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(v2.1.1.9155) new mic_p50() and mic_p90() - updated AMR intro
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@ -1,6 +1,6 @@
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This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
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First and foremost, you are trained on version 2.1.1.9154. Remember this whenever someone asks which AMR package version you’re at.
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First and foremost, you are trained on version 2.1.1.9155. Remember this whenever someone asks which AMR package version you’re at.
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Below are the contents of the file, the file, and all the files (documentation) in the package. Every file content is split using 100 hypens.
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----------------------------------------------------------------------------------------------------
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@ -262,6 +262,8 @@ export(mdr_cmi2012)
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export(mdr_tb)
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export(mdro)
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export(mean_amr_distance)
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export(mic_p50)
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export(mic_p90)
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export(mo_authors)
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export(mo_class)
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export(mo_cleaning_regex)
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@ -656,30 +658,18 @@ It will be downloaded and installed automatically. For RStudio, click on the men
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#### Latest development version
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[](https://github.com/msberends/AMR/actions/workflows/check-old.yaml?query=branch%3Amain)
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[](https://github.com/msberends/AMR/actions/workflows/check-recent.yaml?query=branch%3Amain)
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[](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml)
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[](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml)
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[](https://www.codefactor.io/repository/github/msberends/amr)
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[](https://codecov.io/gh/msberends/AMR?branch=main)
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Please read our [Developer Guideline here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
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The latest and unpublished development version can be installed from GitHub in two ways:
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The latest and unpublished development version can be installed from the [rOpenSci R-universe platform](https://msberends.r-universe.dev/AMR):
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1. Manually, using:
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```r
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install.packages("remotes") # if you haven't already
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remotes::install_github("msberends/AMR")
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```
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2. Automatically, using the [rOpenSci R-universe platform](https://ropensci.org/r-universe/), by adding [our R-universe address](https://msberends.r-universe.dev) to your list of repositories ('repos'):
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```r
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options(repos = c(getOption("repos"),
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msberends = "https://msberends.r-universe.dev"))
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```
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After this, you can install and update this `AMR` package like any official release (e.g., using `install.packages("AMR")` or in RStudio via *Tools* > *Check for Package Updates...*).
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```r
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install.packages("AMR", repos = "https://msberends.r-universe.dev")
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```
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### Get started
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@ -2873,6 +2863,8 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/as.mic.Rd':
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\alias{is.mic}
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\alias{NA_mic_}
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\alias{rescale_mic}
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\alias{mic_p50}
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\alias{mic_p90}
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\alias{droplevels.mic}
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\title{Transform Input to Minimum Inhibitory Concentrations (MIC)}
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\usage{
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@ -2884,6 +2876,10 @@ NA_mic_
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rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE)
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mic_p50(x, na.rm = FALSE, ...)
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mic_p90(x, na.rm = FALSE, ...)
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\method{droplevels}{mic}(x, as.mic = FALSE, ...)
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}
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\arguments{
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@ -2953,6 +2949,8 @@ With \code{\link[=rescale_mic]{rescale_mic()}}, existing MIC ranges can be limit
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For \code{ggplot2}, use one of the \code{\link[=scale_x_mic]{scale_*_mic()}} functions to plot MIC values. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
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\code{NA_mic_} is a missing value of the new \code{mic} class, analogous to e.g. base \R's \code{\link[base:NA]{NA_character_}}.
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Use \code{\link[=mic_p50]{mic_p50()}} and \code{\link[=mic_p90]{mic_p90()}} to get the 50th and 90th percentile of MIC values. They return 'normal' \link{numeric} values.
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}
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\examples{
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mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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@ -7540,6 +7538,131 @@ The interpretation of "I" will be named "Increased exposure" for all EUCAST guid
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For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option \code{\link[=AMR-options]{AMR_guideline}} is set. See \code{\link[=as.sir]{as.sir()}} for more information.
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}
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}
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\examples{
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some_mic_values <- random_mic(size = 100)
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some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
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some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
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\donttest{
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# Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
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if (require("ggplot2")) {
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autoplot(some_mic_values)
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}
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if (require("ggplot2")) {
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# when providing the microorganism and antibiotic, colours will show interpretations:
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autoplot(some_mic_values, mo = "Escherichia coli", ab = "cipro")
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}
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if (require("ggplot2")) {
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# support for 20 languages, various guidelines, and many options
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autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
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guideline = "CLSI 2024", language = "no",
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title = "Disk diffusion from the North")
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}
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# Plotting using scale_x_mic() -----------------------------------------
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if (require("ggplot2")) {
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mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
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counts = c(1, 1, 2, 2, 3, 3)),
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aes(mics, counts)) +
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geom_col()
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mic_plot +
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labs(title = "without scale_x_mic()")
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}
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if (require("ggplot2")) {
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mic_plot +
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scale_x_mic() +
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labs(title = "with scale_x_mic()")
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}
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if (require("ggplot2")) {
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mic_plot +
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scale_x_mic(keep_operators = "all") +
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labs(title = "with scale_x_mic() keeping all operators")
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}
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if (require("ggplot2")) {
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mic_plot +
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scale_x_mic(mic_range = c(1, 16)) +
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labs(title = "with scale_x_mic() using a manual 'within' range")
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}
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if (require("ggplot2")) {
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mic_plot +
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scale_x_mic(mic_range = c(0.032, 256)) +
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labs(title = "with scale_x_mic() using a manual 'outside' range")
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}
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# Plotting using scale_y_mic() -----------------------------------------
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some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
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if (require("ggplot2")) {
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ggplot(data.frame(mic = some_mic_values,
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group = some_groups),
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aes(group, mic)) +
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geom_boxplot() +
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geom_violin(linetype = 2, colour = "grey", fill = NA) +
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scale_y_mic()
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}
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if (require("ggplot2")) {
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ggplot(data.frame(mic = some_mic_values,
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group = some_groups),
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aes(group, mic)) +
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geom_boxplot() +
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geom_violin(linetype = 2, colour = "grey", fill = NA) +
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scale_y_mic(mic_range = c(NA, 0.25))
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}
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# Plotting using scale_x_sir() -----------------------------------------
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if (require("ggplot2")) {
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ggplot(data.frame(x = c("I", "R", "S"),
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y = c(45,323, 573)),
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aes(x, y)) +
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geom_col() +
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scale_x_sir()
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}
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# Plotting using scale_y_mic() and scale_colour_sir() ------------------
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if (require("ggplot2")) {
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plain <- ggplot(data.frame(mic = some_mic_values,
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group = some_groups,
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sir = as.sir(some_mic_values,
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mo = "E. coli",
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ab = "cipro")),
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aes(x = group, y = mic, colour = sir)) +
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theme_minimal() +
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geom_boxplot(fill = NA, colour = "grey") +
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geom_jitter(width = 0.25)
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plain
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}
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if (require("ggplot2")) {
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# and now with our MIC and SIR scale functions:
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plain +
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scale_y_mic() +
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scale_colour_sir()
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}
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if (require("ggplot2")) {
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plain +
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scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
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scale_colour_sir(language = "pt",
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name = "Support in 20 languages")
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}
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}
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# Plotting using base R's plot() ---------------------------------------
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plot(some_mic_values)
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# when providing the microorganism and antibiotic, colours will show interpretations:
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plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
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plot(some_disk_values)
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plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
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plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
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plot(some_sir_values)
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}
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@ -8516,8 +8639,10 @@ antibiogram(example_isolates,
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To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
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```{r comb}
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antibiogram(example_isolates,
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antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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combined_ab <- antibiogram(example_isolates,
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antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
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ab_transform = NULL)
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combined_ab
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```
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### Syndromic Antibiogram
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@ -8532,17 +8657,26 @@ antibiogram(example_isolates,
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### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
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To create a WISCA, you must state combination therapy in the `antibiotics` argument (similar to the Combination Antibiogram), define a syndromic group with the `syndromic_group` argument (similar to the Syndromic Antibiogram) in which cases are predefined based on clinical or demographic characteristics (e.g., endocarditis in 75+ females). This next example is a simplification without clinical characteristics, but just gives an idea of how a WISCA can be created:
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To create a **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**, simply set `wisca = TRUE` in the `antibiogram()` function, or use the dedicated `wisca()` function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted by pathogen incidence and antimicrobial susceptibility patterns.
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```{r wisca}
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wisca <- antibiogram(example_isolates,
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antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
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mo_transform = "gramstain",
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minimum = 10, # this should be >= 30, but now just as example
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syndromic_group = ifelse(example_isolates$age >= 65 &
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example_isolates$gender == "M",
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"WISCA Group 1", "WISCA Group 2"))
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wisca
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example_isolates %>%
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wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
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minimum = 10) # Recommended threshold: ≥30
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```
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WISCA uses a **Bayesian decision model** to integrate data from multiple pathogens, improving empirical therapy guidance, especially for low-incidence infections. It is **pathogen-agnostic**, meaning results are syndrome-based rather than stratified by microorganism.
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For reliable results, ensure your data includes **only first isolates** (use `first_isolate()`) and consider filtering for **the top *n* species** (use `top_n_microorganisms()`), as WISCA outcomes are most meaningful when based on robust incidence estimates.
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For **patient- or syndrome-specific WISCA**, run the function on a grouped `tibble`, i.e., using `group_by()` first:
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```{r wisca_grouped}
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example_isolates %>%
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top_n_microorganisms(n = 10) %>%
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group_by(age_group = age_groups(age, c(25, 50, 75)),
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gender) %>%
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wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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```
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### Plotting antibiograms
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@ -8550,7 +8684,7 @@ wisca
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Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
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```{r}
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autoplot(wisca)
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autoplot(combined_ab)
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```
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To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the `resistance()` and `susceptibility()` functions.
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@ -8575,9 +8709,54 @@ our_data_1st %>%
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summarise(amoxicillin = resistance(AMX))
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```
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## Interpreting MIC and Disk Diffusion Values
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Minimal inhibitory concentration (MIC) values and disk diffusion diameters can be interpreted into clinical breakpoints (SIR) using `as.sir()`. Here’s an example with randomly generated MIC values for *Klebsiella pneumoniae* and ciprofloxacin:
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```{r mic_interpretation}
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set.seed(123)
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mic_values <- random_mic(100)
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sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
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my_data <- tibble(MIC = mic_values, SIR = sir_values)
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my_data
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```
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This allows direct interpretation according to EUCAST or CLSI breakpoints, facilitating automated AMR data processing.
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## Plotting MIC and SIR Interpretations
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We can visualise MIC distributions and their SIR interpretations using `ggplot2`, using the new `scale_y_mic()` for the y-axis and `scale_colour_sir()` to colour-code SIR categories.
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```{r mic_plot}
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# add a group
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my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
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ggplot(my_data,
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aes(x = group, y = MIC, colour = SIR)) +
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geom_jitter(width = 0.2, size = 2) +
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geom_boxplot(fill = NA, colour = "grey40") +
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scale_y_mic() +
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scale_colour_sir() +
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labs(title = "MIC Distribution and SIR Interpretation",
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x = "Sample Groups",
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y = "MIC (mg/L)")
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```
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This plot provides an intuitive way to assess susceptibility patterns across different groups while incorporating clinical breakpoints.
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For a more straightforward and less manual approach, `ggplot2`'s function `autoplot()` has been extended by this package to directly plot MIC and disk diffusion values:
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```{r autoplot}
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autoplot(mic_values)
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# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
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autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
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```
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----
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*Author: Dr. Matthijs Berends, 26th Feb 2023*
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*Author: Dr. Matthijs Berends, 23rd Feb 2025*
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Reference in New Issue
Block a user