vignettes/WISCA.Rmd
WISCA.Rmd
Clinical guidelines for empirical antimicrobial therapy require +probabilistic reasoning: what is the chance that a regimen will +cover the likely infecting organisms, before culture results are +available?
This is the purpose of WISCA, or:
+Weighted-Incidence Syndromic Combination +Antibiogram +
Weighted-Incidence Syndromic Combination +Antibiogram
WISCA is a Bayesian approach that integrates: - Pathogen +prevalence (how often each species causes the syndrome), - +Regimen susceptibility (how often a regimen works +if the pathogen is known),
to estimate the overall empirical coverage of +antimicrobial regimens — with quantified uncertainty.
This vignette explains how WISCA works, why it is useful, and how to +apply it in AMR.
A standard antibiogram gives you:
``` Species → Antibiotic → Susceptibility %
But clinicians don’t know the species a priori. They need to +choose a regimen that covers the likely pathogens — +without knowing which one is present.
Traditional antibiograms: - Fragment information by organism, - Do +not weight by real-world prevalence, - Do not account for combination +therapy or sample size, - Do not provide uncertainty.
WISCA asks:
+“What is the probability that this regimen +will cover the pathogen, given the syndrome?” +
“What is the probability that this regimen +will cover the pathogen, given the syndrome?”
This means combining two things: - Incidence of each +pathogen in the syndrome, - Susceptibility of each +pathogen to the regimen.
We can write this as:
``` coverage = ∑ (pathogen incidence × susceptibility)
For example, suppose: - E. coli causes 60% of cases, and 90% of +E. coli are susceptible to a drug. - Klebsiella causes 40% of +cases, and 70% of Klebsiella are susceptible.
Then:
``` coverage = (0.6 × 0.9) + (0.4 × 0.7) = 0.82
But in real data, incidence and susceptibility are estimated +from samples — so they carry uncertainty. WISCA models this +probabilistically, using conjugate Bayesian +distributions.
Let: - K be the number of pathogens, - +α = (1, 1, ..., 1) be a **Dirichlet** prior (uniform), - n += (n₁, …, nₖ) be the observed counts per species.
α = (1, 1, ..., 1) be a **Dirichlet** prior (uniform), -
Then the posterior incidence follows:
``` incidence ∼ Dirichlet(α + n)
In simulations, we draw from this posterior using:
``` xᵢ ∼ Gamma(αᵢ + nᵢ, 1)
``` incidenceᵢ = xᵢ / ∑ xⱼ
Each pathogen–regimen pair has: - prior: Beta(1, 1) - +data: S susceptible out of N tested
prior: Beta(1, 1) -
``` susceptibility ∼ Beta(1 + S, 1 + (N - S))
In each simulation, we draw random susceptibility per species from +this Beta distribution.
Putting it together:
``` For each simulation: - Draw incidence ∼ Dirichlet - Draw +susceptibility ∼ Beta - Multiply → coverage estimate
We repeat this (e.g. 1000×) and summarise: - Mean: +expected coverage - Quantiles: credible interval +(default 95%)
+library(AMR) +data <- example_isolates + +# Add a fake syndrome column for stratification +data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Other")
library(AMR) +data <- example_isolates + +# Add a fake syndrome column for stratification +data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Other")
+antibiogram(data, + wisca = TRUE)
antibiogram(data, + wisca = TRUE)
+antibiogram(data, + syndromic_group = "syndrome", + wisca = TRUE)
antibiogram(data, + syndromic_group = "syndrome", + wisca = TRUE)
The antibiogram() function supports combination +regimens:
antibiogram()
+antibiogram(data, + antimicrobials = c("AMC", "GEN", "AMC + GEN", "CIP"), + wisca = TRUE)
antibiogram(data, + antimicrobials = c("AMC", "GEN", "AMC + GEN", "CIP"), + wisca = TRUE)
Suppose you get this output:
Interpretation:
+“AMC + GEN covers 88% of expected pathogens for this syndrome, +with 95% certainty that the true coverage lies between 83% and +93%.” +
“AMC + GEN covers 88% of expected pathogens for this syndrome, +with 95% certainty that the true coverage lies between 83% and +93%.”
Regimens with few tested isolates will show wider +intervals.
minimum = 30
simulations = 1000
conf_interval = 0.95
combine_SI = TRUE
Bielicki JA et al. (2016).Weighted-incidence syndromic combination antibiograms to guide +empiric treatment in pediatric bloodstream infections.J Antimicrob Chemother, 71(2):529–536. doi:10.1093/jac/dkv397
WISCA shifts empirical therapy from simple percent susceptible toward +probabilistic, syndrome-based decision support. It is a +statistically principled, clinically intuitive method to guide regimen +selection — and easy to use via the antibiogram() function +in the AMR package.
For antimicrobial stewardship teams, it enables +disease-specific, reproducible, and data-driven +guidance — even in the face of sparse data.
vignettes/datasets.Rmd
datasets.Rmd
vignettes/welcome_to_AMR.Rmd
welcome_to_AMR.Rmd
Note: to keep the package size as small as possible, we only include -this vignette on CRAN. You can read more vignettes on our website about -how to conduct AMR data analysis, determine MDROs, find explanation of -EUCAST and CLSI breakpoints, and much more: https://amr-for-r.org.
The AMR package is a peer-reviewed, free and open-source R -package with zero -dependencies to simplify the analysis and prediction of -Antimicrobial Resistance (AMR) and to work with microbial and -antimicrobial data and properties, by using evidence-based methods. -Our aim is to provide a standard for clean and -reproducible AMR data analysis, that can therefore empower -epidemiological analyses to continuously enable surveillance and -treatment evaluation in any setting. We are a team of many different researchers -from around the globe to make this a successful and durable project!
AMR
This work was published in the Journal of Statistical Software -(Volume 104(3); ) and formed the basis of two PhD theses ( and ).
After installing this package, R knows ~79 -000 distinct microbial species (updated June 2024) and all -~620 -antimicrobial and antiviral drugs by name and code -(including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and -knows all about valid SIR and MIC values. The integral clinical -breakpoint guidelines from CLSI 2011-2025 and EUCAST 2011-2025 are -included, even with epidemiological cut-off (ECOFF) values. It supports -and can read any data format, including WHONET data. This package works -on Windows, macOS and Linux with all versions of R since R-3.0 (April -2013). It was designed to work in any setting, including those -with very limited resources. It was created for both routine -data analysis and academic research at the Faculty of Medical Sciences -of the University of Groningen and the -University Medical Center -Groningen.
The AMR package is available in English, Chinese, Czech, -Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, -Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, -Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial -microorganism names are provided in these languages.
This package was intended as a comprehensive toolbox for integrated -AMR data analysis. This package can be used for:
All reference data sets in the AMR package - including information on -microorganisms, antimicrobials, and clinical breakpoints - are freely -available for download in multiple formats: R, MS Excel, Apache Feather, -Apache Parquet, SPSS, and Stata.
For maximum compatibility, we also provide machine-readable, -tab-separated plain text files suitable for use in any software, -including laboratory information systems.
Visit our -website for direct download links, or explore the actual files in our -GitHub repository.
This AMR package for R is free, open-source software and -licensed under the GNU -General Public License v2.0 (GPL-2). These requirements are -consequently legally binding: modifications must be released under the -same license when distributing the package, changes made to the code -must be documented, source code must be made available when the package -is distributed, and a copy of the license and copyright notice must be -included with the package.
(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using the instructions here.)
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the University of Prince Edward Island’s Atlantic Veterinary College, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
antibiotics
antimicrobials
as.sir()
wisca()
scale_*_mic()
scale_*_sir()
rescale_mic()
top_n_microorganisms()
mo_group_members()
mic_p50()
mic_p90()
resistance_predict()
sir_predict()
isoxazolylpenicillins()
monobactams()
nitrofurans()
phenicols()
rifamycins()
sulfonamides()
ab_*
amr_*
mdro()
vctrs
.xpt
rsi
sir
tidymodels
breakpoint_type = "animal"
host
parallel
as.sir(your_data, parallel = TRUE)
guideline
ab
mo
uti
as.sir(..., ab = "column1", mo = "column2", uti = "column3")
This changelog only contains changes from AMR v3.0 (March 2025) and later.
A logical indicating whether to overwrite non-NA values (default: FALSE). When FALSE, only non-SIR values are modified (i.e., any value that is not already S, I or R). To ensure compliance with EUCAST guidelines, this should remain FALSE, as EUCAST notes often state that an organism "should be tested for susceptibility to individual agents or be reported resistant".
NA
FALSE
A logical indicating whether to overwrite existing SIR values (default: FALSE). When FALSE, only non-SIR values are modified (i.e., any value that is not already S, I or R). To ensure compliance with EUCAST guidelines, this should remain FALSE, as EUCAST notes often state that an organism "should be tested for susceptibility to individual agents or be reported resistant".