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Merge remote-tracking branch 'origin/main' into breakpoints
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dr. M.S. (Matthijs) Berends committed
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@@ -57,7 +57,8 @@ jobs:
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# - {os: ubuntu-latest, r: '3.3', allowfail: false}
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# - {os: ubuntu-latest, r: '3.2', allowfail: false}
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# - {os: ubuntu-latest, r: '3.1', allowfail: false}
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- {os: ubuntu-latest, r: '3.0', allowfail: false}
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# R 3.0 cannot be resolved for ubuntu-latest (Ubuntu 26.04), so use Ubuntu 24.04
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- {os: ubuntu-24.04, r: '3.0', allowfail: false}
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env:
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R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
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@@ -48,7 +48,8 @@ jobs:
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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# Python 3.9 is not available for ubuntu-latest (Ubuntu 26.04)
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python-version: '3.12'
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- name: Install build dependencies
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run: |
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+2
-2
@@ -1,6 +1,6 @@
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Package: AMR
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Version: 3.1.0.9006
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Date: 2026-10-23
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Version: 3.1.0.9014
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Date: 2026-10-30
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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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@@ -1,4 +1,16 @@
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# AMR 3.1.0.9006
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# AMR 3.1.0.9014
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This version fixes a critical bug in WISCA that biased all coverage estimates of `wisca()` and `antibiogram(..., wisca = TRUE)` downwards since AMR 3.0.0. Users are strongly advised to re-run their WISCA analyses.
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### Changed
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* WISCA (`wisca()` and `antibiogram(..., wisca = TRUE)`)
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* Fixed a critical bug that systematically underestimated the coverage of every WISCA since AMR 3.0.0. Pathogens absent from a syndromic group, and pathogens without any test result for a regimen, were still included in the model with only prior information: each added a prior weight of 1 to the pathogen incidence, and untested pathogens kept their full incidence weight while counting as about 50% susceptible. The bias was largest for stratified analyses (e.g., up to 14 percentage points for meropenem in random strata of `example_isolates`). Now, only pathogens that occur in a group and have at least one test result for the regimen are included. Please re-run all WISCA analyses (#337)
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* `retrieve_wisca_parameters()` now returns only the parameters actually used in the model, and `n_total` in the `long_numeric` attribute now counts only isolates of the pathogens included for that regimen
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* A new warning is given when the prior weight of 1 per pathogen (Bielicki *et al.*, 2016) accounts for more than 5% of the pathogen incidence, i.e., with rare pathogens or small groups, as this can strongly lower the estimated coverage; consider `top_n_microorganisms()` or fewer and larger groups in that case
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* Microorganism coercion (`as.mo()`)
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* Fixed the misaligned arrows in the message on taxonomically renamed microorganisms in colour-supporting consoles, for old names whose author reference lacks "*et al.*" (#336)
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* `intrinsic_resistant` data set
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* Species groups and complexes are now listed for every antimicrobial to which all their species are intrinsically resistant. This adds 62 bug-drug combinations for the *Burkholderia cepacia* complex, coryneform bacteria, the *Enterobacter cloacae* complex (e.g., amoxicillin and amoxicillin/clavulanic acid), the *Klebsiella pneumoniae* complex, and coagulase-negative staphylococci, which affects `mo_is_intrinsic_resistant()`, `not_intrinsic_resistant()`, and the priors of `wisca()` (#338)
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### Other
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* The manual index on the website was rewritten to improve findability, with sections ordered by task and a filter field
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+29
-6
@@ -156,6 +156,10 @@
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#'
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#' **Prior distributions:** Pathogen incidence uses a non-informative \eqn{Dirichlet(1, 1, \ldots, 1)} prior. Susceptibility proportions use the Jeffreys prior, \eqn{\beta(0.5, 0.5)}, except for pathogen-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. 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]))`.
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#'
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#' **Pathogens included in the model:** For each regimen (and group), only pathogens that occur in the data and have at least one test result for the regimen are included. Pathogens without any test result for a regimen do not contribute to its coverage, nor to its `n_total` in `long_numeric`. Use [retrieve_wisca_parameters()] to get the parameters that were used.
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#'
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#' **Rare pathogens and small groups:** Since every pathogen receives a prior weight of 1 in the Dirichlet distribution, the prior weighs heavily when there are few isolates relative to the number of pathogens, i.e., with pathogens found only once or a few times, or in small groups. This pulls the incidence towards a uniform distribution, and the very uncertain susceptibility of rare pathogens can then strongly lower the estimated coverage, without reflecting the clinical expectation. A warning is therefore given when this prior weight accounts for more than 5% of the pathogen incidence in any regimen (or group). Consider limiting the analysis to the most common pathogens using [top_n_microorganisms()], or using fewer and larger groups.
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#'
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#' **Interpreting the output:** Overlapping credible intervals between regimens indicate no significant difference in coverage; if a narrower-spectrum regimen overlaps with a broader one, the narrower-spectrum option may be preferred on stewardship grounds. Non-overlapping intervals indicate a clinically meaningful difference. For small sample sizes, consider pooling data from multiple sites to improve precision, provided pathogen distributions are sufficiently similar (Bielicki *et al.*, 2016).
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#'
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#' For the full mathematical derivation and worked examples, see the [WISCA vignette](https://amr-for-r.org/articles/WISCA.html).
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@@ -182,12 +186,13 @@
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#'
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#' # basic WISCA: empirical coverage per regimen, weighted by pathogen
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#' # incidence, with 95% credible intervals
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#' wisca(example_isolates,
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#' # (10 most common species, since rare species can distort coverage estimates)
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#' wisca(top_n_microorganisms(example_isolates, n = 10),
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#' antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN")
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#' )
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#'
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#' # equivalent using antibiogram():
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#' antibiogram(example_isolates,
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#' antibiogram(top_n_microorganisms(example_isolates, n = 10),
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#' antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN"),
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#' wisca = TRUE
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#' )
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@@ -272,7 +277,7 @@
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#'
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#' # Print the output for R Markdown / Quarto -----------------------------
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#'
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#' ureido <- wisca(example_isolates,
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#' ureido <- wisca(top_n_microorganisms(example_isolates, n = 10),
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#' antimicrobials = ureidopenicillins(),
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#' syndromic_group = "ward"
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#' )
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@@ -290,7 +295,7 @@
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#' antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
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#' mo_transform = "gramstain"
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#' )
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#' ab2 <- wisca(example_isolates,
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#' ab2 <- wisca(top_n_microorganisms(example_isolates, n = 10),
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#' antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
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#' syndromic_group = "ward"
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#' )
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@@ -744,7 +749,8 @@ antibiogram.default <- function(x,
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coverage = NA_real_,
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lower_ci = NA_real_,
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upper_ci = NA_real_,
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n_total = sum(n_total, na.rm = TRUE),
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# only pathogens with test results for the regimen enter the WISCA model, see below
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n_total = sum(n_total[which(n_tested > 0)]),
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n_tested = sum(n_tested, na.rm = TRUE),
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n_susceptible = sum(n_susceptible, na.rm = TRUE)
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)
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@@ -761,7 +767,24 @@ antibiogram.default <- function(x,
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out_wisca$group <- out_wisca$ab
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}
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wisca_parameters <- out
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# only pathogens that occur in the group (n_total > 0) and have at least one test result for the regimen (n_tested > 0) may contribute to WISCA; otherwise, absent or untested pathogens would enter with prior-only parameters and bias the coverage downwards (#337)
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wisca_parameters <- out[which(out$n_total > 0 & out$n_tested > 0), , drop = FALSE]
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# the Dirichlet prior of 1 per pathogen (Bielicki et al., 2016) weighs heavily when many pathogens are rare
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prior_share <- vapply(
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split(wisca_parameters$n_total, as.character(wisca_parameters$group)),
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function(n) length(n) / (length(n) + sum(n)),
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FUN.VALUE = numeric(1)
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)
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if (any(prior_share > 0.05)) {
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warning_(
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"In ", sum(prior_share > 0.05), " of ", length(prior_share), " WISCA regimen(s) or group(s), the prior weight of 1 per pathogen accounts for more than 5% of the pathogen incidence (up to ", round(max(prior_share) * 100, 1), "%), since there are few isolates relative to the number of pathogens, i.e., rare pathogens or a small group. ",
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"Because WISCA gives every pathogen a prior weight of 1 (Bielicki et al., 2016), this can strongly lower the estimated coverage without reflecting the clinical expectation. ",
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"Consider limiting the analysis to the most common pathogens with {.help [{.fun top_n_microorganisms}](AMR::top_n_microorganisms)}, or using fewer and larger groups.",
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call = FALSE
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)
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}
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wisca_draws <- list()
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wisca_components <- list()
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@@ -1144,7 +1144,10 @@ print.mo_renamed <- function(x, extra_txt = "", n = 25, ...) {
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rows <- seq_len(min(NROW(x), n))
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message_("The following microorganism", ifelse(NROW(x) > 1, "s were", " was"), " taxonomically renamed", extra_txt, ":")
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old_format <- format(paste0(font_italic(x$old[rows], collapse = NULL), x$ref_old[rows])) # format() will set trailing spaces for textual alignment
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old_format <- paste0(font_italic(x$old[rows], collapse = NULL), x$ref_old[rows])
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# pad based on the visible width, since format() counts ANSI codes as characters
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visible_width <- nchar(font_stripstyle(old_format), type = "width")
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old_format <- paste0(old_format, strrep(" ", max(visible_width) - visible_width))
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old_format <- gsub(" ", "\u00a0", old_format, fixed = TRUE)
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for (old_tax in rows) {
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message_("\u00a0\u00a0", AMR_env$bullet_icon, " ", old_format[old_tax], " -> ", font_italic(x$new[old_tax]), x$ref_new[old_tax], as_note = FALSE)
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@@ -58,6 +58,23 @@ int_resis3 <- int_resis2[which(!int_resis2$ab %in% untreatable), ]
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int_resis3$ab <- as.ab(int_resis3$ab)
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int_resis3
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# add species groups (e.g., complexes) for every antimicrobial to which all their species are intrinsically resistant (#338)
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groups <- microorganisms.groups[which(microorganisms$rank[match(microorganisms.groups$mo, microorganisms$mo)] == "species"), , drop = FALSE]
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int_resis_keys <- paste(int_resis3$mo, int_resis3$ab)
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groups_species <- split(as.character(groups$mo), as.character(groups$mo_group))
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int_resis_groups <- do.call(rbind, Map(function(group, species) {
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abx <- unique(as.character(int_resis3$ab[int_resis3$mo %in% species]))
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abx <- abx[vapply(abx, function(ab) all(paste(species, ab) %in% int_resis_keys), logical(1))]
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data.frame(mo = rep(group, length(abx)), ab = abx, stringsAsFactors = FALSE)
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}, names(groups_species), groups_species))
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int_resis_groups <- int_resis_groups[which(!paste(int_resis_groups$mo, int_resis_groups$ab) %in% int_resis_keys), , drop = FALSE]
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int_resis_groups$mo <- as.mo(int_resis_groups$mo, keep_synonyms = TRUE)
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int_resis_groups$ab <- as.ab(int_resis_groups$ab)
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int_resis3 <- bind_rows(int_resis3, int_resis_groups)
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# keep the order of the microorganisms and antimicrobials data sets
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int_resis3 <- int_resis3[order(match(int_resis3$mo, microorganisms$mo), match(int_resis3$ab, antimicrobials$ab)), , drop = FALSE]
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rownames(int_resis3) <- NULL
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all(int_resis3$mo %in% microorganisms$mo)
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all(int_resis3$ab %in% antimicrobials$ab)
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@@ -31323,8 +31323,19 @@
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"Burkholderia cepacia" "Zorbamycin"
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"Burkholderia cepacia complex" "Acetylmidecamycin"
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"Burkholderia cepacia complex" "Acetylspiramycin"
|
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"Burkholderia cepacia complex" "Amikacin"
|
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"Burkholderia cepacia complex" "Amikacin/fosfomycin"
|
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"Burkholderia cepacia complex" "Amoxicillin"
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"Burkholderia cepacia complex" "Amoxicillin/clavulanic acid"
|
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"Burkholderia cepacia complex" "Ampicillin"
|
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"Burkholderia cepacia complex" "Ampicillin/sulbactam"
|
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"Burkholderia cepacia complex" "Apramycin"
|
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"Burkholderia cepacia complex" "Arbekacin"
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||||
"Burkholderia cepacia complex" "Astromicin"
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"Burkholderia cepacia complex" "Avoparcin"
|
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"Burkholderia cepacia complex" "Azithromycin"
|
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"Burkholderia cepacia complex" "Aztreonam"
|
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"Burkholderia cepacia complex" "Bekanamycin"
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"Burkholderia cepacia complex" "Benzylpenicillin"
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"Burkholderia cepacia complex" "Bleomycin"
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"Burkholderia cepacia complex" "Cadazolid"
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@@ -31342,54 +31353,87 @@
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"Burkholderia cepacia complex" "Cefmetazole"
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"Burkholderia cepacia complex" "Cefonicid"
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"Burkholderia cepacia complex" "Ceforanide"
|
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"Burkholderia cepacia complex" "Cefotaxime"
|
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"Burkholderia cepacia complex" "Cefotetan"
|
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"Burkholderia cepacia complex" "Cefotiam"
|
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"Burkholderia cepacia complex" "Cefoxitin"
|
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"Burkholderia cepacia complex" "Cefprozil"
|
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"Burkholderia cepacia complex" "Cefroxadine"
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"Burkholderia cepacia complex" "Ceftezole"
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"Burkholderia cepacia complex" "Ceftriaxone"
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"Burkholderia cepacia complex" "Cefuroxime"
|
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"Burkholderia cepacia complex" "Cefuroxime axetil"
|
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"Burkholderia cepacia complex" "Cephradine"
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"Burkholderia cepacia complex" "Chloramphenicol"
|
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"Burkholderia cepacia complex" "Ciprofloxacin"
|
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"Burkholderia cepacia complex" "Clarithromycin"
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"Burkholderia cepacia complex" "Clindamycin"
|
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"Burkholderia cepacia complex" "Colistin"
|
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"Burkholderia cepacia complex" "Cycloserine"
|
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"Burkholderia cepacia complex" "Dalbavancin"
|
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"Burkholderia cepacia complex" "Dibekacin"
|
||||
"Burkholderia cepacia complex" "Dirithromycin"
|
||||
"Burkholderia cepacia complex" "Ertapenem"
|
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"Burkholderia cepacia complex" "Erythromycin"
|
||||
"Burkholderia cepacia complex" "Flurithromycin"
|
||||
"Burkholderia cepacia complex" "Fosfomycin"
|
||||
"Burkholderia cepacia complex" "Framycetin"
|
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"Burkholderia cepacia complex" "Fusidic acid"
|
||||
"Burkholderia cepacia complex" "Gamithromycin"
|
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"Burkholderia cepacia complex" "Gentamicin"
|
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"Burkholderia cepacia complex" "Habekacin"
|
||||
"Burkholderia cepacia complex" "Hygromycin"
|
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"Burkholderia cepacia complex" "Isepamicin"
|
||||
"Burkholderia cepacia complex" "Josamycin"
|
||||
"Burkholderia cepacia complex" "Kanamycin"
|
||||
"Burkholderia cepacia complex" "Kanamycin/cephalexin"
|
||||
"Burkholderia cepacia complex" "Kasugamycin"
|
||||
"Burkholderia cepacia complex" "Kitasamycin"
|
||||
"Burkholderia cepacia complex" "Lincomycin"
|
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"Burkholderia cepacia complex" "Linezolid"
|
||||
"Burkholderia cepacia complex" "Loracarbef"
|
||||
"Burkholderia cepacia complex" "Meleumycin"
|
||||
"Burkholderia cepacia complex" "Micronomicin"
|
||||
"Burkholderia cepacia complex" "Midecamycin"
|
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"Burkholderia cepacia complex" "Miocamycin"
|
||||
"Burkholderia cepacia complex" "Nafithromycin"
|
||||
"Burkholderia cepacia complex" "Neomycin"
|
||||
"Burkholderia cepacia complex" "Netilmicin"
|
||||
"Burkholderia cepacia complex" "Norvancomycin"
|
||||
"Burkholderia cepacia complex" "Oleandomycin"
|
||||
"Burkholderia cepacia complex" "Oritavancin"
|
||||
"Burkholderia cepacia complex" "Ostreogrycin"
|
||||
"Burkholderia cepacia complex" "Pentisomicin"
|
||||
"Burkholderia cepacia complex" "Piperacillin"
|
||||
"Burkholderia cepacia complex" "Piperacillin/tazobactam"
|
||||
"Burkholderia cepacia complex" "Pirlimycin"
|
||||
"Burkholderia cepacia complex" "Plazomicin"
|
||||
"Burkholderia cepacia complex" "Polymyxin B"
|
||||
"Burkholderia cepacia complex" "Primycin"
|
||||
"Burkholderia cepacia complex" "Pristinamycin"
|
||||
"Burkholderia cepacia complex" "Propikacin"
|
||||
"Burkholderia cepacia complex" "Quinupristin/dalfopristin"
|
||||
"Burkholderia cepacia complex" "Ramoplanin"
|
||||
"Burkholderia cepacia complex" "Ribostamycin"
|
||||
"Burkholderia cepacia complex" "Rifampicin"
|
||||
"Burkholderia cepacia complex" "Rokitamycin"
|
||||
"Burkholderia cepacia complex" "Roxithromycin"
|
||||
"Burkholderia cepacia complex" "Sisomicin"
|
||||
"Burkholderia cepacia complex" "Solithromycin"
|
||||
"Burkholderia cepacia complex" "Spiramycin"
|
||||
"Burkholderia cepacia complex" "Streptoduocin"
|
||||
"Burkholderia cepacia complex" "Streptomycin"
|
||||
"Burkholderia cepacia complex" "Tedizolid"
|
||||
"Burkholderia cepacia complex" "Teicoplanin"
|
||||
"Burkholderia cepacia complex" "Telavancin"
|
||||
"Burkholderia cepacia complex" "Telithromycin"
|
||||
"Burkholderia cepacia complex" "Thiacetazone"
|
||||
"Burkholderia cepacia complex" "Ticarcillin"
|
||||
"Burkholderia cepacia complex" "Ticarcillin/clavulanic acid"
|
||||
"Burkholderia cepacia complex" "Tildipirosin"
|
||||
"Burkholderia cepacia complex" "Tilmicosin"
|
||||
"Burkholderia cepacia complex" "Tobramycin"
|
||||
"Burkholderia cepacia complex" "Trimethoprim"
|
||||
"Burkholderia cepacia complex" "Troleandomycin"
|
||||
"Burkholderia cepacia complex" "Tulathromycin"
|
||||
"Burkholderia cepacia complex" "Tylosin"
|
||||
@@ -65522,6 +65566,7 @@
|
||||
"Cnuibacter physcomitrellae" "Polymyxin B"
|
||||
"Cnuibacter physcomitrellae" "Temocillin"
|
||||
"Coagulase-negative Staphylococcus (CoNS)" "Aztreonam"
|
||||
"Coagulase-negative Staphylococcus (CoNS)" "Ceftazidime"
|
||||
"Coagulase-negative Staphylococcus (CoNS)" "Colistin"
|
||||
"Coagulase-negative Staphylococcus (CoNS)" "Nalidixic acid"
|
||||
"Coagulase-negative Staphylococcus (CoNS)" "Polymyxin B"
|
||||
@@ -67762,6 +67807,11 @@
|
||||
"Corynebacterium zhongnanshanii" "Nalidixic acid"
|
||||
"Corynebacterium zhongnanshanii" "Polymyxin B"
|
||||
"Corynebacterium zhongnanshanii" "Temocillin"
|
||||
"Coryneform bacterium" "Aztreonam"
|
||||
"Coryneform bacterium" "Colistin"
|
||||
"Coryneform bacterium" "Nalidixic acid"
|
||||
"Coryneform bacterium" "Polymyxin B"
|
||||
"Coryneform bacterium" "Temocillin"
|
||||
"Cosenzaea" "Acetylmidecamycin"
|
||||
"Cosenzaea" "Acetylspiramycin"
|
||||
"Cosenzaea" "Avoparcin"
|
||||
@@ -76494,11 +76544,20 @@
|
||||
"Enterobacter cloacae cloacae" "Zorbamycin"
|
||||
"Enterobacter cloacae complex" "Acetylmidecamycin"
|
||||
"Enterobacter cloacae complex" "Acetylspiramycin"
|
||||
"Enterobacter cloacae complex" "Amoxicillin"
|
||||
"Enterobacter cloacae complex" "Amoxicillin/clavulanic acid"
|
||||
"Enterobacter cloacae complex" "Ampicillin"
|
||||
"Enterobacter cloacae complex" "Ampicillin/sulbactam"
|
||||
"Enterobacter cloacae complex" "Avoparcin"
|
||||
"Enterobacter cloacae complex" "Azithromycin"
|
||||
"Enterobacter cloacae complex" "Benzylpenicillin"
|
||||
"Enterobacter cloacae complex" "Bleomycin"
|
||||
"Enterobacter cloacae complex" "Cadazolid"
|
||||
"Enterobacter cloacae complex" "Cefadroxil"
|
||||
"Enterobacter cloacae complex" "Cefalexin"
|
||||
"Enterobacter cloacae complex" "Cefalotin"
|
||||
"Enterobacter cloacae complex" "Cefazolin"
|
||||
"Enterobacter cloacae complex" "Cefoxitin"
|
||||
"Enterobacter cloacae complex" "Clarithromycin"
|
||||
"Enterobacter cloacae complex" "Clindamycin"
|
||||
"Enterobacter cloacae complex" "Cycloserine"
|
||||
@@ -102515,6 +102574,8 @@
|
||||
"Klebsiella pneumoniae" "Zorbamycin"
|
||||
"Klebsiella pneumoniae complex" "Acetylmidecamycin"
|
||||
"Klebsiella pneumoniae complex" "Acetylspiramycin"
|
||||
"Klebsiella pneumoniae complex" "Amoxicillin"
|
||||
"Klebsiella pneumoniae complex" "Ampicillin"
|
||||
"Klebsiella pneumoniae complex" "Avoparcin"
|
||||
"Klebsiella pneumoniae complex" "Azithromycin"
|
||||
"Klebsiella pneumoniae complex" "Benzylpenicillin"
|
||||
@@ -102556,6 +102617,7 @@
|
||||
"Klebsiella pneumoniae complex" "Telavancin"
|
||||
"Klebsiella pneumoniae complex" "Telithromycin"
|
||||
"Klebsiella pneumoniae complex" "Thiacetazone"
|
||||
"Klebsiella pneumoniae complex" "Ticarcillin"
|
||||
"Klebsiella pneumoniae complex" "Tildipirosin"
|
||||
"Klebsiella pneumoniae complex" "Tilmicosin"
|
||||
"Klebsiella pneumoniae complex" "Troleandomycin"
|
||||
|
||||
Binary file not shown.
@@ -1 +1 @@
|
||||
b32da210529081a8e0ab8874956739d1
|
||||
e17c4cf3ac9495a9ed832b64da96770a
|
||||
Binary file not shown.
@@ -70,15 +70,15 @@ With the help of contributors from all corners of the world, the `AMR` package i
|
||||
One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`.
|
||||
|
||||
```{r}
|
||||
# AMR works great with dplyr, but it's not required or neccesary
|
||||
# AMR works great with dplyr, but it's not required or necessary
|
||||
library(AMR)
|
||||
library(dplyr, warn.conflicts = FALSE)
|
||||
|
||||
example_isolates %>%
|
||||
mutate(bacteria = mo_fullname()) %>%
|
||||
example_isolates |>
|
||||
mutate(bacteria = mo_fullname()) |>
|
||||
# filtering functions for microorganisms:
|
||||
filter(mo_is_gram_negative(),
|
||||
mo_is_intrinsic_resistant(ab = "cefotax")) %>%
|
||||
mo_is_intrinsic_resistant(ab = "cefotax")) |>
|
||||
# antimicrobial selectors:
|
||||
select(bacteria,
|
||||
aminoglycosides(),
|
||||
@@ -94,44 +94,46 @@ The `AMR` package supports four types of antibiograms, with support for `r lengt
|
||||
**For empirical therapy guidance (i.e., coverage estimates), use WISCA** (Weighted-Incidence Syndromic Combination Antibiogram). When a clinician starts empirical treatment, the causative pathogen is unknown. The relevant question is not *"what percentage of E. coli is susceptible?"* but *"what is the probability that this regimen will cover whatever pathogen is causing the infection?"*. WISCA answers that question directly, weighting susceptibility by pathogen incidence and providing credible intervals via Bayesian simulation. See `vignette("WISCA")` for the full explanation.
|
||||
|
||||
```{r}
|
||||
wisca(example_isolates,
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
example_isolates |>
|
||||
top_n_microorganisms(n = 10) |> # 10 most common species
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
```
|
||||
|
||||
WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:
|
||||
|
||||
```{r}
|
||||
wisca(example_isolates,
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
example_isolates |>
|
||||
top_n_microorganisms(n = 10) |>
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
```
|
||||
|
||||
**For AMR surveillance**, traditional antibiograms remain the right tool for tracking resistance per species over time:
|
||||
|
||||
```{r}
|
||||
antibiogram(example_isolates,
|
||||
mo_transform = "gramstain",
|
||||
antimicrobials = c("AMC", carbapenems(), "TZP"))
|
||||
example_isolates |>
|
||||
antibiogram(mo_transform = "gramstain",
|
||||
antimicrobials = c("AMC", carbapenems(), "TZP"))
|
||||
```
|
||||
|
||||
Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species:
|
||||
|
||||
```{r}
|
||||
antibiogram(example_isolates,
|
||||
mo_transform = "gramstain",
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
example_isolates |>
|
||||
antibiogram(mo_transform = "gramstain",
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
Like many other functions in this package, `antibiogram()` and `wisca()` come with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages that are often detected automatically based on system language:
|
||||
|
||||
```{r}
|
||||
antibiogram(example_isolates,
|
||||
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
language = "uk") # Ukrainian
|
||||
example_isolates |>
|
||||
antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
language = "uk") # Ukrainian
|
||||
```
|
||||
|
||||
### Interpreting and plotting MIC and SIR values
|
||||
@@ -176,9 +178,9 @@ ggplot(data.frame(mic = some_mic_values,
|
||||
For a manual approach, you can use the `resistance` or `susceptibility()` function:
|
||||
|
||||
```{r}
|
||||
example_isolates %>%
|
||||
example_isolates |>
|
||||
# group by ward:
|
||||
group_by(ward) %>%
|
||||
group_by(ward) |>
|
||||
# calculate AMR using resistance() for gentamicin and tobramycin
|
||||
# and get their 95% confidence intervals using sir_confidence_interval():
|
||||
summarise(across(c(GEN, TOB),
|
||||
@@ -192,9 +194,9 @@ Or use [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_s
|
||||
library(AMR)
|
||||
library(dplyr)
|
||||
|
||||
out <- example_isolates %>%
|
||||
out <- example_isolates |>
|
||||
# group by ward:
|
||||
group_by(ward) %>%
|
||||
group_by(ward) |>
|
||||
# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
|
||||
summarise(across(c(aminoglycosides(), polymyxins()),
|
||||
resistance))
|
||||
@@ -203,12 +205,12 @@ out
|
||||
|
||||
```{r}
|
||||
# transform the antibiotic columns to names:
|
||||
out %>% set_ab_names()
|
||||
out |> set_ab_names()
|
||||
```
|
||||
|
||||
```{r}
|
||||
# transform the antibiotic column to ATC codes:
|
||||
out %>% set_ab_names(property = "atc")
|
||||
out |> set_ab_names(property = "atc")
|
||||
```
|
||||
|
||||
## What else can you do with this package?
|
||||
|
||||
@@ -59,8 +59,8 @@ times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation
|
||||
in scientific research.
|
||||
|
||||
After installing this package, R knows [**~103 000 distinct microbial
|
||||
species**](./reference/microorganisms.html) (updated April 2026) and all
|
||||
[**~630 antimicrobial and antiviral
|
||||
species**](./reference/microorganisms.html) (updated October 2026) and
|
||||
all [**~630 antimicrobial and antiviral
|
||||
drugs**](./reference/antimicrobials.html) 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
|
||||
@@ -154,15 +154,15 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
|
||||
which work in base R, `dplyr` and `data.table`.
|
||||
|
||||
``` r
|
||||
# AMR works great with dplyr, but it's not required or neccesary
|
||||
# AMR works great with dplyr, but it's not required or necessary
|
||||
library(AMR)
|
||||
library(dplyr, warn.conflicts = FALSE)
|
||||
|
||||
example_isolates %>%
|
||||
mutate(bacteria = mo_fullname()) %>%
|
||||
example_isolates |>
|
||||
mutate(bacteria = mo_fullname()) |>
|
||||
# filtering functions for microorganisms:
|
||||
filter(mo_is_gram_negative(),
|
||||
mo_is_intrinsic_resistant(ab = "cefotax")) %>%
|
||||
mo_is_intrinsic_resistant(ab = "cefotax")) |>
|
||||
# antimicrobial selectors:
|
||||
select(bacteria,
|
||||
aminoglycosides(),
|
||||
@@ -217,38 +217,46 @@ credible intervals via Bayesian simulation. See `vignette("WISCA")` for
|
||||
the full explanation.
|
||||
|
||||
``` r
|
||||
wisca(example_isolates,
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
example_isolates |>
|
||||
top_n_microorganisms(n = 10) |> # 10 most common species
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
```
|
||||
|
||||
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:------------------------|:-------------------------------------|:-------------------------------------|
|
||||
| 70.2% (65-75.4%) | 93.6% (92-95.1%) | 89.9% (87-92.5%) |
|
||||
| 73% (67.7-79%) | 98.7% (98-99.2%) | 93.8% (90.5-96.6%) |
|
||||
|
||||
WISCA supports stratification by any clinical variable, so you can
|
||||
generate syndrome-specific or ward-specific coverage estimates:
|
||||
|
||||
``` r
|
||||
wisca(example_isolates,
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
example_isolates |>
|
||||
top_n_microorganisms(n = 10) |>
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
minimum = 10) # Recommended threshold: >=30
|
||||
#> Warning: In 3 of 9 WISCA regimen(s) or group(s), the prior weight of 1 per pathogen accounts for more than
|
||||
#> 5% of the pathogen incidence (up to 6.4%), since there are few isolates relative to the number of
|
||||
#> pathogens, i.e., rare pathogens or a small group. Because WISCA gives every pathogen a prior weight
|
||||
#> of 1 (Bielicki et al., 2016), this can strongly lower the estimated coverage without reflecting the
|
||||
#> clinical expectation. Consider limiting the analysis to the most common pathogens with
|
||||
#> `top_n_microorganisms()`, or using fewer and larger groups.
|
||||
```
|
||||
|
||||
| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
|
||||
| Clinical | 74.8% (69-80.3%) | 93.7% (92.1-95.1%) | 90.4% (86.8-93.3%) |
|
||||
| ICU | 56.7% (48.2-65.4%) | 86.6% (83.3-89.6%) | 83.1% (78.2-87.5%) |
|
||||
| Outpatient | 57.6% (46-69%) | 76.4% (69.8-81.7%) | 68% (57.7-77.5%) |
|
||||
| Clinical | 78.9% (72.8-85.6%) | 98.7% (97.8-99.3%) | 94.5% (90.3-97.6%) |
|
||||
| ICU | 57.3% (48.9-69.5%) | 97.5% (95.7-98.8%) | 91.7% (85.8-96.3%) |
|
||||
| Outpatient | 68% (49.4-88.5%) | 95.7% (90.7-98.7%) | 79.3% (62.7-93.8%) |
|
||||
|
||||
**For AMR surveillance**, traditional antibiograms remain the right tool
|
||||
for tracking resistance per species over time:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
mo_transform = "gramstain",
|
||||
antimicrobials = c("AMC", carbapenems(), "TZP"))
|
||||
example_isolates |>
|
||||
antibiogram(mo_transform = "gramstain",
|
||||
antimicrobials = c("AMC", carbapenems(), "TZP"))
|
||||
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
|
||||
```
|
||||
|
||||
@@ -261,9 +269,9 @@ Combination antibiograms show the additional coverage gained by adding a
|
||||
second agent, stratified by species:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
mo_transform = "gramstain",
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
example_isolates |>
|
||||
antibiogram(mo_transform = "gramstain",
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
@@ -276,11 +284,11 @@ come with support for 28 languages that are often detected automatically
|
||||
based on system language:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
language = "uk") # Ukrainian
|
||||
example_isolates |>
|
||||
antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
language = "uk") # Ukrainian
|
||||
```
|
||||
|
||||
| Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин |
|
||||
@@ -334,9 +342,9 @@ For a manual approach, you can use the `resistance` or
|
||||
`susceptibility()` function:
|
||||
|
||||
``` r
|
||||
example_isolates %>%
|
||||
example_isolates |>
|
||||
# group by ward:
|
||||
group_by(ward) %>%
|
||||
group_by(ward) |>
|
||||
# calculate AMR using resistance() for gentamicin and tobramycin
|
||||
# and get their 95% confidence intervals using sir_confidence_interval():
|
||||
summarise(across(c(GEN, TOB),
|
||||
@@ -358,9 +366,9 @@ to select a series of antibiotic columns:
|
||||
library(AMR)
|
||||
library(dplyr)
|
||||
|
||||
out <- example_isolates %>%
|
||||
out <- example_isolates |>
|
||||
# group by ward:
|
||||
group_by(ward) %>%
|
||||
group_by(ward) |>
|
||||
# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
|
||||
summarise(across(c(aminoglycosides(), polymyxins()),
|
||||
resistance))
|
||||
@@ -384,7 +392,7 @@ out
|
||||
|
||||
``` r
|
||||
# transform the antibiotic columns to names:
|
||||
out %>% set_ab_names()
|
||||
out |> set_ab_names()
|
||||
#> # A tibble: 3 × 6
|
||||
#> ward gentamicin tobramycin amikacin kanamycin colistin
|
||||
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
|
||||
@@ -395,7 +403,7 @@ out %>% set_ab_names()
|
||||
|
||||
``` r
|
||||
# transform the antibiotic column to ATC codes:
|
||||
out %>% set_ab_names(property = "atc")
|
||||
out |> set_ab_names(property = "atc")
|
||||
#> # A tibble: 3 × 6
|
||||
#> ward J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
|
||||
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
|
||||
@@ -427,7 +435,7 @@ data analysis. This package can be used for:
|
||||
organisms (MDRO) ([tutorial](./reference/mdro.html))
|
||||
- Calculating (empirical) susceptibility of both mono therapy and
|
||||
combination therapies ([tutorial](./articles/AMR.html))
|
||||
- Apply AMR functions in predictive modelling
|
||||
- Applying AMR functions in predictive modelling
|
||||
([tutorial](./articles/AMR_with_tidymodels.html))
|
||||
- Getting properties for any microorganism (like Gram stain, species,
|
||||
genus or family) ([manual](./reference/mo_property.html))
|
||||
@@ -435,7 +443,8 @@ data analysis. This package can be used for:
|
||||
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
|
||||
([manual](./reference/ab_property.html))
|
||||
- Plotting antimicrobial resistance ([tutorial](./articles/AMR.html))
|
||||
- Applying EUCAST expert rules ([manual](./reference/eucast_rules.html))
|
||||
- Applying EUCAST and CLSI interpretive rules
|
||||
([manual](./reference/interpretive_rules.html))
|
||||
- Getting SNOMED codes of a microorganism, or getting properties of a
|
||||
microorganism based on a SNOMED code
|
||||
([manual](./reference/mo_property.html))
|
||||
|
||||
+9
-4
@@ -268,6 +268,10 @@ It does so by combining two quantities: the relative incidence of each pathogen
|
||||
|
||||
\strong{Prior distributions:} Pathogen incidence uses a non-informative \eqn{Dirichlet(1, 1, \ldots, 1)} prior. Susceptibility proportions use the Jeffreys prior, \eqn{\beta(0.5, 0.5)}, except for pathogen-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. 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).
|
||||
|
||||
\strong{Pathogens included in the model:} For each regimen (and group), only pathogens that occur in the data and have at least one test result for the regimen are included. Pathogens without any test result for a regimen do not contribute to its coverage, nor to its \code{n_total} in \code{long_numeric}. Use \code{\link[=retrieve_wisca_parameters]{retrieve_wisca_parameters()}} to get the parameters that were used.
|
||||
|
||||
\strong{Rare pathogens and small groups:} Since every pathogen receives a prior weight of 1 in the Dirichlet distribution, the prior weighs heavily when there are few isolates relative to the number of pathogens, i.e., with pathogens found only once or a few times, or in small groups. This pulls the incidence towards a uniform distribution, and the very uncertain susceptibility of rare pathogens can then strongly lower the estimated coverage, without reflecting the clinical expectation. A warning is therefore given when this prior weight accounts for more than 5\% of the pathogen incidence in any regimen (or group). Consider limiting the analysis to the most common pathogens using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}, or using fewer and larger groups.
|
||||
|
||||
\strong{Interpreting the output:} Overlapping credible intervals between regimens indicate no significant difference in coverage; if a narrower-spectrum regimen overlaps with a broader one, the narrower-spectrum option may be preferred on stewardship grounds. Non-overlapping intervals indicate a clinically meaningful difference. For small sample sizes, consider pooling data from multiple sites to improve precision, provided pathogen distributions are sufficiently similar (Bielicki \emph{et al.}, 2016).
|
||||
|
||||
For the full mathematical derivation and worked examples, see the \href{https://amr-for-r.org/articles/WISCA.html}{WISCA vignette}.
|
||||
@@ -353,12 +357,13 @@ example_isolates
|
||||
|
||||
# basic WISCA: empirical coverage per regimen, weighted by pathogen
|
||||
# incidence, with 95\% credible intervals
|
||||
wisca(example_isolates,
|
||||
# (10 most common species, since rare species can distort coverage estimates)
|
||||
wisca(top_n_microorganisms(example_isolates, n = 10),
|
||||
antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN")
|
||||
)
|
||||
|
||||
# equivalent using antibiogram():
|
||||
antibiogram(example_isolates,
|
||||
antibiogram(top_n_microorganisms(example_isolates, n = 10),
|
||||
antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN"),
|
||||
wisca = TRUE
|
||||
)
|
||||
@@ -443,7 +448,7 @@ antibiogram(ex1,
|
||||
|
||||
# Print the output for R Markdown / Quarto -----------------------------
|
||||
|
||||
ureido <- wisca(example_isolates,
|
||||
ureido <- wisca(top_n_microorganisms(example_isolates, n = 10),
|
||||
antimicrobials = ureidopenicillins(),
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
@@ -461,7 +466,7 @@ ab1 <- antibiogram(example_isolates,
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
ab2 <- wisca(example_isolates,
|
||||
ab2 <- wisca(top_n_microorganisms(example_isolates, n = 10),
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
\alias{intrinsic_resistant}
|
||||
\title{Data Set Denoting Bacterial Intrinsic Resistance}
|
||||
\format{
|
||||
A \link[tibble:tibble]{tibble} with 307 684 observations and 3 variables:
|
||||
A \link[tibble:tibble]{tibble} with 307 746 observations and 3 variables:
|
||||
\itemize{
|
||||
\item \code{mo}\cr Microorganism ID which occurs in \code{\link[=microorganisms]{microorganisms$mo}}. Names can be retrieved using \code{\link[=mo_name]{mo_name()}}.
|
||||
\item \code{ab}\cr Antimicrobial ID which occurs in \code{\link[=antimicrobials]{antimicrobials$ab}}. Names can be retrieved using \code{\link[=ab_name]{ab_name()}}.
|
||||
|
||||
@@ -47,6 +47,44 @@ test_that("test-antibiogram.R", {
|
||||
wisca_known <- suppressMessages(wisca(example_isolates[example_isolates$mo != "UNKNOWN", ], antimicrobials = "GEN", simulations = 100, info = FALSE, guideline = "EUCAST"))
|
||||
expect_true("UNKNOWN" %in% attributes(wisca_all)$wisca_parameters$mo)
|
||||
expect_false(identical(as.data.frame(wisca_all), as.data.frame(wisca_known)))
|
||||
|
||||
# WISCA only includes pathogens that occur in a group and were tested for the regimen (#337)
|
||||
set.seed(337)
|
||||
wisca_data <- example_isolates[mo_domain(example_isolates$mo) == "Bacteria", ]
|
||||
wisca_data$grp <- sample(c("A", "B", "C"), nrow(wisca_data), replace = TRUE)
|
||||
wisca_strat <- suppressWarnings(wisca(wisca_data, antimicrobials = "MEM", syndromic_group = "grp", simulations = 5000, info = FALSE))
|
||||
wisca_params <- retrieve_wisca_parameters(wisca_strat)
|
||||
# no absent or untested pathogens
|
||||
expect_true(all(wisca_params$n_total > 0 & wisca_params$n_tested > 0))
|
||||
# each stratum equals the unstratified WISCA of its subset
|
||||
for (g in c("A", "B", "C")) {
|
||||
wisca_subset <- suppressWarnings(wisca(wisca_data[wisca_data$grp == g, ], antimicrobials = "MEM", simulations = 5000, info = FALSE))
|
||||
cov_strat <- attributes(wisca_strat)$long_numeric$coverage[attributes(wisca_strat)$long_numeric$syndromic_group == g]
|
||||
expect_true(abs(cov_strat - attributes(wisca_subset)$long_numeric$coverage) < 0.01)
|
||||
expect_identical(attributes(wisca_strat)$long_numeric$n_total[attributes(wisca_strat)$long_numeric$syndromic_group == g], attributes(wisca_subset)$long_numeric$n_total)
|
||||
}
|
||||
# untested pathogens have no influence
|
||||
wisca_untested <- wisca_data[1:20, ]
|
||||
wisca_untested$mo <- as.mo("Brucella melitensis")
|
||||
wisca_untested$MEM <- NA_sir_
|
||||
set.seed(337)
|
||||
wisca_without <- suppressWarnings(wisca(wisca_data, antimicrobials = "MEM", simulations = 1000, info = FALSE))
|
||||
set.seed(337)
|
||||
wisca_with <- suppressWarnings(wisca(rbind(wisca_data, wisca_untested), antimicrobials = "MEM", simulations = 1000, info = FALSE))
|
||||
expect_identical(attributes(wisca_with)$long_numeric, attributes(wisca_without)$long_numeric)
|
||||
expect_false("B_BRCLL_MLTN" %in% retrieve_wisca_parameters(wisca_with)$mo)
|
||||
# warning for a large prior weight of rare pathogens
|
||||
expect_warning(wisca(wisca_data, antimicrobials = "MEM", syndromic_group = "grp", simulations = 100, info = FALSE), "prior weight")
|
||||
# (expect_warning(..., NA) is not supported by tinytest)
|
||||
wisca_warnings <- character(0)
|
||||
withCallingHandlers(
|
||||
wisca(example_isolates, antimicrobials = "MEM", simulations = 100, info = FALSE),
|
||||
warning = function(w) {
|
||||
wisca_warnings <<- c(wisca_warnings, conditionMessage(w))
|
||||
invokeRestart("muffleWarning")
|
||||
}
|
||||
)
|
||||
expect_false(any(grepl("prior weight", wisca_warnings)))
|
||||
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
|
||||
ab_grouped <- tryCatch(
|
||||
# pathogen names are translated, so test in English, which must also hold on systems in another language
|
||||
@@ -230,7 +268,19 @@ test_that("test-antibiogram.R", {
|
||||
expect_equal(nrow(ab_grp_seq), nrow(ab_grp_par))
|
||||
}
|
||||
|
||||
# 6. parallel = TRUE without a plan raises an informative error
|
||||
# 6. parallel and sequential use the same WISCA parameters and give the same coverage (#337)
|
||||
set.seed(337)
|
||||
wisca_data <- example_isolates[mo_domain(example_isolates$mo) == "Bacteria", ]
|
||||
wisca_data$grp <- sample(c("A", "B", "C"), nrow(wisca_data), replace = TRUE)
|
||||
future::plan(future::sequential)
|
||||
wisca_strat_seq <- suppressWarnings(wisca(wisca_data, antimicrobials = "MEM", syndromic_group = "grp", simulations = 5000, info = FALSE))
|
||||
future::plan(future::multicore)
|
||||
wisca_strat_par <- suppressWarnings(wisca(wisca_data, antimicrobials = "MEM", syndromic_group = "grp", simulations = 5000, parallel = TRUE, info = FALSE))
|
||||
expect_identical(retrieve_wisca_parameters(wisca_strat_par), retrieve_wisca_parameters(wisca_strat_seq))
|
||||
expect_true(all(retrieve_wisca_parameters(wisca_strat_par)$n_tested > 0))
|
||||
expect_true(all(abs(attributes(wisca_strat_par)$long_numeric$coverage - attributes(wisca_strat_seq)$long_numeric$coverage) < 0.01))
|
||||
|
||||
# 7. parallel = TRUE without a plan raises an informative error
|
||||
future::plan(future::sequential)
|
||||
expect_error(
|
||||
suppressWarnings(wisca(example_isolates, antimicrobials = "TZP", parallel = TRUE, info = FALSE)),
|
||||
|
||||
@@ -547,6 +547,25 @@ test_that("intrinsic_resistant: synonyms have exactly the resistances of their c
|
||||
expect_true(length(absent) == 0, info = paste("Current names missing while their synonym is listed:", describe_mo(absent)))
|
||||
})
|
||||
|
||||
test_that("intrinsic_resistant: species groups have the resistances shared by all their species", {
|
||||
skip_on_cran()
|
||||
ir_keys <- paste(intrinsic_resistant$mo, intrinsic_resistant$ab)
|
||||
groups <- microorganisms.groups[which(microorganisms$rank[match(microorganisms.groups$mo, microorganisms$mo)] == "species"), , drop = FALSE]
|
||||
groups_species <- split(as.character(groups$mo), as.character(groups$mo_group))
|
||||
missing <- unlist(Map(function(group, species) {
|
||||
abx <- unique(as.character(intrinsic_resistant$ab[intrinsic_resistant$mo %in% species]))
|
||||
abx <- abx[vapply(abx, function(ab) all(paste(species, ab) %in% ir_keys), logical(1))]
|
||||
if (length(abx) == 0) {
|
||||
return(NULL)
|
||||
}
|
||||
abx <- abx[!paste(group, abx) %in% ir_keys]
|
||||
if (length(abx) > 0) paste0(group, " (", paste(abx, collapse = ", "), ")")
|
||||
}, names(groups_species), groups_species), use.names = FALSE)
|
||||
expect_true(length(missing) == 0, info = paste("Species groups missing resistances shared by all their species:", paste(missing, collapse = "; ")))
|
||||
# Enterobacter cloacae complex (#338)
|
||||
expect_true(all(mo_is_intrinsic_resistant(rep("B_ENTRBC_CLOC-C", 2), ab = c("AMX", "AMC"))))
|
||||
})
|
||||
|
||||
test_that("interpretive rules name organisms that exist", {
|
||||
skip_on_cran()
|
||||
r <- AMR:::INTERPRETIVE_RULES_DF
|
||||
|
||||
@@ -270,6 +270,7 @@ Use the `wisca()` function, or equivalently `antibiogram(..., wisca = TRUE)`. WI
|
||||
|
||||
```{r wisca}
|
||||
wisca_result <- example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
wisca(
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10
|
||||
|
||||
Reference in new issue
Block a user