vignettes/AMR.Rmd
WISCA uses a Bayesian decision model to integrate @@ -1190,57 +1190,57 @@ function on a grouped tibble, i.e., using
tibble
This page was entirely written by our AMR for R Assistant, a ChatGPT -manually-trained model able to answer any question about the AMR -package. +manually-trained model able to answer any question about the +AMR package.
This page was entirely written by our AMR for R Assistant, a ChatGPT -manually-trained model able to answer any question about the AMR -package.
AMR
Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective diff --git a/articles/EUCAST.html b/articles/EUCAST.html index 7354b8426..1a093e42d 100644 --- a/articles/EUCAST.html +++ b/articles/EUCAST.html @@ -5,7 +5,7 @@ -
vignettes/EUCAST.Rmd
vignettes/MDR.Rmd
vignettes/PCA.Rmd
vignettes/WHONET.Rmd
+This explainer was largely written by our AMR for R Assistant, a ChatGPT +manually-trained model able to answer any question about the +AMR package. +
This explainer was largely written by our AMR for R Assistant, a ChatGPT +manually-trained model able to answer any question about the +AMR package.
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), - +
This is the purpose of WISCA, or +Weighted-Incidence Syndromic Combination +Antibiogram.
WISCA is a Bayesian approach that integrates:
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 %
Species → Antibiotic → Susceptibility %
But clinicians don’t know the species a priori. They need to -choose a regimen that covers the likely pathogens — +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.
Traditional antibiograms calculate the susceptibility % as just the +number of resistant isolates divided by the total number of tested +isolates. Therefore, traditional antibiograms:
“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.
This means combining two things:
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.
Coverage=∑i(Incidencei×Susceptibilityi)\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
For example, suppose:
Then:
``` coverage = (0.6 × 0.9) + (0.4 × 0.7) = 0.82
Coverage=(0.6×0.9)+(0.4×0.7)=0.82\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
But in real data, incidence and susceptibility are estimated -from samples — so they carry uncertainty. WISCA models this +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
Let:
Then the posterior incidence is:
p∼Dirichlet(α1+n1,…,αK+nK)p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
To simulate from this, we use:
xi∼Gamma(αi+ni,1),pi=xi∑j=1Kxjx_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
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.
Each pathogen–regimen pair has a prior and data:
The +SS +category could also include values SDD (susceptible, dose-dependent) and +I (intermediate [CLSI], or susceptible, increased exposure +[EUCAST]).
Then the posterior is:
θ∼Beta(α0+S,β0+N−S)\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
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%)
Coverage=∑i=1Kpi⋅θi\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
Repeat this simulation (e.g. 1000×) and summarise:
+ library(AMR) data <- example_isolates -# Add a fake syndrome column for stratification -data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Other") +# Structure of our data +data +#> # A tibble: 2,000 × 46 +#> date patient age gender ward mo PEN OXA FLC AMX +#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir> +#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA +#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R +#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA +#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA +#> # ℹ 1,990 more rows +#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, +#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, +#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, +#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, +#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, +#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … + +# Add a fake syndrome column +data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "No UTI")
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)
+wisca(data, + antimicrobials = c("AMC", "CIP", "GEN"))
wisca(data, + antimicrobials = c("AMC", "CIP", "GEN"))
+wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"))
wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"))
-antibiogram(data, - syndromic_group = "syndrome", - wisca = TRUE)
antibiogram(data, - syndromic_group = "syndrome", - wisca = TRUE)
+wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), + syndromic_group = "syndrome")
wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), + syndromic_group = "syndrome")
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)
The AMR package is available in 20 languages, which can +all be used for the wisca() function too:
wisca()
+wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), + syndromic_group = gsub("UTI", "UCI", data$syndrome), + language = "Spanish")
wisca(data, + antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), + syndromic_group = gsub("UTI", "UCI", data$syndrome), + language = "Spanish")
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
syndromic_group
tidymodels
WISCA enables:
It is available in the AMR package via either:
+wisca(...) + +antibiogram(..., wisca = TRUE)
wisca(...) + +antibiogram(..., wisca = 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.
Bielicki, JA, et al. (2016). Selecting appropriate empirical +antibiotic regimens for paediatric bloodstream infections: application +of a Bayesian decision model to local and pooled antimicrobial +resistance surveillance data. J Antimicrob +Chemother. 71(3):794-802. https://doi.org/10.1093/jac/dkv397
(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
rsi
sir
resistance_predict()
sir_predict()
as.sir()
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 separating character for antimicrobial columns in combination antibiograms.
A logical to indicate whether the antimicrobial columns must be sorted on name.
A logical to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is FALSE). This will use a Bayesian decision model to estimate regimen coverage probabilities using Monte Carlo simulations. Set simulations, conf_interval, and interval_side to adjust.
FALSE
simulations
conf_interval
interval_side