-
AMR 2.1.1.9133
+
AMR 2.1.1.9134
(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.)
-
A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
+
A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
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.
-
Breaking
+
Breaking
- Removed all functions and references that used the deprecated
rsi class, which were all replaced with their sir equivalents two years ago
-
New
+
New
-
One Health implementation
- Function
as.sir() now has extensive support for veterinary breakpoints from CLSI. Use breakpoint_type = "animal" and set the host argument to a variable that contains animal species names.
@@ -109,7 +109,7 @@
-
Changed
+
Changed
- SIR interpretation
- It is now possible to use column names for argument
ab, mo, and uti: as.sir(..., ab = "column1", mo = "column2", uti = "column3"). This greatly improves the flexibility for users.
- Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
@@ -172,7 +172,7 @@
-
Other
+
Other
- Added Dr. Larisse Bolton as contributor for her fantastic implementation of WISCA in a mathematically solid way
- Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations
- Greatly improved
vctrs integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as dplyr’s bind_rows(), rowwise() and c_across() are now supported for e.g. columns of class mic. Despite this, this AMR package is still zero-dependent on any other package, including dplyr and vctrs.
@@ -180,7 +180,7 @@
- Stopped support for SAS (
.xpt) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.
-
Older Versions
+
Older Versions
This changelog only contains changes from AMR v3.0 (February 2025) and later.
- For prior v2 versions, please see our v2 archive.
- For prior v1 versions, please see our v1 archive.
diff --git a/pkgdown.yml b/pkgdown.yml
index d929fac2f..e6ad9d3d6 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -12,7 +12,7 @@ articles:
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
-last_built: 2025-01-27T21:47Z
+last_built: 2025-01-27T22:15Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 06e236762..597630d7b 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9133
+ 2.1.1.9134
@@ -305,7 +305,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
-
WISCA, as outlined by Barbieri et al. (doi:10.1186/s13756-021-00939-2
+
WISCA, as outlined by Bielicki et al. (doi:10.1093/jac/dkv397
), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \(\theta\) for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
$$\theta \sim \text{Beta}(\alpha_0, \beta_0)$$
@@ -313,13 +313,17 @@ Adhering to previously described approaches (see Source) and especially the Baye
$$y \sim \text{Binomial}(n, \theta)$$
Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \(\theta\) is also a Beta distribution:
$$\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)$$
+
Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \(\alpha\), where each \(\alpha_i\) corresponds to a specific pathogen. The prior is typically chosen to be uniform (\(\alpha_i = 1\)), reflecting an assumption of equal prior probability across pathogens.
+
The posterior distribution of pathogen incidence is then given by:
+
$$\text{Dirichlet}(\alpha_1 + n_1, \alpha_2 + n_2, \dots, \alpha_K + n_K)$$
+
where \(n_i\) is the number of infections caused by pathogen \(i\) observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:
+
$$x_i \sim \text{Gamma}(\alpha_i + n_i, 1)$$
+$$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$
+
where \(x_i\) represents unnormalised pathogen counts, and \(p_i\) is the normalised proportion for pathogen \(i\).
For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using dplyr's group_by() as a pre-processing step before running wisca(). In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
$$\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}$$
-
By combining empirical data with prior knowledge, WISCA overcomes the limitations
-of traditional combination antibiograms, offering disease-specific, patient-stratified
-estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial
-stewardship programs and empirical treatment guideline refinement.
+
By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.