Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
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Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
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Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
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Details
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This function returns a table with values between 0 and 100 for susceptibility, not resistance.
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Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms.
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These functions return a table with values between 0 and 100 for susceptibility, not resistance.
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Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them with one of the four available algorithms: isolate-based, patient-based, episode-based, or phenotype-based.
For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top n species encountered in the data. You can filter on this top n using top_n_microorganisms(). For example, use top_n_microorganisms(your_data, n = 10) as a pre-processing step to only include the top 10 species in the data.
The numeric values of an antibiogram are stored in a long format as the attribute long_numeric. You can retrieve them using attributes(x)$long_numeric, where x is the outcome of antibiogram() or wisca(). This is ideal for e.g. advanced plotting.
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$$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$
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.
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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:
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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(). 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.
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AMR (for R)
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2.1.1.9134
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2.1.1.9135