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<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.9/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.9/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram"><meta name="description" content="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."><meta property="og:description" content="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."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"></head><body>
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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."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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@@ -304,40 +304,27 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<h2 id="why-use-wisca-">Why Use WISCA?<a class="anchor" aria-label="anchor" href="#why-use-wisca-"></a></h2>
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<p>WISCA, as outlined by Barbieri <em>et al.</em> (<a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">doi:10.1186/s13756-021-00939-2</a>
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), stands for
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Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
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of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
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This method leverages a Bayesian hierarchical logistic regression framework with random
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effects for pathogens and regimens, enabling robust estimates in the presence of sparse
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data.</p>
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<p>The Bayesian model assumes conjugate priors for parameter estimation. For example, the
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coverage probability $theta$ for a given antimicrobial regimen
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is modeled using a Beta distribution as a prior:</p>
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<p><img src="figures/beta_prior.png" width="300" alt="Beta prior"></p>
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<p>where \($alpha$_0\) and \($beta$_0\) represent prior successes and failures, respectively,
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informed by expert knowledge or weakly informative priors (e.g., \($alpha$_0 = 1, $beta$_0 = 1\)).</p>
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<p>The likelihood function is constructed based on observed data, where the number of covered
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cases for a regimen follows a binomial distribution:</p>
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<p><img src="figures/binomial_likelihood.png" width="300" alt="Binomial likelihood"></p>
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<p>Posterior parameter estimates are obtained by combining the prior and likelihood using
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Bayes' theorem. The posterior distribution of \($theta$\) is also a Beta distribution:</p>
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<p><img src="figures/posterior_beta.png" width="300" alt="Beta posterior"></p>
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<p>For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
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patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
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This hierarchical structure ensures partial pooling of estimates across groups,
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improving stability in strata with small sample sizes. The model is implemented using
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Hamiltonian Monte Carlo (HMC) sampling.</p>
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<p>Stratified results are provided based on covariates such as age, sex, and clinical
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complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
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For example, posterior odds ratios (ORs) are derived to quantify the effect of these
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covariates on coverage probabilities:</p>
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<p><img src="figures/odds_ratio.png" width="300" alt="Odds ratio formula"></p>
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), 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.</p>
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<p>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:</p>
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<p>$$\theta \sim \text{Beta}(\alpha_0, \beta_0)$$</p>
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<p>where \(\alpha_0\) and \(\beta_0\) represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \(\alpha_0 = 1, \beta_0 = 1\)). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:</p>
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<p>$$y \sim \text{Binomial}(n, \theta)$$</p>
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<p>Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \(\theta\) is also a Beta distribution:</p>
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<p>$$\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)$$</p>
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<p>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.</p>
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<p>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 <code>dplyr</code>'s <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> as a pre-processing step before running <code>wisca()</code>. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:</p>
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<p>$$\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}$$</p>
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<p>By combining empirical data with prior knowledge, WISCA overcomes the limitations
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of traditional combination antibiograms, offering disease-specific, patient-stratified
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estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial
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stewardship programs and empirical treatment guideline refinement.</p>
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</div>
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<div class="section level2">
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<h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
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<p>Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends</p>
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</div>
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<div class="section level2">
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<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
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@@ -537,7 +524,8 @@ stewardship programs and empirical treatment guideline refinement.</p>
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<span class="r-in"><span></span></span>
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<span class="r-in"><span><span class="va">ureido</span> <span class="op"><-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
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<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="antimicrobial_class_selectors.html">ureidopenicillins</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
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<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span></span></span>
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<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
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<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
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<span class="r-in"><span><span class="op">)</span></span></span>
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<span class="r-msg co"><span class="r-pr">#></span> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)</span>
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<span class="r-in"><span></span></span>
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@@ -550,10 +538,20 @@ stewardship programs and empirical treatment guideline refinement.</p>
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<span class="r-out co"><span class="r-pr">#></span> </span>
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<span class="r-out co"><span class="r-pr">#></span> |Pathogen |Piperacillin/tazobactam |</span>
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<span class="r-out co"><span class="r-pr">#></span> |:---------------|:-----------------------|</span>
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<span class="r-out co"><span class="r-pr">#></span> |CoNS |30% (10/33) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*E. coli* |94% (393/416) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*K. pneumoniae* |89% (47/53) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*S. pneumoniae* |100% (112/112) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*B. fragilis* |5% (0-17%,N=20) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |CoNS |32% (17-47%,N=33) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*E. cloacae* |73% (51-88%,N=20) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*E. coli* |94% (92-96%,N=416) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*E. faecalis* |95% (82-100%,N=18) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*E. faecium* |10% (1-26%,N=18) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |GBS |95% (84-100%,N=18) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*K. pneumoniae* |87% (78-95%,N=53) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*P. aeruginosa* |97% (88-100%,N=27) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*P. mirabilis* |97% (88-100%,N=27) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*S. anginosus* |94% (80-100%,N=16) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*S. marcescens* |50% (32-69%,N=22) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*S. pneumoniae* |99% (97-100%,N=112) |</span>
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<span class="r-out co"><span class="r-pr">#></span> |*S. pyogenes* |95% (81-100%,N=16) |</span>
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<span class="r-in"><span></span></span>
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<span class="r-in"><span></span></span>
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<span class="r-in"><span><span class="co"># Generate plots with ggplot2 or base R --------------------------------</span></span></span>
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