(antibiogram,"S")), "U",combiantibiogram)) %>%
mutate(combiantibiogram = ifelse(!any(str_detect(antibiogram,"U")) & !any(str_detect(antibiogram,"S")), "R", combiantibiogram)) %>%
distinct(date,patient,mo,.keep_all = TRUE) %>%
ungroup() %>%
select(-antibiogram) %>%
rename("antibiogram" = "combiantibiogram") %>%
mutate(antibiogram = ifelse(antibiogram == "U",NA_character_, antibiogram)) %>%
mutate(antibiogram = as.factor(antibiogram))
test_isolates_temp5 <- test_isolates_upd %>% #isolate the single regimens
group_by(date,patient,mo) %>%
filter(!str_detect(keyantimicrobials,fixed("+"))) %>%
ungroup()
test_isolates_final <- bind_rows(test_isolates_temp4,test_isolates_temp5)
test_isolates_final <- test_isolates_final %>%
arrange(patient)
} else {
test_isolates_final <- test_isolates_first_upd
}
test_isolates_final <<- test_isolates_final
test_isolates_final$fullname <- as.factor(test_isolates_final$fullname)
test_isolates_final$date <- ymd(test_isolates_final$date)
#Calculate pathogen incidence and susceptible by regimen
mo_name_total <- test_isolates_final %>% #deduplicate pathogen list
filter(!duplicated(mo)) %>%
select(mo,fullname)
organism_count_total <- test_isolates_final %>% #count the number of occurrences of pathogens within first isolates set
group_by(mo) %>%
distinct(date,patient,.keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_total_upd <- organism_count_total %>%
mutate(mo = as.character(mo)) %>%
mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"),"B_KLBSL",mo)) %>%
mutate(mo = as.factor(mo))
klebsiella_combine <- organism_count_total_upd %>%
filter(mo == "B_KLBSL") %>%
summarise(n_kleb = sum(n))
organism_count_total_upd <- organism_count_total_upd %>%
mutate(n = ifelse(mo == "B_KLBSL", klebsiella_combine$n_kleb, n)) %>%
distinct(mo,.keep_all = TRUE)
organism_count_total_upd <- rename(organism_count_total_upd,
"n_inc" = "n")
regimen_organism <- slice_max(organism_count_total_upd, order_by = n_inc, n = pathogen_in) #choose only the top n number of occurring pathogens to consider in WISCA
total_pathogens <- sum(regimen_organism$n_inc) #total number of occurrences of pathogens
print(paste0("Total pathogens = ",sum(organism_count_total_upd$n_inc)))
print(paste0("Number of pathogens contributing to WISCA = ", total_pathogens))
print(paste0("Percentage of pathogens contributing to WISCA = ",round((total_pathogens/sum(organism_count_total_upd$n_inc))*100,0),"%"))
test_isolates_final <- test_isolates_final %>%
mutate(mo = as.character(mo)) %>%
mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"), "B_KLBSL",mo)) %>% #combine all Klebsiella species together
mutate(mo = as.factor(mo)) %>%
mutate(fullname = as.character(fullname)) %>%
mutate(fullname = ifelse(str_detect(fullname, "Klebsiella"), "Klebsiella sp.", fullname)) %>% #rename all Klebsiella species
mutate(fullname = as.factor(fullname))
#for each antimicrobial regimen calculate the pathogen incidence and sensitivity
params_set_out <- data.frame()
if (analysis %in% names(test_isolates_final)){
#if there is a facility/ward/category level analysis required
for (j in 1:length(unique(test_isolates_final[[analysis]]))){#for every facility/ward/category
infection_episodes <- length(unique(test_isolates_final[test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j],]$patient))
if (infection_episodes < 100){#must have at least 100 infection episodes for WISCA
warning(paste0("Number of infection episodes = ",infection_episodes,". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
} else {
print(paste0("Number of infection episodes = ",infection_episodes," > 100 minimum required. Sample size adequate for WISCA analysis."))
}
y_site <- subset(test_isolates_final, test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j])
mo_name_sites <- y_site %>% #deduplicate
filter(!duplicated(mo)) %>%
select(mo,fullname,all_of(analysis))
organism_count_regimen <- y_site %>% #count the number of organisms per regimen
filter(mo %in% regimen_organism$mo) %>%
group_by(mo) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df <- left_join(x = organism_count_regimen, y = mo_name_sites, by = "mo")[,c("mo",analysis,"fullname","n")]
total_sites_pathogens <- sum(organism_count_df$n)
print(paste0("Number of pathogens contributing to WISCA in ",unique(test_isolates_final[[analysis]])[j]," = ", total_sites_pathogens))
organism_count_df <- organism_count_df %>%
mutate(prop.n = n/total_sites_pathogens) #pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
#
susceptible_regimen_tested <<- y_site %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(!is.na(antibiogram)) %>%
group_by(mo,keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
susceptible_regimen_ntested <<- y_site %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(is.na(antibiogram)) %>% #Not tested
group_by(mo,keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df_tested <<- susceptible_regimen_tested %>%
group_by(mo,keyantimicrobials) %>%
summarise(tested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_ntested <<- susceptible_regimen_ntested %>%
group_by(mo,keyantimicrobials) %>%
summarise(ntested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo","keyantimicrobials")) #combine tested and nontested
organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
mutate(tested_n = ifelse(is.na(tested_n), 0 ,tested_n),
ntested_n = ifelse(is.na(ntested_n), 0, ntested_n))
organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[,c("mo","keyantimicrobials","tested_n")], by = "mo")
#minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
condition_tested <- organism_count_df_regimen_tested %>%
filter(tested_n <30)
if (nrow(condition_tested) > 0){
warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for following regimens")
View(condition_tested[c("mo","n","keyantimicrobials","tested_n")])
} else {
print("Number of tested isolates per regimen exceed minimum threshold of 30.")
}
organism_tested_sensitive <- susceptible_regimen_tested %>%
filter(antibiogram == "S")
if (nrow(susceptible_regimen_tested)> 0){
sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo","keyantimicrobials"))
sensitivity <- sensitivity %>%
rename("S_n" = "n.y") %>%
mutate(S_n = ifelse(is.na(S_n),0,S_n)) %>%
select(-antibiogram)
sensitivity <- sensitivity %>% #calculate proportion sensitive out of those tested and having results
mutate(prop.S = ifelse(tested_n != 0, S_n/tested_n, 0))
sensitivity$perc.S <- sensitivity$prop.S * 100
params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL)
} else {
next
}
}
params_set_out$fullname <- droplevels(params_set_out$fullname)
params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]] <- as.factor(params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]])
} else{
infection_episodes <- length(unique(test_isolates_final$patient))
if (infection_episodes < 100){#must have at least 100 infection episodes for WISCA
warning(paste0("Number of infection episodes = ",infection_episodes,". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
} else {
print(paste0("Number of infection episodes = ",infection_episodes," > 100 minimum required. Sample size adequate for WISCA analysis."))
}
mo_name_sites <- test_isolates_final %>% #deduplicate
filter(!duplicated(mo)) %>%
select(mo,fullname)
organism_count_path <- test_isolates_final %>% #count the number of organisms
filter(mo %in% regimen_organism$mo) %>%
group_by(mo) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df <- left_join(x = organism_count_path, y = mo_name_sites, by = "mo")[,c("mo","fullname","n")]
total_sites_pathogens <- sum(organism_count_df$n)
organism_count_df <- organism_count_df %>%
mutate(prop.n = n/total_pathogens) #pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
susceptible_regimen_tested <<- test_isolates_final %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(!is.na(antibiogram)) %>% #Tested
group_by(mo,keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
susceptible_regimen_ntested <<- test_isolates_final %>% #determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(is.na(antibiogram)) %>% #Not tested
group_by(mo,keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df_tested <<- susceptible_regimen_tested %>%
group_by(mo,keyantimicrobials) %>%
summarise(tested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_ntested <<- susceptible_regimen_ntested %>%
group_by(mo,keyantimicrobials) %>%
summarise(ntested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo","keyantimicrobials")) #combine tested and nontested
organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
mutate(tested_n = ifelse(is.na(tested_n), 0 ,tested_n),
ntested_n = ifelse(is.na(ntested_n), 0, ntested_n))
organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[,c("mo","keyantimicrobials","tested_n")], by = "mo")
#minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
condition_tested <- organism_count_df_regimen_tested %>%
filter(tested_n <30)
if (nrow(condition_tested) > 0){
warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for these regimens.")
View(condition_tested[,c("mo","n","keyantimicrobials","tested_n")])
} else {
print("Number of tested isolates per regimen exceed minimum threshold of 30.")
}
organism_tested_sensitive <- susceptible_regimen_tested %>%
filter(antibiogram == "S")
if (nrow(susceptible_regimen_tested)> 0){
sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo","keyantimicrobials"))
sensitivity <- sensitivity %>%
rename("S_n" = "n.y") %>%
mutate(S_n = ifelse(is.na(S_n),0,S_n)) %>%
select(-antibiogram)
sensitivity <- sensitivity %>% #calculate proportion sensitive out of those tested and having results
mutate(prop.S = ifelse(tested_n != 0, S_n/tested_n, 0))
sensitivity$perc.S <- sensitivity$prop.S * 100
params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL)
} else {
next
}
params_set_out$fullname <- droplevels(params_set_out$fullname)
}
# if (infection_in != ""){
# params_set_out <- params_set_out %>%
# mutate(infection_type = infection_in) %>%
# mutate(infection_type_full = infection_full) %>%
# rowwise() %>%
# mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
# str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "),
# ab_name(keyantimicrobials))) %>%
# select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
#
#
# } else {
# print("here??")
# params_set_out <- params_set_out %>%
# mutate(infection_type_full = infection_full) %>%
# rowwise() %>%
# mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
# str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "),
# ab_name(keyantimicrobials))) %>%
# select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
# }
return(params_set_out)
}

122
data-raw/wisca_test/wisca_AMR.R Normal file
View File

@@ -0,0 +1,122 @@
# Copyright (c) [2022] [Larisse Bolton (author), Aislinn Cook (contributor)]
# Adapted from: Bielicki JA, Sharland M, Heath PT, et al. Evaluation of the coverage of 3 antibiotic regimens for
# neonatal sepsis in the hospital setting across Asian countries. JAMA Netw Open.
# 2020:3(2):e1921124. doi:10.1001.jamanetworkopen.2019.21124
wisca_funct <- function(wisca_in,analysis){
#function used to gerenrate coverage estimate parameters from input data
priors <- function(wisca_prior_in){
#INCIDENCE
#prior Dirichlet (Gamma) parameters
A <- rep(1, times = length(unique(wisca_prior_in$mo)))
Multiplier <- rep(1, times = length(unique(wisca_prior_in$mo)))
multinomial_obs <- round((wisca_prior_in$n.x)*Multiplier,0)
#posterior Dirichlet (Gamma) parameters
gamma_A <- A + multinomial_obs
B <- rep(1, times = length(gamma_A))
##SUSCEPTIBILITY
#prior Beta parameters
beta_A <- rep(1, times = length(unique(wisca_prior_in$mo)))
beta_B <- rep(1, times = length(unique(wisca_prior_in$mo)))
#Binomial distribution parameters
r <- wisca_prior_in$S_n #number of pathogens that tested sensitive
n_obs <- wisca_prior_in$tested_n #number of pathogens tested
# n_tested <- round(n_obs*(wisca_calc_input$prop.sens))
diff_nr <- n_obs - r #difference between number of pathogens that were tested and those that tested sensitive
#posterior beta parameters
post_beta_1 <- beta_A + r
post_beta_2 <- beta_B + diff_nr
priors_df <- bind_cols(gamma_A,B,post_beta_1,post_beta_2)
names(priors_df) <- c("gamma_A","B","post_beta_1","post_beta_2")
return(priors_df)
}
#simulations per bug
#function to generate simulations
coverage <- function(wisca_sim_in){
sim_coverage <- wisca_sim_in %>%
mutate(random_incidence = runif(n = nrow(wisca_sim_in), min = 0, max = 1),
random_susceptibility = runif(n = nrow(wisca_sim_in), min = 0, max = 1)) %>%
mutate(simulation_bug = qgamma(random_incidence, shape = gamma_A, scale = B),
simulation_suscep = qbeta(p = random_susceptibility,
shape1 = post_beta_1, shape2 = post_beta_2)) %>%
mutate(sim_inc_total = sum(simulation_bug),
simulated_incidence = simulation_bug/sim_inc_total) %>%
mutate(simulated_bugcover = simulated_incidence *simulation_suscep,
coverage = sum(simulated_bugcover))
sim_coverage_out <- slice_head(sim_coverage, n = 1)$coverage
return(sim_coverage_out)
}
##will run this for every regimen
set.seed(1243)
cover <- data.frame()
for (gg in 1:length(unique(wisca_in$keyantimicrobials))){
simulation_nr <- 1000
#generate 1000 coverage estimates and determine mean and CI
wisca <- subset(wisca_in, keyantimicrobials == unique(wisca_in$keyantimicrobials)[gg])
if (analysis %in% names(wisca)){
cover_level <- data.frame()
for (ii in 1:length(levels(wisca[[analysis]]))){
wisca_prep <- subset(wisca, wisca[[analysis]] == levels(wisca[[analysis]])[ii])
params_priors <- priors(wisca_prior_in = wisca_prep)
coverage_simulation_total <- replicate(n = simulation_nr,coverage(wisca_sim_in = params_priors))
av_coverage <- mean(coverage_simulation_total)
ci_coverage_lower <- quantile(coverage_simulation_total, probs = 0.025)
ci_coverage_upper <- quantile(coverage_simulation_total, probs = 0.975)
combine_out <- cbind.data.frame(unique(wisca_in$keyantimicrobials)[gg],
levels(wisca[[analysis]])[ii],
av_coverage,
ci_coverage_lower,
ci_coverage_upper,
row.names = NULL)
names(combine_out) <- c("Regimen",str_to_sentence(analysis),"Coverage","Lower_CI","Upper_CI")
cover_level <- rbind.data.frame(cover_level,combine_out, row.names = NULL)
}
cover <- rbind.data.frame(cover,cover_level, row.names = NULL)
} else {
params_priors <- priors(wisca_prior_in = wisca)
coverage_simulation_total <- replicate(n = simulation_nr,coverage(wisca_sim_in = params_priors))
av_coverage <- mean(coverage_simulation_total)
ci_coverage_lower <- quantile(coverage_simulation_total, probs = 0.025)
ci_coverage_upper <- quantile(coverage_simulation_total, probs = 0.975)
combine_out <- cbind.data.frame(unique(wisca_in$keyantimicrobials)[gg],
av_coverage,
ci_coverage_lower,
ci_coverage_upper,
row.names = NULL)
names(combine_out) <- c("Regimen","Coverage","Lower_CI","Upper_CI")
cover <- rbind.data.frame(cover,combine_out, row.names = NULL)
}
}
cover <- cover %>%
mutate(across(.cols = Coverage:Upper_CI, ~ round(.x*100,1))) %>%
rowwise() %>%
mutate(Regimen_full = ifelse(str_detect(Regimen, fixed("+")),
str_c(sapply(unlist(unique(str_split(Regimen, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "),
ab_name(Regimen))) %>%
relocate(Regimen_full,.before = Regimen)
return(cover)
}

BIN
data/clinical_breakpoints.rda
View File

Binary file not shown.

BIN
data/intrinsic_resistant.rda
View File

Binary file not shown.

BIN
data/microorganisms.codes.rda
View File

Binary file not shown.

BIN
data/microorganisms.groups.rda
View File

Binary file not shown.

BIN
data/microorganisms.rda
View File

Binary file not shown.

4
index.md
View File

@@ -8,7 +8,7 @@
- Peer-reviewed, used in over 175 countries, available in 28 languages
- Generates **antibiograms** - traditional, combined, syndromic, and
even WISCA
- Provides the **full microbiological taxonomy** of ~79 000 distinct
- Provides the **full microbiological taxonomy** of ~97 000 distinct
species and extensive info of ~620 antimicrobial drugs
- Applies **CLSI 2011-2026** and **EUCAST 2011-2026** clinical and
veterinary breakpoints, and ECOFFs, for MIC and disk zone
@@ -59,7 +59,7 @@ formed the basis of two PhD theses ([DOI
10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and
[DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
After installing this package, R knows [**~79 000 distinct microbial
After installing this package, R knows [**~97 000 distinct microbial
species**](./reference/microorganisms.html) (updated June 2024) and all
[**~620 antimicrobial and antiviral
drugs**](./reference/antimicrobials.html) by name and code (including

42
man/AMR.Rd
View File

@@ -5,7 +5,27 @@
\alias{AMR-package}
\alias{AMR}
\title{The \code{AMR} Package}
\source{
\description{
Welcome to the \code{AMR} package.
The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyright}{free and open-source} R package with \href{https://en.wikipedia.org/wiki/Dependency_hell}{zero dependencies} to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. \strong{Our aim is to provide a standard} for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of \href{https://amr-for-r.org/authors.html}{many different researchers} from around the globe to make this a successful and durable project!
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated June 2024) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
}
\section{Download Our Reference Data}{
All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.
For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.
Visit \href{https://amr-for-r.org/articles/datasets.html}{our website for direct download links}, or explore the actual files in \href{https://github.com/msberends/AMR/tree/main/data-raw/datasets}{our GitHub repository}.
}
\references{
To cite AMR in publications use:
Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). "AMR: An R Package for Working with Antimicrobial Resistance Data." \emph{Journal of Statistical Software}, \emph{104}(3), 1-31. \doi{10.18637/jss.v104.i03}
@@ -25,26 +45,6 @@ A BibTeX entry for LaTeX users is:
}
}
}
\description{
Welcome to the \code{AMR} package.
The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyright}{free and open-source} R package with \href{https://en.wikipedia.org/wiki/Dependency_hell}{zero dependencies} to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. \strong{Our aim is to provide a standard} for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of \href{https://amr-for-r.org/authors.html}{many different researchers} from around the globe to make this a successful and durable project!
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~79 000 distinct microbial species}} (updated June 2024) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
}
\section{Download Our Reference Data}{
All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.
For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.
Visit \href{https://amr-for-r.org/articles/datasets.html}{our website for direct download links}, or explore the actual files in \href{https://github.com/msberends/AMR/tree/main/data-raw/datasets}{our GitHub repository}.
}
\seealso{
Useful links:
\itemize{

2
man/WHOCC.Rd
View File

@@ -8,7 +8,7 @@ All antimicrobial drugs and their official names, ATC codes, ATC groups and defi
}
\section{WHOCC}{
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/index_en.htm}).
These have become the gold standard for international drug utilisation monitoring and research.

2
man/ab_property.Rd
View File

@@ -96,7 +96,7 @@ The function \code{\link[=set_ab_names]{set_ab_names()}} is a special column ren
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://atcddd.fhi.no/atc_ddd_index/}
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/reg_hum_atc.htm}
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/index_en.htm}
}
\section{Download Our Reference Data}{

4
man/age_groups.Rd
View File

@@ -17,10 +17,10 @@ age_groups(x, split_at = c(0, 12, 25, 55, 75), names = NULL,
\item{na.rm}{A \link{logical} to indicate whether missing values should be removed.}
}
\value{
Ordered [factor]
Ordered \link{factor}
}
\description{
Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis. The function returns an ordered [factor].
Split ages into age groups defined by the \code{split} argument. This allows for easier demographic (antimicrobial resistance) analysis. The function returns an ordered \link{factor}.
}
\details{
To split ages, the input for the \code{split_at} argument can be:

20
man/amr-tidymodels.Rd
View File

@@ -30,6 +30,26 @@ step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
skip = FALSE, id = recipes::rand_id("sir_numeric"))
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the sequence of
operations for this recipe.}
\item{...}{One or more selector functions to choose variables for this step.
See \code{\link[recipes:selections]{selections()}} for more details.}
\item{role}{Not used by this step since no new variables are created.}
\item{trained}{A logical to indicate if the quantities for preprocessing have
been estimated.}
\item{skip}{A logical. Should the step be skipped when the recipe is baked by
\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes:prep]{prep()}} is run, some
operations may not be able to be conducted on new data (e.g. processing the
outcome variable(s)). Care should be taken when using \code{skip = TRUE} as it
may affect the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\description{
This family of functions allows using AMR-specific data types such as \verb{<sir>} and \verb{<mic>} inside \code{tidymodels} pipelines.
}

52
man/antibiogram.Rd
View File

@@ -6,17 +6,9 @@
\alias{retrieve_wisca_parameters}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{wisca_plot}
\alias{knit_print.antibiogram}
\title{Generate Traditional, Combination, Syndromic, or WISCA Antibiograms}
\source{
\itemize{
\item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397}
\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124}
\item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
\item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
}
\usage{
antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
@@ -40,7 +32,12 @@ retrieve_wisca_parameters(wisca_model, ...)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
\method{autoplot}{antibiogram}(object, geom = c("pointrange", "point", "col",
"bar", "errorbar"), ci = TRUE, sort = TRUE, flip = NULL,
caption = NULL, ...)
wisca_plot(wisca_model, wisca_plot_type = c("susceptibility_incidence",
"posterior_coverage"), ...)
\method{knit_print}{antibiogram}(x, italicise = TRUE,
na = getOption("knitr.kable.NA", default = ""), ...)
@@ -69,11 +66,11 @@ retrieve_wisca_parameters(wisca_model, ...)
}
}}
\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: \code{"mo"}, \code{"fullname"}, \code{"status"}, \code{"kingdom"}, \code{"phylum"}, \code{"class"}, \code{"order"}, \code{"family"}, \code{"genus"}, \code{"species"}, \code{"subspecies"}, \code{"rank"}, \code{"ref"}, \code{"oxygen_tolerance"}, \code{"source"}, \code{"lpsn"}, \code{"lpsn_parent"}, \code{"lpsn_renamed_to"}, \code{"mycobank"}, \code{"mycobank_parent"}, \code{"mycobank_renamed_to"}, \code{"gbif"}, \code{"gbif_parent"}, \code{"gbif_renamed_to"}, \code{"prevalence"}, or \code{"snomed"}. Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
\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: \code{"mo"}, \code{"fullname"}, \code{"status"}, \code{"domain"}, \code{"kingdom"}, \code{"phylum"}, \code{"class"}, \code{"order"}, \code{"family"}, \code{"genus"}, \code{"species"}, \code{"subspecies"}, \code{"rank"}, \code{"ref"}, \code{"oxygen_tolerance"}, \code{"morphology"}, \code{"source"}, \code{"lpsn"}, \code{"lpsn_parent"}, \code{"lpsn_renamed_to"}, \code{"mycobank"}, \code{"mycobank_parent"}, \code{"mycobank_renamed_to"}, \code{"gbif"}, \code{"gbif_parent"}, \code{"gbif_renamed_to"}, \code{"prevalence"}, or \code{"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{antimicrobials} data set (defaults to \code{"name"}): \code{"ab"}, \code{"cid"}, \code{"name"}, \code{"group"}, \code{"atc"}, \code{"atc_group1"}, \code{"atc_group2"}, \code{"abbreviations"}, \code{"synonyms"}, \code{"oral_ddd"}, \code{"oral_units"}, \code{"iv_ddd"}, \code{"iv_units"}, or \code{"loinc"}. Can also be \code{NULL} to not transform the input.}
\item{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*.}
\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-and-replace-when]{case_when()}}. See \emph{Examples}.}
\item{add_total_n}{\emph{(deprecated in favour of \code{formatting_type})} 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.}
@@ -95,7 +92,9 @@ retrieve_wisca_parameters(wisca_model, ...)
\item{sort_columns}{A \link{logical} to indicate whether the antimicrobial columns must be sorted on name.}
\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{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}. Per \doi{10.1093/jac/dkv397}, susceptibility priors are \eqn{\beta(0.5, 0.5)} (Jeffreys) and intrinsically resistant pairs (based on \link{intrinsic_resistant}) use \eqn{\beta(1, 9999)}.
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.}
@@ -107,12 +106,24 @@ retrieve_wisca_parameters(wisca_model, ...)
\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}. Requires the \code{\link[future.apply:future_lapply]{future.apply}} package. For WISCA, Monte Carlo simulations are distributed across workers; for grouped antibiograms, each group is processed by a separate worker. \strong{A non-sequential \code{\link[future:plan]{future::plan()}} must already be active before setting \code{parallel = TRUE}} -- for example, \code{future::plan(future::multisession)}. An error is thrown if \code{parallel = TRUE} is used without a plan set by the user.}
\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{...}{Currently unused.}
\item{wisca_model}{The outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}.}
\item{object}{An \code{\link[=antibiogram]{antibiogram()}} object.}
\item{geom}{The plotting style for the point estimate. One of \code{"pointrange"} (default), \code{"point"}, \code{"col"}/\code{"bar"}, or \code{"errorbar"}. \code{"pointrange"} is recommended for coverage data: bars imply a meaningful baseline at zero, which coverage estimates rarely have.}
\item{ci}{Logical, whether to draw the credible/confidence interval. Defaults to \code{TRUE}. Ignored (forced \code{TRUE}) when \code{geom = "pointrange"} or \code{"errorbar"}, since the interval is intrinsic to those geoms.}
\item{sort}{Logical, whether to order regimens by coverage. Defaults to \code{TRUE}. When faceted (per pathogen) or grouped (syndromic), ordering is applied within each panel/group.}
\item{flip}{Logical, whether to draw regimens on the y-axis (horizontal). Defaults to \code{NULL}, which flips automatically when any regimen label exceeds 20 characters (long combination names read poorly on the x-axis). Set \code{TRUE}/\code{FALSE} to override.}
\item{caption}{Text to show as caption, will explain non-inferiority for WISCA models.}
\item{wisca_plot_type}{Either \code{"susceptibility_incidence"} (default) or \code{"posterior_coverage"}.}
\item{italicise}{A \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}.}
\item{na}{Character to use for showing \code{NA} values.}
@@ -213,6 +224,10 @@ wisca(your_data,
}\if{html}{\out{</div>}}
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 data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
\strong{Prior Distributions}
When \code{wisca = TRUE} or when using \code{wisca()}, pathogen incidence is modelled with a non-informative \eqn{Dirichlet(1, 1, \ldots, 1)} prior. Susceptibility proportions use the Jeffreys prior, \eqn{\beta(0.5, 0.5)}, except for bug-drug combinations with known intrinsic resistance, which use a strongly informative \eqn{\beta(1, 9999)} prior that forces near-zero susceptibility regardless of observed data (Bielicki \emph{et al.}, 2016). Intrinsic resistance is determined using the \link{intrinsic_resistant} data set, which is based on \href{https://www.eucast.org/bacteria/important-additional-information/expert-rules/}{'EUCAST Expected Resistant Phenotypes' v1.2} (2023).
}
}
@@ -439,6 +454,15 @@ plot(ab1)
plot(ab2)
}
}
\references{
\itemize{
\item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397}
\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124}
\item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
\item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
}
\author{
Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
}

20
man/antimicrobials.Rd
View File

@@ -13,14 +13,14 @@
\item \code{cid}\cr Compound ID as found in PubChem. \emph{\strong{This is a unique identifier.}}
\item \code{name}\cr Official name as used by WHONET/EARS-Net or the WHO. \emph{\strong{This is a unique identifier.}}
\item \code{group}\cr One or more short and concise group names, based on WHONET and WHOCC definitions
\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02} (last updated May 4th, 2025):
\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02} (last updated 4th of May, 2025):
\item \code{atc_group1}\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like \code{"Macrolides, lincosamides and streptogramins"}
\item \code{atc_group2}\cr Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like \code{"Macrolides"}
\item \code{abbr}\cr List of abbreviations as used in many countries, also for antimicrobial susceptibility testing (AST)
\item \code{synonyms}\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
}
ATC properties (last updated May 4th, 2025):
ATC properties (last updated 4th of May, 2025):
\itemize{
\item \code{oral_ddd}\cr Defined Daily Dose (DDD), oral treatment, currently available for 180 drugs
\item \code{oral_units}\cr Units of \code{oral_ddd}
@@ -54,13 +54,6 @@ An object of class \code{deprecated_amr_dataset} (inherits from \code{tbl_df}, \
An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 120 rows and 11 columns.
}
\source{
\itemize{
\item WHO Collaborating Centre for Drug Statistics Methodology, Guidelines for ATC classification and DDD assignment, Oslo Accessed from \url{https://atcddd.fhi.no/atc_ddd_index/} on May 4th, 2025.
\item Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023). Accessed from \url{https://loinc.org} on October 19th, 2023.
\item European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}
}
}
\usage{
antimicrobials
@@ -93,7 +86,7 @@ Visit \href{https://amr-for-r.org/articles/datasets.html}{our website for direct
\section{WHOCC}{
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/index_en.htm}).
These have become the gold standard for international drug utilisation monitoring and research.
@@ -106,6 +99,13 @@ The WHOCC is located in Oslo at the Norwegian Institute of Public Health and fun
antimicrobials
antivirals
}
\references{
\itemize{
\item WHO Collaborating Centre for Drug Statistics Methodology, Guidelines for ATC classification and DDD assignment, Oslo Accessed from \url{https://atcddd.fhi.no/atc_ddd_index/} on 4th of May, 2025.
\item Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023). Accessed from \url{https://loinc.org} on 19th of October, 2023.
\item European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://ec.europa.eu/health/documents/community-register/html/index_en.htm}
}
}
\seealso{
\link{microorganisms}, \link{intrinsic_resistant}
}

4
man/as.ab.Rd
View File

@@ -58,12 +58,12 @@ You can add your own manual codes to be considered by \code{\link[=as.ab]{as.ab(
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://atcddd.fhi.no/atc_ddd_index/}
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/reg_hum_atc.htm}
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://health.ec.europa.eu/documents/community-register/html/index_en.htm}
}
\section{WHOCC}{
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://atcddd.fhi.no}) and the Pharmaceuticals Community Register of the European Commission (\url{https://ec.europa.eu/health/documents/community-register/html/index_en.htm}).
These have become the gold standard for international drug utilisation monitoring and research.

Some files were not shown because too many files have changed in this diff Show More