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| Author | SHA1 | Date | |
|---|---|---|---|
| 1b499bf012 | |||
| a4e2e25e3f | |||
| 70c601ca11 |
@ -1,6 +1,6 @@
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Package: AMR
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Version: 2.0.0.9028
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Date: 2023-07-08
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Version: 2.0.0.9031
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Date: 2023-07-10
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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7
NEWS.md
7
NEWS.md
@ -1,4 +1,4 @@
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# AMR 2.0.0.9028
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# AMR 2.0.0.9031
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## New
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* Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added for `as.sir()`. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations
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@ -15,6 +15,10 @@
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## Changed
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* Updated algorithm of `as.mo()` by giving more weight to fungi
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* `mo_rank()` now returns `NA` for 'unknown' microorganisms (`B_ANAER`, `B_ANAER-NEG`, `B_ANAER-POS`, `B_GRAMN`, `B_GRAMP`, `F_FUNGUS`, `F_YEAST`, and `UNKNOWN`)
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* When printing MO codes in a tibble, a mouse-hover now shows the full name of the microorganism
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* Plots for MIC and disk diffusion values:
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* Now have settable arguments for breakpoint type and PK/PD, like `as.sir()`
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* Will now contain the name of the guideline table in the subtitle of the plot
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* Fixed formatting for `sir_interpretation_history()`
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* Fixed some WHONET codes for microorganisms and consequently a couple of entries in `clinical_breakpoints`
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* Fixed a bug for `as.mo()` that led to coercion of `NA` values when using custom microorganism codes
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@ -26,6 +30,7 @@
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* Updated the code table in `microorganisms.codes`
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* Fixed an endless loop if using `reference_df` in `as.mo()`
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* Fixed bug for indicating UTIs in `as.sir()`
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* Greatly improved speed of `as.sir()`
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# AMR 2.0.0
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@ -60,19 +60,19 @@ EUCAST_VERSION_EXPERT_RULES <- list(
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version_txt = "v3.3",
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year = 2021,
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title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes"
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),
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"3.2" = list(
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version_txt = "v3.2",
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year = 2020,
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title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes"
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),
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"3.1" = list(
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version_txt = "v3.1",
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year = 2016,
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title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
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url = "https://www.eucast.org/expert_rules_and_expected_phenotypes"
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)
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)
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# EUCAST_VERSION_RESISTANTPHENOTYPES <- list(
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@ -80,7 +80,7 @@ EUCAST_VERSION_EXPERT_RULES <- list(
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# version_txt = "v1.2",
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# year = 2023,
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# title = "'Expected Resistant Phenotypes'",
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# url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
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# url = "https://www.eucast.org/expert_rules_and_expected_phenotypes"
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# )
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# )
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@ -1237,24 +1237,24 @@ font_grey_bg <- function(..., collapse = " ") {
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}
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font_red_bg <- function(..., collapse = " ") {
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# this is #ed553b (picked to be colourblind-safe with other SIR colours)
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;203m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;203m", after = "\033[49m", collapse = collapse)
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}
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font_orange_bg <- function(..., collapse = " ") {
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# this is #f6d55c (picked to be colourblind-safe with other SIR colours)
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;222m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;222m", after = "\033[49m", collapse = collapse)
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}
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font_yellow_bg <- function(..., collapse = " ") {
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;228m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;228m", after = "\033[49m", collapse = collapse)
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}
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font_green_bg <- function(..., collapse = " ") {
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# this is #3caea3 (picked to be colourblind-safe with other SIR colours)
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
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}
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font_purple_bg <- function(..., collapse = " ") {
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;89m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;89m", after = "\033[49m", collapse = collapse)
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}
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font_rose_bg <- function(..., collapse = " ") {
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try_colour(font_black(..., collapse = collapse), before = "\033[48;5;217m", after = "\033[49m", collapse = collapse)
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try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;217m", after = "\033[49m", collapse = collapse)
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}
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font_na <- function(..., collapse = " ") {
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font_red(..., collapse = collapse)
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@ -1533,19 +1533,17 @@ readRDS_AMR <- function(file, refhook = NULL) {
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# Faster data.table implementations ----
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match <- function(x, table, ...) {
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chmatch <- import_fn("chmatch", "data.table", error_on_fail = FALSE)
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if (!is.null(chmatch) && is.character(x) && is.character(table)) {
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if (!is.null(AMR_env$chmatch) && inherits(x, "character") && inherits(table, "character")) {
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# data.table::chmatch() is much faster than base::match() for character
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chmatch(x, table, ...)
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AMR_env$chmatch(x, table, ...)
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} else {
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base::match(x, table, ...)
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}
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}
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`%in%` <- function(x, table) {
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chin <- import_fn("%chin%", "data.table", error_on_fail = FALSE)
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if (!is.null(chin) && is.character(x) && is.character(table)) {
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if (!is.null(AMR_env$chin) && inherits(x, "character") && inherits(table, "character")) {
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# data.table::`%chin%`() is much faster than base::`%in%`() for character
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chin(x, table)
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AMR_env$chin(x, table)
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} else {
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base::`%in%`(x, table)
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}
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2
R/data.R
2
R/data.R
@ -176,7 +176,7 @@
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#' Data Set with `r format(nrow(microorganisms.groups), big.mark = " ")` Microorganisms In Species Groups
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#'
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#' A data set containing species groups and microbiological complexes, which are used in [the clinical breakpoints table][clinial_breakpoints].
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#' A data set containing species groups and microbiological complexes, which are used in [the clinical breakpoints table][clinical_breakpoints].
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#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.groups), big.mark = " ")` observations and `r ncol(microorganisms.groups)` variables:
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#' - `mo_group`\cr ID of the species group / microbiological complex
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#' - `mo`\cr ID of the microorganism belonging in the species group / microbiological complex
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41
R/mo.R
41
R/mo.R
@ -134,6 +134,10 @@
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#' "Ureaplasmium urealytica",
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#' "Ureaplazma urealitycium"
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#' ))
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#'
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#' # input will get cleaned up with the input given in the `cleaning_regex` argument,
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#' # which defaults to `mo_cleaning_regex()`:
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#' cat(mo_cleaning_regex(), "\n")
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#'
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#' as.mo("Streptococcus group A")
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#'
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@ -561,14 +565,17 @@ mo_reset_session <- function() {
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#' @rdname as.mo
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#' @export
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mo_cleaning_regex <- function() {
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parts_to_remove <- c("e?spp([^a-z]+|$)", "e?ssp([^a-z]+|$)", "e?ss([^a-z]+|$)", "e?sp([^a-z]+|$)", "e?subsp", "sube?species", "e?species",
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"biovar[a-z]*", "biotype", "serovar[a-z]*", "var([^a-z]+|$)", "serogr.?up[a-z]*",
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"titer", "dummy", "Ig[ADEGM]")
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paste0(
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"(",
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"[^A-Za-z- \\(\\)\\[\\]{}]+",
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"|",
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"([({]|\\[).+([})]|\\])",
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"|",
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"(^| )(e?spp|e?ssp|e?ss|e?sp|e?subsp|sube?species|biovar|biotype|serovar|var|serogr.?up|e?species|titer|dummy)[.]*|( Ig[ADEGM])( |$))"
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)
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"|(^| )(",
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paste0(parts_to_remove[order(1 - nchar(parts_to_remove))], collapse = "|"),
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"))")
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}
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# UNDOCUMENTED METHODS ----------------------------------------------------
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@ -621,6 +628,12 @@ pillar_shaft.mo <- function(x, ...) {
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)
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}
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# add the names to the bugs as mouse-over!
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if (tryCatch(isTRUE(getExportedValue("ansi_has_hyperlink_support", ns = asNamespace("cli"))()), error = function(e) FALSE)) {
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out[!x %in% c("UNKNOWN", NA)] <- font_url(url = mo_name(x[!x %in% c("UNKNOWN", NA)], language = NULL, keep_synonyms = TRUE),
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txt = out[!x %in% c("UNKNOWN", NA)])
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}
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# make it always fit exactly
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max_char <- max(nchar(x))
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if (is.na(max_char)) {
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@ -832,10 +845,10 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
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add_MO_lookup_to_AMR_env()
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col_red <- function(x) font_rose_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
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col_orange <- function(x) font_orange_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
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col_yellow <- function(x) font_yellow_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
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col_green <- function(x) font_green_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
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col_red <- function(x) font_rose_bg(x, collapse = NULL)
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col_orange <- function(x) font_orange_bg(x, collapse = NULL)
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col_yellow <- function(x) font_yellow_bg(x, collapse = NULL)
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col_green <- function(x) font_green_bg(x, collapse = NULL)
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if (has_colour()) {
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cat(word_wrap("Colour keys: ",
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@ -978,9 +991,9 @@ convert_colloquial_input <- function(x) {
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perl = TRUE
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)
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# Streptococci in different languages, like "estreptococos grupo B"
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out[x %like_case% "strepto[ck]o[ck][a-zA-Z]* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$",
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out[x %like_case% "strepto[ck]o[ck][a-zA-Z ]* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$",
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"B_STRPT_GRP\\U\\1",
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x[x %like_case% "strepto[ck]o[ck][a-zA-Z]* [abcdefghijkl]$"],
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x[x %like_case% "strepto[ck]o[ck][a-zA-Z ]* [abcdefghijkl]$"],
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perl = TRUE
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)
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out[x %like_case% "strep[a-z]* group [abcdefghijkl]$"] <- gsub(".* ([abcdefghijkl])$",
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@ -994,6 +1007,7 @@ convert_colloquial_input <- function(x) {
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perl = TRUE
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)
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out[x %like_case% "ha?emoly.*strep"] <- "B_STRPT_HAEM"
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out[x %like_case% "(strepto.* [abcg, ]{2,4}$)"] <- "B_STRPT_ABCG"
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out[x %like_case% "(strepto.* mil+er+i|^mgs[^a-z]*$)"] <- "B_STRPT_MILL"
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out[x %like_case% "mil+er+i gr"] <- "B_STRPT_MILL"
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out[x %like_case% "((strepto|^s).* viridans|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
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@ -1024,6 +1038,9 @@ convert_colloquial_input <- function(x) {
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out[x %like_case% "anaerob[a-z]+ .*gram[ -]?pos.*"] <- "B_ANAER-POS"
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out[is.na(out) & x %like_case% "anaerob[a-z]+ (micro)?.*organism"] <- "B_ANAER"
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# coryneform bacteria
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out[x %like_case% "^coryneform"] <- "B_CORYNF"
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# yeasts and fungi
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out[x %like_case% "^yeast?"] <- "F_YEAST"
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out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS"
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@ -1032,7 +1049,11 @@ convert_colloquial_input <- function(x) {
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out[x %like_case% "meningo[ck]o[ck]"] <- "B_NESSR_MNNG"
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out[x %like_case% "gono[ck]o[ck]"] <- "B_NESSR_GNRR"
|
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out[x %like_case% "pneumo[ck]o[ck]"] <- "B_STRPT_PNMN"
|
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out[x %like_case% "hacek"] <- "B_HACEK"
|
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out[x %like_case% "haemophilus" & x %like_case% "aggregatibacter" & x %like_case% "cardiobacterium" & x %like_case% "eikenella" & x %like_case% "kingella"] <- "B_HACEK"
|
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out[x %like_case% "slow.* grow.* mycobact"] <- "B_MYCBC_SGM"
|
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out[x %like_case% "rapid.* grow.* mycobact"] <- "B_MYCBC_RGM"
|
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|
||||
# unexisting names (con is the WHONET code for contamination)
|
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out[x %in% c("con", "other", "none", "unknown") | x %like_case% "virus"] <- "UNKNOWN"
|
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|
||||
|
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@ -427,22 +427,13 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
|
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kngd <- AMR_env$MO_lookup$kingdom[match(x.mo, AMR_env$MO_lookup$mo)]
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rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
|
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|
||||
out <- factor(
|
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ifelse(prev == 1 & kngd == "Bacteria" & rank != "genus",
|
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"Pathogenic",
|
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ifelse(prev < 2 & kngd == "Fungi",
|
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"Potentially pathogenic",
|
||||
ifelse(prev == 2 & kngd == "Bacteria",
|
||||
"Non-pathogenic",
|
||||
ifelse(kngd == "Bacteria",
|
||||
"Potentially pathogenic",
|
||||
"Unknown"
|
||||
)
|
||||
)
|
||||
)
|
||||
),
|
||||
levels = c("Pathogenic", "Potentially pathogenic", "Non-pathogenic", "Unknown"),
|
||||
ordered = TRUE
|
||||
out <- factor(case_when_AMR(prev == 1 & kngd == "Bacteria" & rank != "genus" ~ "Pathogenic",
|
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(prev < 2 & kngd == "Fungi") ~ "Potentially pathogenic",
|
||||
prev == 2 & kngd == "Bacteria" ~ "Non-pathogenic",
|
||||
kngd == "Bacteria" ~ "Potentially pathogenic",
|
||||
TRUE ~ "Unknown"),
|
||||
levels = c("Pathogenic", "Potentially pathogenic", "Non-pathogenic", "Unknown"),
|
||||
ordered = TRUE
|
||||
)
|
||||
|
||||
load_mo_uncertainties(metadata)
|
||||
@ -922,26 +913,36 @@ mo_validate <- function(x, property, language, keep_synonyms = keep_synonyms, ..
|
||||
Lancefield <- FALSE
|
||||
}
|
||||
has_Becker_or_Lancefield <- Becker %in% c(TRUE, "all") || Lancefield %in% c(TRUE, "all")
|
||||
|
||||
# get microorganisms data set, but remove synonyms if keep_synonyms is FALSE
|
||||
mo_data_check <- AMR_env$MO_lookup[which(AMR_env$MO_lookup$status %in% if (isTRUE(keep_synonyms)) c("synonym", "accepted") else "accepted"), , drop = FALSE]
|
||||
|
||||
if (all(x %in% c(mo_data_check$mo, NA)) && !has_Becker_or_Lancefield) {
|
||||
# do nothing, just don't run the other if-else's
|
||||
} else if (all(x %in% c(unlist(mo_data_check[[property]]), NA)) && !has_Becker_or_Lancefield) {
|
||||
# no need to do anything, just return it
|
||||
return(x)
|
||||
|
||||
if (isFALSE(has_Becker_or_Lancefield) && isTRUE(keep_synonyms) && all(x %in% c(AMR_env$MO_lookup$mo, NA))) {
|
||||
# fastest way to get properties
|
||||
if (property == "snomed") {
|
||||
x <- lapply(x, function(y) unlist(AMR_env$MO_lookup$snomed[match(y, AMR_env$MO_lookup$mo)]))
|
||||
} else {
|
||||
x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
|
||||
}
|
||||
|
||||
} else {
|
||||
# we need to get MO codes now
|
||||
x <- replace_old_mo_codes(x, property = property)
|
||||
x <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
|
||||
}
|
||||
|
||||
# get property reeaaally fast using match()
|
||||
if (property == "snomed") {
|
||||
x <- lapply(x, function(y) unlist(AMR_env$MO_lookup$snomed[match(y, AMR_env$MO_lookup$mo)]))
|
||||
} else {
|
||||
x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
|
||||
# get microorganisms data set, but remove synonyms if keep_synonyms is FALSE
|
||||
mo_data_check <- AMR_env$MO_lookup[which(AMR_env$MO_lookup$status %in% if (isTRUE(keep_synonyms)) c("synonym", "accepted") else "accepted"), , drop = FALSE]
|
||||
|
||||
if (all(x %in% c(mo_data_check$mo, NA)) && !has_Becker_or_Lancefield) {
|
||||
# do nothing, just don't run the other if-else's
|
||||
} else if (all(x %in% c(unlist(mo_data_check[[property]]), NA)) && !has_Becker_or_Lancefield) {
|
||||
# no need to do anything, just return it
|
||||
return(x)
|
||||
} else {
|
||||
# we need to get MO codes now
|
||||
x <- replace_old_mo_codes(x, property = property)
|
||||
x <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
|
||||
}
|
||||
|
||||
# get property reeaaally fast using match()
|
||||
if (property == "snomed") {
|
||||
x <- lapply(x, function(y) unlist(AMR_env$MO_lookup$snomed[match(y, AMR_env$MO_lookup$mo)]))
|
||||
} else {
|
||||
x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
|
||||
}
|
||||
}
|
||||
|
||||
if (property == "mo") {
|
||||
|
||||
52
R/plot.R
52
R/plot.R
@ -40,6 +40,7 @@
|
||||
#' @param colours_SIR colours to use for filling in the bars, must be a vector of three values (in the order S, I and R). The default colours are colour-blind friendly.
|
||||
#' @param language language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant' - the default is system language (see [get_AMR_locale()]) and can be overwritten by setting the [package option][AMR-options] [`AMR_locale`][AMR-options], e.g. `options(AMR_locale = "de")`, see [translate]. Use `language = NULL` or `language = ""` to prevent translation.
|
||||
#' @param expand a [logical] to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.
|
||||
#' @inheritParams as.sir
|
||||
#' @details
|
||||
#' The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
|
||||
#'
|
||||
@ -93,6 +94,8 @@ plot.mic <- function(x,
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...) {
|
||||
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
|
||||
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
|
||||
@ -123,7 +126,9 @@ plot.mic <- function(x,
|
||||
colours_SIR = colours_SIR,
|
||||
fn = as.mic,
|
||||
language = language,
|
||||
type = "MIC",
|
||||
method = "MIC",
|
||||
include_PKPD = include_PKPD,
|
||||
breakpoint_type = breakpoint_type,
|
||||
...
|
||||
)
|
||||
barplot(x,
|
||||
@ -224,6 +229,8 @@ autoplot.mic <- function(object,
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...) {
|
||||
stop_ifnot_installed("ggplot2")
|
||||
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
|
||||
@ -256,7 +263,9 @@ autoplot.mic <- function(object,
|
||||
colours_SIR = colours_SIR,
|
||||
fn = as.mic,
|
||||
language = language,
|
||||
type = "MIC",
|
||||
method = "MIC",
|
||||
include_PKPD = include_PKPD,
|
||||
breakpoint_type = breakpoint_type,
|
||||
...
|
||||
)
|
||||
df <- as.data.frame(x, stringsAsFactors = TRUE)
|
||||
@ -327,6 +336,8 @@ plot.disk <- function(x,
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...) {
|
||||
meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
||||
meet_criteria(ylab, allow_class = "character", has_length = 1)
|
||||
@ -357,7 +368,9 @@ plot.disk <- function(x,
|
||||
colours_SIR = colours_SIR,
|
||||
fn = as.disk,
|
||||
language = language,
|
||||
type = "disk",
|
||||
method = "disk",
|
||||
include_PKPD = include_PKPD,
|
||||
breakpoint_type = breakpoint_type,
|
||||
...
|
||||
)
|
||||
|
||||
@ -458,6 +471,8 @@ autoplot.disk <- function(object,
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...) {
|
||||
stop_ifnot_installed("ggplot2")
|
||||
meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
|
||||
@ -490,7 +505,9 @@ autoplot.disk <- function(object,
|
||||
colours_SIR = colours_SIR,
|
||||
fn = as.disk,
|
||||
language = language,
|
||||
type = "disk",
|
||||
method = "disk",
|
||||
include_PKPD = include_PKPD,
|
||||
breakpoint_type = breakpoint_type,
|
||||
...
|
||||
)
|
||||
df <- as.data.frame(x, stringsAsFactors = TRUE)
|
||||
@ -744,38 +761,45 @@ plot_name_of_I <- function(guideline) {
|
||||
}
|
||||
}
|
||||
|
||||
plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, fn, language, type, ...) {
|
||||
plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, fn, language, method, breakpoint_type, include_PKPD, ...) {
|
||||
guideline <- get_guideline(guideline, AMR::clinical_breakpoints)
|
||||
|
||||
# store previous interpretations to backup
|
||||
sir_history <- AMR_env$sir_interpretation_history
|
||||
# and clear previous interpretations
|
||||
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
|
||||
|
||||
if (!is.null(mo) && !is.null(ab)) {
|
||||
# interpret and give colour based on MIC values
|
||||
mo <- as.mo(mo)
|
||||
moname <- mo_name(mo, language = language)
|
||||
ab <- as.ab(ab)
|
||||
abname <- ab_name(ab, language = language)
|
||||
|
||||
sir <- suppressWarnings(suppressMessages(as.sir(fn(names(x)), mo = mo, ab = ab, guideline = guideline, include_screening = FALSE, include_PKPD = TRUE, ...)))
|
||||
|
||||
sir <- suppressWarnings(suppressMessages(as.sir(fn(names(x)), mo = mo, ab = ab, guideline = guideline, include_screening = FALSE, include_PKPD = include_PKPD, breakpoint_type = breakpoint_type, ...)))
|
||||
guideline_txt <- guideline
|
||||
if (all(is.na(sir))) {
|
||||
sir_screening <- suppressWarnings(suppressMessages(as.sir(fn(names(x)), mo = mo, ab = ab, guideline = guideline, include_screening = TRUE, include_PKPD = TRUE, ...)))
|
||||
sir_screening <- suppressWarnings(suppressMessages(as.sir(fn(names(x)), mo = mo, ab = ab, guideline = guideline, include_screening = TRUE, include_PKPD = include_PKPD, breakpoint_type = breakpoint_type, ...)))
|
||||
if (!all(is.na(sir_screening))) {
|
||||
message_(
|
||||
"Only ", guideline, " ", type, " interpretations found for ",
|
||||
"Only ", guideline, " ", method, " interpretations found for ",
|
||||
ab_name(ab, language = NULL, tolower = TRUE), " in ", italicise(moname), " for screening"
|
||||
)
|
||||
sir <- sir_screening
|
||||
guideline_txt <- paste0("(Screen, ", guideline_txt, ")")
|
||||
} else {
|
||||
message_(
|
||||
"No ", guideline, " ", type, " interpretations found for ",
|
||||
"No ", guideline, " ", method, " interpretations found for ",
|
||||
ab_name(ab, language = NULL, tolower = TRUE), " in ", italicise(moname)
|
||||
)
|
||||
guideline_txt <- ""
|
||||
guideline_txt <- paste0("(", guideline_txt, ")")
|
||||
}
|
||||
} else {
|
||||
if (isTRUE(list(...)$uti)) {
|
||||
guideline_txt <- paste("UTIs,", guideline_txt)
|
||||
}
|
||||
guideline_txt <- paste0("(", guideline_txt, ")")
|
||||
ref_tbl <- paste0('"', unique(AMR_env$sir_interpretation_history$ref_table), '"', collapse = "/")
|
||||
guideline_txt <- paste0("(", guideline_txt, ": ", ref_tbl, ")")
|
||||
}
|
||||
cols <- character(length = length(sir))
|
||||
cols[is.na(sir)] <- "#BEBEBE"
|
||||
@ -787,5 +811,9 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, f
|
||||
cols <- "#BEBEBE"
|
||||
sub <- NULL
|
||||
}
|
||||
|
||||
# restore previous interpretations to backup
|
||||
AMR_env$sir_interpretation_history <- sir_history
|
||||
|
||||
list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
|
||||
}
|
||||
|
||||
133
R/sir.R
133
R/sir.R
@ -105,7 +105,7 @@
|
||||
#'
|
||||
#' The function [is_sir_eligible()] returns `TRUE` when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` argument. If the input is a [data.frame], it iterates over all columns and returns a [logical] vector.
|
||||
#' @section Interpretation of SIR:
|
||||
#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (<https://www.eucast.org/newsiandr/>):
|
||||
#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (<https://www.eucast.org/newsiandr>):
|
||||
#'
|
||||
#' - **S - Susceptible, standard dosing regimen**\cr
|
||||
#' A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
@ -793,7 +793,7 @@ as_sir_method <- function(method_short,
|
||||
mo.bak <- mo
|
||||
}
|
||||
# be sure to take current taxonomy, as the 'clinical_breakpoints' data set only contains current taxonomy
|
||||
mo <- suppressWarnings(suppressMessages(as.mo(mo, keep_synonyms = FALSE, inf0 = FALSE)))
|
||||
mo <- suppressWarnings(suppressMessages(as.mo(mo, keep_synonyms = FALSE, info = FALSE)))
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (is.na(ab)) {
|
||||
message_("Returning NAs for unknown antibiotic: '", font_bold(ab.bak),
|
||||
@ -846,12 +846,13 @@ as_sir_method <- function(method_short,
|
||||
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
|
||||
|
||||
msg_note <- function(messages) {
|
||||
messages <- unique(messages)
|
||||
for (i in seq_len(length(messages))) {
|
||||
messages[i] <- word_wrap(extra_indent = 5, messages[i])
|
||||
}
|
||||
message(
|
||||
font_yellow(font_bold(paste0(" Note", ifelse(length(messages) > 1, "s", ""), ":\n"))),
|
||||
paste0(" ", font_black(AMR_env$bullet_icon), " ", font_black(messages, collapse = NULL), collapse = "\n")
|
||||
font_yellow_bg(paste0(" NOTE", ifelse(length(messages) > 1, "S", ""), " \n")),
|
||||
paste0(" ", font_black(AMR_env$bullet_icon), " ", font_black(messages, collapse = NULL), collapse = "\n")
|
||||
)
|
||||
}
|
||||
|
||||
@ -873,12 +874,12 @@ as_sir_method <- function(method_short,
|
||||
# when as.sir.disk is called directly
|
||||
df$values <- as.disk(df$values)
|
||||
}
|
||||
df_unique <- unique(df[ , c("mo", "uti"), drop = FALSE])
|
||||
|
||||
rise_warning <- FALSE
|
||||
rise_note <- FALSE
|
||||
method_coerced <- toupper(method)
|
||||
ab_coerced <- ab
|
||||
mo_coerced <- mo
|
||||
|
||||
if (identical(reference_data, AMR::clinical_breakpoints)) {
|
||||
breakpoints <- reference_data %pm>%
|
||||
@ -906,24 +907,16 @@ as_sir_method <- function(method_short,
|
||||
breakpoints <- breakpoints %pm>%
|
||||
subset(mo != "UNKNOWN" & ref_tbl %unlike% "PK.*PD")
|
||||
}
|
||||
if (all(uti == FALSE, na.rm = TRUE)) {
|
||||
# remove UTI breakpoints
|
||||
breakpoints <- breakpoints %pm>%
|
||||
subset(is.na(uti) | uti == FALSE)
|
||||
} else if (all(uti == TRUE, na.rm = TRUE)) {
|
||||
# remove UTI breakpoints
|
||||
breakpoints <- breakpoints %pm>%
|
||||
subset(uti == TRUE)
|
||||
}
|
||||
|
||||
msgs <- character(0)
|
||||
if (nrow(breakpoints) == 0) {
|
||||
# apparently no breakpoints found
|
||||
msg_note(paste0(
|
||||
"No ", method_coerced, " breakpoints available for ",
|
||||
suppressMessages(suppressWarnings(ab_name(ab_coerced, language = NULL, tolower = TRUE))),
|
||||
" (", ab_coerced, ")"
|
||||
))
|
||||
message(
|
||||
paste0(font_rose_bg(" WARNING "), "\n"),
|
||||
font_black(paste0(" ", AMR_env$bullet_icon, " No ", method_coerced, " breakpoints available for ",
|
||||
suppressMessages(suppressWarnings(ab_name(ab_coerced, language = NULL, tolower = TRUE))),
|
||||
" (", ab_coerced, ")")))
|
||||
|
||||
load_mo_uncertainties(metadata_mo)
|
||||
return(rep(NA_sir_, nrow(df)))
|
||||
}
|
||||
@ -933,32 +926,41 @@ as_sir_method <- function(method_short,
|
||||
add_intrinsic_resistance_to_AMR_env()
|
||||
}
|
||||
|
||||
p <- progress_ticker(n = length(unique(df$mo)), n_min = 10, title = font_blue(intro_txt), only_bar_percent = TRUE)
|
||||
p <- progress_ticker(n = nrow(df_unique), n_min = 10, title = font_blue(intro_txt), only_bar_percent = TRUE)
|
||||
has_progress_bar <- !is.null(import_fn("progress_bar", "progress", error_on_fail = FALSE)) && nrow(df_unique) >= 10
|
||||
on.exit(close(p))
|
||||
|
||||
|
||||
# run the rules
|
||||
for (mo_currrent in unique(df$mo)) {
|
||||
for (i in seq_len(nrow(df_unique))) {
|
||||
p$tick()
|
||||
rows <- which(df$mo == mo_currrent)
|
||||
mo_current <- df_unique[i, "mo", drop = TRUE]
|
||||
uti_current <- df_unique[i, "uti", drop = TRUE]
|
||||
if (is.na(uti_current)) {
|
||||
# preference, so no filter on UTIs
|
||||
rows <- which(df$mo == mo_current)
|
||||
} else {
|
||||
rows <- which(df$mo == mo_current & df$uti == uti_current)
|
||||
}
|
||||
values <- df[rows, "values", drop = TRUE]
|
||||
uti <- df[rows, "uti", drop = TRUE]
|
||||
new_sir <- rep(NA_sir_, length(rows))
|
||||
|
||||
# find different mo properties
|
||||
mo_current_genus <- as.mo(mo_genus(mo_currrent, language = NULL))
|
||||
mo_current_family <- as.mo(mo_family(mo_currrent, language = NULL))
|
||||
mo_current_order <- as.mo(mo_order(mo_currrent, language = NULL))
|
||||
mo_current_class <- as.mo(mo_class(mo_currrent, language = NULL))
|
||||
if (mo_currrent %in% AMR::microorganisms.groups$mo) {
|
||||
# find different mo properties, as fast as possible
|
||||
mo_current_genus <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$genus[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$genus)]
|
||||
mo_current_family <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$family[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$family)]
|
||||
mo_current_order <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$order[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$order)]
|
||||
mo_current_class <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$class[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$class)]
|
||||
mo_current_rank <- AMR_env$MO_lookup$rank[match(mo_current, AMR_env$MO_lookup$mo)]
|
||||
mo_current_name <- AMR_env$MO_lookup$fullname[match(mo_current, AMR_env$MO_lookup$mo)]
|
||||
if (mo_current %in% AMR::microorganisms.groups$mo) {
|
||||
# get the species group
|
||||
mo_current_species_group <- AMR::microorganisms.groups$mo_group[match(mo_currrent, AMR::microorganisms.groups$mo)]
|
||||
mo_current_species_group <- AMR::microorganisms.groups$mo_group[match(mo_current, AMR::microorganisms.groups$mo)]
|
||||
} else {
|
||||
mo_current_species_group <- mo_currrent
|
||||
mo_current_species_group <- mo_current
|
||||
}
|
||||
mo_current_other <- as.mo("UNKNOWN")
|
||||
mo_current_other <- structure("UNKNOWN", class = c("mo", "character"))
|
||||
# formatted for notes
|
||||
mo_formatted <- suppressMessages(suppressWarnings(mo_fullname(mo_currrent, language = NULL, keep_synonyms = FALSE)))
|
||||
if (!mo_rank(mo_currrent) %in% c("kingdom", "phylum", "class", "order")) {
|
||||
mo_formatted <- mo_current_name
|
||||
if (!mo_current_rank %in% c("kingdom", "phylum", "class", "order")) {
|
||||
mo_formatted <- font_italic(mo_formatted)
|
||||
}
|
||||
ab_formatted <- paste0(
|
||||
@ -976,40 +978,45 @@ as_sir_method <- function(method_short,
|
||||
mo_current_other
|
||||
))
|
||||
|
||||
if (any(uti, na.rm = TRUE)) {
|
||||
if (is.na(unique(uti_current))) {
|
||||
breakpoints_current <- breakpoints_current %pm>%
|
||||
# this will put UTI = FALSE first, then UTI = TRUE, then UTI = NA
|
||||
pm_arrange(rank_index, uti) # 'uti' is a column in data set 'clinical_breakpoints'
|
||||
} else if (unique(uti_current) == TRUE) {
|
||||
breakpoints_current <- breakpoints_current %pm>%
|
||||
subset(uti == TRUE) %pm>%
|
||||
# be as specific as possible (i.e. prefer species over genus):
|
||||
# the below `pm_desc(uti)` will put `TRUE` on top and FALSE on bottom
|
||||
pm_arrange(rank_index, pm_desc(uti)) # 'uti' is a column in data set 'clinical_breakpoints'
|
||||
} else {
|
||||
pm_arrange(rank_index)
|
||||
} else if (unique(uti_current) == FALSE) {
|
||||
breakpoints_current <- breakpoints_current %pm>%
|
||||
# sort UTI = FALSE first, then UTI = TRUE
|
||||
pm_arrange(rank_index, uti)
|
||||
subset(uti == FALSE) %pm>%
|
||||
# be as specific as possible (i.e. prefer species over genus):
|
||||
pm_arrange(rank_index)
|
||||
}
|
||||
|
||||
# throw notes for different body sites
|
||||
if (nrow(breakpoints_current) == 1 && all(breakpoints_current$uti == TRUE) && any(uti %in% c(FALSE, NA)) && message_not_thrown_before("as.sir", "uti", ab_coerced)) {
|
||||
site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
|
||||
if (is.na(site)) {
|
||||
site <- paste0("an unspecified body site")
|
||||
} else {
|
||||
site <- paste0("body site '", site, "'")
|
||||
}
|
||||
if (nrow(breakpoints_current) == 1 && all(breakpoints_current$uti == TRUE) && any(uti_current %in% c(FALSE, NA)) && message_not_thrown_before("as.sir", "uti", ab_coerced)) {
|
||||
# only UTI breakpoints available
|
||||
warning_("in `as.sir()`: interpretation of ", font_bold(ab_formatted), " is only available for (uncomplicated) urinary tract infections (UTI) for some microorganisms, thus assuming `uti = TRUE`. See `?as.sir`.")
|
||||
rise_warning <- TRUE
|
||||
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_currrent, ab_coerced)) {
|
||||
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti_current)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_current, ab_coerced)) {
|
||||
# both UTI and Non-UTI breakpoints available
|
||||
msgs <- c(msgs, paste0("Breakpoints for UTI ", font_underline("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming non-UTI. Use argument `uti` to set which isolates are from urine. See `?as.sir`."))
|
||||
msgs <- c(msgs, paste0("Breakpoints for UTI ", font_underline("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, ". Use argument `uti` to set which isolates are from urine. See `?as.sir`."))
|
||||
breakpoints_current <- breakpoints_current %pm>%
|
||||
pm_filter(uti == FALSE)
|
||||
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_currrent, ab_coerced)) {
|
||||
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_current, ab_coerced)) {
|
||||
# breakpoints for multiple body sites available
|
||||
site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
|
||||
if (is.na(site)) {
|
||||
site <- paste0("an unspecified body site")
|
||||
} else {
|
||||
site <- paste0("body site '", site, "'")
|
||||
}
|
||||
msgs <- c(msgs, paste0("Multiple breakpoints available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, "."))
|
||||
}
|
||||
|
||||
# first check if mo is intrinsic resistant
|
||||
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_currrent, ab_coerced) %in% AMR_env$intrinsic_resistant) {
|
||||
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_current, ab_coerced) %in% AMR_env$intrinsic_resistant) {
|
||||
msgs <- c(msgs, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
|
||||
new_sir <- rep(as.sir("R"), length(rows))
|
||||
} else if (nrow(breakpoints_current) == 0) {
|
||||
@ -1059,10 +1066,11 @@ as_sir_method <- function(method_short,
|
||||
index = rows,
|
||||
ab_input = rep(ab.bak, length(rows)),
|
||||
ab_guideline = rep(ab_coerced, length(rows)),
|
||||
mo_input = rep(mo.bak[match(mo_currrent, df$mo)][1], length(rows)),
|
||||
mo_input = rep(mo.bak[match(mo_current, df$mo)][1], length(rows)),
|
||||
mo_guideline = rep(breakpoints_current[, "mo", drop = TRUE], length(rows)),
|
||||
guideline = rep(guideline_coerced, length(rows)),
|
||||
ref_table = rep(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
|
||||
uti = rep(breakpoints_current[, "uti", drop = TRUE], length(rows)),
|
||||
method = rep(method_coerced, length(rows)),
|
||||
input = as.double(values),
|
||||
outcome = as.sir(new_sir),
|
||||
@ -1078,14 +1086,14 @@ as_sir_method <- function(method_short,
|
||||
close(p)
|
||||
|
||||
# printing messages
|
||||
if (!is.null(import_fn("progress_bar", "progress", error_on_fail = FALSE))) {
|
||||
if (has_progress_bar == TRUE) {
|
||||
# the progress bar has overwritten the intro text, so:
|
||||
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
|
||||
}
|
||||
if (isTRUE(rise_warning)) {
|
||||
message(font_yellow(font_bold(" * WARNING *")))
|
||||
message(font_rose_bg(" WARNING "))
|
||||
} else if (length(msgs) == 0) {
|
||||
message(font_green(" OK."))
|
||||
message(font_green_bg(" OK "))
|
||||
} else {
|
||||
msg_note(sort(msgs))
|
||||
}
|
||||
@ -1101,8 +1109,7 @@ as_sir_method <- function(method_short,
|
||||
sir_interpretation_history <- function(clean = FALSE) {
|
||||
meet_criteria(clean, allow_class = "logical", has_length = 1)
|
||||
|
||||
out.bak <- AMR_env$sir_interpretation_history
|
||||
out <- out.bak
|
||||
out <- AMR_env$sir_interpretation_history
|
||||
if (NROW(out) == 0) {
|
||||
message_("No results to return. Run `as.sir()` on MIC values or disk diffusion zones first to see a 'logbook' data set here.")
|
||||
return(invisible(NULL))
|
||||
@ -1113,10 +1120,8 @@ sir_interpretation_history <- function(clean = FALSE) {
|
||||
# keep stored for next use
|
||||
if (isTRUE(clean)) {
|
||||
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
|
||||
} else {
|
||||
AMR_env$sir_interpretation_history <- out.bak
|
||||
}
|
||||
|
||||
|
||||
# sort descending on time
|
||||
out <- out[order(out$datetime, decreasing = TRUE), , drop = FALSE]
|
||||
|
||||
@ -1136,7 +1141,11 @@ pillar_shaft.sir <- function(x, ...) {
|
||||
out[is.na(x)] <- font_grey(" NA")
|
||||
out[x == "S"] <- font_green_bg(" S ")
|
||||
out[x == "I"] <- font_orange_bg(" I ")
|
||||
out[x == "R"] <- font_red_bg(" R ")
|
||||
if (is_dark()) {
|
||||
out[x == "R"] <- font_red_bg(" R ")
|
||||
} else {
|
||||
out[x == "R"] <- font_rose_bg(" R ")
|
||||
}
|
||||
}
|
||||
create_pillar_column(out, align = "left", width = 5)
|
||||
}
|
||||
|
||||
2
R/zzz.R
2
R/zzz.R
@ -73,6 +73,8 @@ AMR_env$sir_interpretation_history <- data.frame(
|
||||
AMR_env$custom_ab_codes <- character(0)
|
||||
AMR_env$custom_mo_codes <- character(0)
|
||||
AMR_env$is_dark_theme <- NULL
|
||||
AMR_env$chmatch <- import_fn("chmatch", "data.table", error_on_fail = FALSE)
|
||||
AMR_env$chin <- import_fn("%chin%", "data.table", error_on_fail = FALSE)
|
||||
|
||||
# determine info icon for messages
|
||||
if (pkg_is_available("cli")) {
|
||||
|
||||
@ -1 +1 @@
|
||||
87c6c20d117acd06c37bab6d93966a0b
|
||||
7aeceefb444830af010fcc16f5ba4705
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -133,8 +133,11 @@ organisms <- organisms %>%
|
||||
select(-group) %>%
|
||||
distinct()
|
||||
|
||||
# 2023-07-08 SGM must be Slowly-growing Mycobacterium, not Strep Gamma, not sure why this went wrong
|
||||
|
||||
# 2023-07-08 SGM is also Strep gamma in WHONET, must only be Slowly-growing Mycobacterium
|
||||
organisms <- organisms %>%
|
||||
filter(!(code == "SGM" & name %like% "Streptococcus"))
|
||||
# this must be empty:
|
||||
organisms$code[organisms$code %>% duplicated()]
|
||||
|
||||
saveRDS(organisms, "data-raw/organisms.rds", version = 2)
|
||||
|
||||
@ -223,7 +226,7 @@ breakpoints %>%
|
||||
filter(!WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE) %>%
|
||||
pull(WHONET_ABX_CODE) %>%
|
||||
unique()
|
||||
# they are at the moment all old codes that have right replacements in `antibiotics`, so we can use as.ab()
|
||||
# they are at the moment all old codes that have the right replacements in `antibiotics`, so we can use as.ab()
|
||||
|
||||
|
||||
## Build new breakpoints table ----
|
||||
@ -260,7 +263,7 @@ breakpoints_new <- breakpoints %>%
|
||||
gsub("–", "-", ., fixed = TRUE)) %>%
|
||||
arrange(desc(guideline), mo, ab, type, method) %>%
|
||||
filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)) & !is.na(mo) & !is.na(ab)) %>%
|
||||
distinct(guideline, ab, mo, method, site, breakpoint_S, .keep_all = TRUE)
|
||||
distinct(guideline, type, ab, mo, method, site, breakpoint_S, .keep_all = TRUE)
|
||||
|
||||
# check the strange duplicates
|
||||
breakpoints_new %>%
|
||||
@ -268,7 +271,7 @@ breakpoints_new %>%
|
||||
filter(id %in% .$id[which(duplicated(id))])
|
||||
# remove duplicates
|
||||
breakpoints_new <- breakpoints_new %>%
|
||||
distinct(guideline, ab, mo, method, site, .keep_all = TRUE)
|
||||
distinct(guideline, type, ab, mo, method, site, .keep_all = TRUE)
|
||||
|
||||
# fix reference table names
|
||||
breakpoints_new %>% filter(guideline %like% "EUCAST", is.na(ref_tbl)) %>% View()
|
||||
@ -289,10 +292,10 @@ breakpoints_new[which(breakpoints_new$method == "MIC" &
|
||||
breakpoints_new[which(breakpoints_new$method == "MIC" &
|
||||
is.na(breakpoints_new$breakpoint_R)), "breakpoint_R"] <- max(m)
|
||||
# raise these one higher valid MIC factor level:
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 129), "breakpoint_R"] <- m[which(m == 128) + 1]
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 257), "breakpoint_R"] <- m[which(m == 256) + 1]
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 513), "breakpoint_R"] <- m[which(m == 512) + 1]
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 1025), "breakpoint_R"] <- m[which(m == 1024) + 1]
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 129), "breakpoint_R"] <- 128
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 257), "breakpoint_R"] <- 256
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 513), "breakpoint_R"] <- 513
|
||||
breakpoints_new[which(breakpoints_new$breakpoint_R == 1025), "breakpoint_R"] <- 1024
|
||||
|
||||
# WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
|
||||
# EUCAST 2022 guideline: S <= 8 and R > 8
|
||||
@ -319,6 +322,9 @@ breakpoints_new %>% filter(guideline == "EUCAST 2023", ab == "AMC", mo == "B_[OR
|
||||
# compare with current version
|
||||
clinical_breakpoints %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
|
||||
|
||||
# must have "human" and "ECOFF"
|
||||
breakpoints_new %>% filter(mo == "B_STRPT_PNMN", ab == "AMP", guideline == "EUCAST 2020", method == "MIC")
|
||||
|
||||
# check dimensions
|
||||
dim(breakpoints_new)
|
||||
dim(clinical_breakpoints)
|
||||
|
||||
Binary file not shown.
@ -106,7 +106,7 @@ expect_identical(mo_oxygen_tolerance(c("Klebsiella pneumoniae", "Clostridioides
|
||||
c("aerobe", "anaerobe"))
|
||||
|
||||
expect_equal(as.character(table(mo_pathogenicity(example_isolates$mo))),
|
||||
c("1561", "422", "1", "16"))
|
||||
c("1874", "109", "1", "16"))
|
||||
|
||||
expect_equal(mo_ref("Escherichia coli"), "Castellani et al., 1919")
|
||||
expect_equal(mo_authors("Escherichia coli"), "Castellani et al.")
|
||||
@ -129,9 +129,12 @@ for (l in AMR:::LANGUAGES_SUPPORTED[-1]) {
|
||||
|
||||
# test languages
|
||||
expect_error(mo_gramstain("Escherichia coli", language = "UNKNOWN"))
|
||||
dutch <- suppressWarnings(mo_name(microorganisms$fullname[which(microorganisms$fullname %unlike% "unknown|coagulase|Fungi|[(]class[)]|[{]")], language = "nl", keep_synonyms = TRUE)) # should be transformable to English again
|
||||
expect_identical(suppressWarnings(mo_name(dutch, language = NULL, keep_synonyms = TRUE)),
|
||||
microorganisms$fullname[which(microorganisms$fullname %unlike% "unknown|coagulase|Fungi|[(]class[)]|[{]")]) # gigantic test - will run ALL names
|
||||
fullnames <- microorganisms$fullname[which(microorganisms$fullname %unlike% "unknown|coagulase|Fungi|[(]class[)]|[{]")]
|
||||
to_dutch <- suppressWarnings(mo_name(fullnames, language = "nl", keep_synonyms = TRUE))
|
||||
back_to_english <- suppressWarnings(mo_name(dutch, language = NULL, keep_synonyms = TRUE))
|
||||
diffs <- paste0('"', fullnames[fullnames != back_to_english], '"', collapse = ", ")
|
||||
expect_identical(fullnames, back_to_english, info = diffs) # gigantic test - will run ALL names
|
||||
|
||||
|
||||
# manual property function
|
||||
expect_error(mo_property("Escherichia coli", property = c("genus", "fullname")))
|
||||
|
||||
@ -133,7 +133,7 @@ The \code{\link[=ab_selector]{ab_selector()}} function can be used to internally
|
||||
|
||||
The \code{\link[=administrable_per_os]{administrable_per_os()}} and \code{\link[=administrable_iv]{administrable_iv()}} functions also rely on the \link{antibiotics} data set - antibiotic columns will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the \link{antibiotics} data set.
|
||||
|
||||
The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of \emph{E. coli} and \emph{K. pneumoniae} and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies \href{https://www.eucast.org/expert_rules_and_expected_phenotypes/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021) to determine intrinsic resistance, using the \code{\link[=eucast_rules]{eucast_rules()}} function internally. Because of this determination, this function is quite slow in terms of performance.
|
||||
The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of \emph{E. coli} and \emph{K. pneumoniae} and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021) to determine intrinsic resistance, using the \code{\link[=eucast_rules]{eucast_rules()}} function internally. Because of this determination, this function is quite slow in terms of performance.
|
||||
}
|
||||
\section{Full list of supported (antibiotic) classes}{
|
||||
|
||||
|
||||
@ -201,6 +201,10 @@ as.mo(c(
|
||||
"Ureaplazma urealitycium"
|
||||
))
|
||||
|
||||
# input will get cleaned up with the input given in the `cleaning_regex` argument,
|
||||
# which defaults to `mo_cleaning_regex()`:
|
||||
cat(mo_cleaning_regex(), "\n")
|
||||
|
||||
as.mo("Streptococcus group A")
|
||||
|
||||
as.mo("S. epidermidis") # will remain species: B_STPHY_EPDR
|
||||
|
||||
@ -94,7 +94,7 @@ sir_interpretation_history(clean = FALSE)
|
||||
|
||||
\item{conserve_capped_values}{a \link{logical} to indicate that MIC values starting with \code{">"} (but not \code{">="}) must always return "R" , and that MIC values starting with \code{"<"} (but not \code{"<="}) must always return "S"}
|
||||
|
||||
\item{add_intrinsic_resistance}{\emph{(only useful when using a EUCAST guideline)} a \link{logical} to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in \emph{Klebsiella} species. Determination is based on the \link{intrinsic_resistant} data set, that itself is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).}
|
||||
\item{add_intrinsic_resistance}{\emph{(only useful when using a EUCAST guideline)} a \link{logical} to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in \emph{Klebsiella} species. Determination is based on the \link{intrinsic_resistant} data set, that itself is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).}
|
||||
|
||||
\item{reference_data}{a \link{data.frame} to be used for interpretation, which defaults to the \link{clinical_breakpoints} data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the \link{clinical_breakpoints} data set (same column names and column types). Please note that the \code{guideline} argument will be ignored when \code{reference_data} is manually set.}
|
||||
|
||||
@ -171,7 +171,7 @@ After using \code{\link[=as.sir]{as.sir()}}, you can use the \code{\link[=eucast
|
||||
|
||||
\subsection{Machine-Readable Clinical Breakpoints}{
|
||||
|
||||
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 28 454 rows and 12 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
|
||||
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 28 885 rows and 12 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
|
||||
}
|
||||
|
||||
\subsection{Other}{
|
||||
@ -185,7 +185,7 @@ The function \code{\link[=is_sir_eligible]{is_sir_eligible()}} returns \code{TRU
|
||||
}
|
||||
\section{Interpretation of SIR}{
|
||||
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr/}):
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr}):
|
||||
\itemize{
|
||||
\item \strong{S - Susceptible, standard dosing regimen}\cr
|
||||
A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{clinical_breakpoints}
|
||||
\title{Data Set with Clinical Breakpoints for SIR Interpretation}
|
||||
\format{
|
||||
A \link[tibble:tibble]{tibble} with 28 454 observations and 12 variables:
|
||||
A \link[tibble:tibble]{tibble} with 28 885 observations and 12 variables:
|
||||
\itemize{
|
||||
\item \code{guideline}\cr Name of the guideline
|
||||
\item \code{type}\cr Breakpoint type, either "ECOFF", "animal", or "human"
|
||||
|
||||
@ -71,7 +71,7 @@ The function \code{\link[=count_df]{count_df()}} takes any variable from \code{d
|
||||
}
|
||||
\section{Interpretation of SIR}{
|
||||
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr/}):
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr}):
|
||||
\itemize{
|
||||
\item \strong{S - Susceptible, standard dosing regimen}\cr
|
||||
A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
|
||||
@ -18,7 +18,7 @@ intrinsic_resistant
|
||||
Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
|
||||
}
|
||||
\details{
|
||||
This data set is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).
|
||||
This data set is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).
|
||||
\subsection{Direct download}{
|
||||
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
|
||||
@ -174,7 +174,7 @@ Amikacin (\code{AMK}, \href{https://www.whocc.no/atc_ddd_index/?code=J01GB06&sho
|
||||
|
||||
\section{Interpretation of SIR}{
|
||||
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr/}):
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr}):
|
||||
\itemize{
|
||||
\item \strong{S - Susceptible, standard dosing regimen}\cr
|
||||
A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
|
||||
@ -17,7 +17,7 @@ A \link[tibble:tibble]{tibble} with 444 observations and 4 variables:
|
||||
microorganisms.groups
|
||||
}
|
||||
\description{
|
||||
A data set containing species groups and microbiological complexes, which are used in \link[=clinial_breakpoints]{the clinical breakpoints table}.
|
||||
A data set containing species groups and microbiological complexes, which are used in \link[=clinical_breakpoints]{the clinical breakpoints table}.
|
||||
}
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
|
||||
@ -327,7 +327,7 @@ Determination of the Gram stain (\code{\link[=mo_gramstain]{mo_gramstain()}}) wi
|
||||
|
||||
Determination of yeasts (\code{\link[=mo_is_yeast]{mo_is_yeast()}}) will be based on the taxonomic kingdom and class. \emph{Budding yeasts} are fungi of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). \emph{True yeasts} are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes, the function will return \code{TRUE}. It returns \code{FALSE} otherwise (or \code{NA} when the input is \code{NA} or the MO code is \code{UNKNOWN}).
|
||||
|
||||
Determination of intrinsic resistance (\code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}}) will be based on the \link{intrinsic_resistant} data set, which is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021). The \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} function can be vectorised over both argument \code{x} (input for microorganisms) and \code{ab} (input for antibiotics).
|
||||
Determination of intrinsic resistance (\code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}}) will be based on the \link{intrinsic_resistant} data set, which is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021). The \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} function can be vectorised over both argument \code{x} (input for microorganisms) and \code{ab} (input for antibiotics).
|
||||
|
||||
Determination of bacterial oxygen tolerance (\code{\link[=mo_oxygen_tolerance]{mo_oxygen_tolerance()}}) will be based on BacDive, see \emph{Source}. The function \code{\link[=mo_is_anaerobic]{mo_is_anaerobic()}} only returns \code{TRUE} if the oxygen tolerance is \code{"anaerobe"}, indicting an obligate anaerobic species or genus. It always returns \code{FALSE} for species outside the taxonomic kingdom of Bacteria.
|
||||
|
||||
|
||||
12
man/plot.Rd
12
man/plot.Rd
@ -24,6 +24,8 @@
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...
|
||||
)
|
||||
|
||||
@ -38,6 +40,8 @@
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...
|
||||
)
|
||||
|
||||
@ -54,6 +58,8 @@
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...
|
||||
)
|
||||
|
||||
@ -68,6 +74,8 @@
|
||||
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
|
||||
language = get_AMR_locale(),
|
||||
expand = TRUE,
|
||||
include_PKPD = getOption("AMR_include_PKPD", TRUE),
|
||||
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
|
||||
...
|
||||
)
|
||||
|
||||
@ -113,6 +121,10 @@
|
||||
|
||||
\item{expand}{a \link{logical} to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.}
|
||||
|
||||
\item{include_PKPD}{a \link{logical} to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}. Can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_include_PKPD}}.}
|
||||
|
||||
\item{breakpoint_type}{the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is \code{"human"}, which can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_breakpoint_type}}.}
|
||||
|
||||
\item{...}{arguments passed on to methods}
|
||||
}
|
||||
\value{
|
||||
|
||||
@ -146,7 +146,7 @@ Using \code{only_all_tested} has no impact when only using one antibiotic as inp
|
||||
|
||||
\section{Interpretation of SIR}{
|
||||
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr/}):
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr}):
|
||||
\itemize{
|
||||
\item \strong{S - Susceptible, standard dosing regimen}\cr
|
||||
A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
|
||||
@ -112,7 +112,7 @@ Valid options for the statistical model (argument \code{model}) are:
|
||||
}
|
||||
\section{Interpretation of SIR}{
|
||||
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr/}):
|
||||
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (\url{https://www.eucast.org/newsiandr}):
|
||||
\itemize{
|
||||
\item \strong{S - Susceptible, standard dosing regimen}\cr
|
||||
A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user