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<img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
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<div class="d-none name"><code>WISCA.Rmd</code></div>
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<div class="section level2">
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<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
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</h2>
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<p>When a clinician starts empirical antimicrobial therapy, the
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causative pathogen is unknown. The question they need answered is not
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<em>“what proportion of</em> E. coli <em>is susceptible to
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ciprofloxacin?“</em> but rather <em>“what is the probability that this
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regimen will adequately cover whatever pathogen turns out to be causing
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my patient’s infection?”</em></p>
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<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
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cannot answer that question. It presents susceptibility percentages per
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species per antibiotic, but:</p>
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<ul>
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<li>
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<strong>It fragments information by organism.</strong> The clinician
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must mentally combine susceptibility rates across multiple species,
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weighting by how often each species causes the syndrome, a calculation
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nobody does at the bedside.</li>
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<li>
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<strong>It ignores pathogen incidence.</strong> A species that
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causes 2% of infections is given the same visual weight as one that
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causes 60%.</li>
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<li>
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<strong>It does not evaluate combination regimens.</strong> Much
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empirical therapy consists of two or more agents, but the traditional
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antibiogram only shows monotherapy per organism.</li>
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<li>
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<strong>It provides no measure of uncertainty.</strong> A reported
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“90% susceptible” based on 50 isolates has a 95% confidence interval of
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roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
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point estimate without context.</li>
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</ul>
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<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
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Antibiogram) resolves all four limitations. It estimates the probability
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that a regimen will provide adequate empirical coverage for a given
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infection syndrome, weighted by local pathogen incidence, with full
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uncertainty quantification via Bayesian inference.</p>
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<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
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demonstrated that traditional antibiogram susceptibility rates could be
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misleading: ciprofloxacin appeared 84% effective against <em>E.
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coli</em> in the traditional antibiogram, but WISCA revealed only 62%
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coverage for UTI and 37% for abdominal infections, because enterococci
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(intrinsically resistant) and other species contribute substantially to
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these syndromes. Randhawa <em>et al.</em> (2014) showed that
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WISCA-guided regimen selection could improve time-to-adequate-coverage
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on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
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Bayesian framework now used in this package, enabling credible intervals
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and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
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globally across 52 hospitals in 23 countries.</p>
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</div>
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<div class="section level2">
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<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
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</h2>
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<p>WISCA asks:</p>
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<blockquote>
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<p>“What is the <strong>probability</strong> that this regimen
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<strong>will cover</strong> the pathogen, given the syndrome?”</p>
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</blockquote>
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<p>This means combining two quantities:</p>
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<ul>
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<li>
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<strong>Pathogen incidence</strong> in the syndrome (how often each
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species causes it),</li>
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<li>
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<strong>Susceptibility</strong> of each pathogen to the
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regimen.</li>
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</ul>
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<p>We can write this as:</p>
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<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
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<p>For example, suppose in your hospital:</p>
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<ul>
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<li>
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<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
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susceptible to a drug.</li>
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<li>
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<em>Klebsiella</em> causes 40% of UTIs, and 70% of
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<em>Klebsiella</em> are susceptible.</li>
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</ul>
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<p>Then:</p>
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<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
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<p>That 82% is a far more clinically meaningful number than the
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species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
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reported separately in a traditional antibiogram, because it directly
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answers the question the clinician actually faces.</p>
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<p>But in real data, both incidence and susceptibility are
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<strong>estimated from finite samples</strong>, so they carry
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uncertainty. A sample of 50 isolates is not a census. WISCA models this
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uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
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distributions.</p>
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</div>
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<div class="section level2">
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<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
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</h2>
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<div class="section level3">
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<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
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</h3>
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<p>Let:</p>
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<ul>
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<li>
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
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be the number of pathogens,</li>
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<li>
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
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be a
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
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prior (uniform, non-informative),</li>
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<li>
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
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be the observed isolate counts per species.</li>
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</ul>
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<p>Then the posterior incidence is:</p>
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<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
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<p>To simulate from this, we use:</p>
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<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
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<p>The Dirichlet is the conjugate prior for multinomial data. With the
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non-informative prior
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
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the posterior is dominated by the data once sample sizes are reasonable.
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With small samples, the posterior is appropriately more diffuse,
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reflecting genuine uncertainty, and the resulting credible intervals
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will be wider.</p>
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</div>
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<div class="section level3">
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<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
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</h3>
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<p>Each pathogen-regimen pair has a prior and observed data:</p>
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<ul>
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<li>Default prior:
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
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(Jeffreys prior)</li>
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<li>Intrinsically resistant pairs:
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
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forcing near-zero susceptibility regardless of observed data (based on
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EUCAST Expected Resistant Phenotypes)</li>
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<li>Data:
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
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susceptible out of
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
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tested</li>
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</ul>
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<p>The
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
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category could also include values SDD (susceptible, dose-dependent) and
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I (intermediate [CLSI], or susceptible, increased exposure
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[EUCAST]).</p>
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<p>Then the posterior is:</p>
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<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
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</div>
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<div class="section level3">
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<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
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</h3>
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<p>Putting it together:</p>
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<ol style="list-style-type: decimal">
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<li>Simulate pathogen incidence:
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<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
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</li>
|
||
<li>Simulate susceptibility:
|
||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
|
||
</li>
|
||
<li>Combine:</li>
|
||
</ol>
|
||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
|
||
<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
|
||
<ul>
|
||
<li>
|
||
<strong>Mean</strong> = expected coverage</li>
|
||
<li>
|
||
<strong>Quantiles</strong> = credible interval (95% by default)</li>
|
||
</ul>
|
||
<p>Because each simulation draws from the full posterior, the resulting
|
||
distribution of coverage estimates naturally captures the joint
|
||
uncertainty in both pathogen incidence and susceptibility. The credible
|
||
interval tells you how confident you can be in the coverage estimate,
|
||
something a traditional antibiogram never provides.</p>
|
||
</div>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
|
||
</h2>
|
||
<table class="table">
|
||
<thead><tr class="header">
|
||
<th>Goal</th>
|
||
<th>Recommended approach</th>
|
||
</tr></thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>Guide empirical therapy decisions</td>
|
||
<td><strong>WISCA</strong></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>Compare regimens for a syndrome</td>
|
||
<td><strong>WISCA</strong></td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>Evaluate combination regimens</td>
|
||
<td><strong>WISCA</strong></td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>Antimicrobial stewardship (A-team)</td>
|
||
<td><strong>WISCA</strong></td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>Track resistance trends per species</td>
|
||
<td>Traditional / Combination</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>AMR surveillance reporting</td>
|
||
<td>Traditional / Syndromic</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>Understand species-level epidemiology</td>
|
||
<td>Traditional</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>In short: if the end goal involves a <em>patient</em> who does not
|
||
yet have a culture result, WISCA is the appropriate tool. If the end
|
||
goal is <em>surveillance</em> of resistance at the species level, the
|
||
traditional antibiogram remains fit for purpose.</p>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
|
||
</h2>
|
||
<div class="section level3">
|
||
<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
|
||
</h3>
|
||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
|
||
<span><span class="va">data</span> <span class="op"><-</span> <span class="va">example_isolates</span></span>
|
||
<span></span>
|
||
<span><span class="co"># Structure of our data</span></span>
|
||
<span><span class="va">data</span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
|
||
<span><span class="co">#> date patient age gender ward mo PEN OXA FLC AMX </span></span>
|
||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><date></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><mo></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span></span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> 2002-01-17 858515 79 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #949494;"> NA</span> </span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,</span></span></span>
|
||
<span><span class="co">#> <span style="color: #949494;"># IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …</span></span></span>
|
||
<span></span>
|
||
<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
|
||
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span></code></pre></div>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
|
||
</h3>
|
||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
|
||
<span><span class="op">)</span></span></code></pre></div>
|
||
<table class="table">
|
||
<thead><tr class="header">
|
||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||
<th align="left">Ciprofloxacin</th>
|
||
<th align="left">Gentamicin</th>
|
||
</tr></thead>
|
||
<tbody><tr class="odd">
|
||
<td align="left">74.2% (72.1-76.1%)</td>
|
||
<td align="left">78.4% (75.6-81.1%)</td>
|
||
<td align="left">72.5% (70.4-74.6%)</td>
|
||
</tr></tbody>
|
||
</table>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
|
||
</h3>
|
||
<p>Combination regimens are specified with a <code>+</code> separator.
|
||
WISCA evaluates whether <em>at least one</em> agent in the combination
|
||
covers the pathogen:</p>
|
||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
|
||
<span><span class="op">)</span></span></code></pre></div>
|
||
<table class="table">
|
||
<colgroup>
|
||
<col width="24%">
|
||
<col width="38%">
|
||
<col width="36%">
|
||
</colgroup>
|
||
<thead><tr class="header">
|
||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
|
||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||
</tr></thead>
|
||
<tbody><tr class="odd">
|
||
<td align="left">74.2% (72.2-76.1%)</td>
|
||
<td align="left">88.8% (87.2-90.4%)</td>
|
||
<td align="left">90.8% (89.4-92.2%)</td>
|
||
</tr></tbody>
|
||
</table>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
|
||
</h3>
|
||
<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
|
||
per clinical stratum. You can pass a column name or any expression:</p>
|
||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||
<span> syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
|
||
<span><span class="op">)</span></span></code></pre></div>
|
||
<table class="table">
|
||
<colgroup>
|
||
<col width="12%">
|
||
<col width="21%">
|
||
<col width="34%">
|
||
<col width="31%">
|
||
</colgroup>
|
||
<thead><tr class="header">
|
||
<th align="left">Syndromic Group</th>
|
||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
|
||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||
</tr></thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td align="left">Non-UTI</td>
|
||
<td align="left">70.3% (67.9-72.7%)</td>
|
||
<td align="left">86.8% (84.9-88.7%)</td>
|
||
<td align="left">88.4% (86.4-90.2%)</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td align="left">UTI</td>
|
||
<td align="left">80.3% (77-83.3%)</td>
|
||
<td align="left">88.4% (85.7-90.8%)</td>
|
||
<td align="left">91% (88.3-93.3%)</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>The <code>AMR</code> package is available in 28 languages, which can
|
||
all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
|
||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||
<span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
|
||
<span> language <span class="op">=</span> <span class="st">"Spanish"</span></span>
|
||
<span><span class="op">)</span></span></code></pre></div>
|
||
<table class="table">
|
||
<colgroup>
|
||
<col width="12%">
|
||
<col width="21%">
|
||
<col width="34%">
|
||
<col width="31%">
|
||
</colgroup>
|
||
<thead><tr class="header">
|
||
<th align="left">Grupo sindrómico</th>
|
||
<th align="left">Amoxicilina/ácido clavulánico</th>
|
||
<th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
|
||
<th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
|
||
</tr></thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td align="left">Non-UCI</td>
|
||
<td align="left">70.4% (68-72.8%)</td>
|
||
<td align="left">86.7% (84.6-88.7%)</td>
|
||
<td align="left">88.5% (86.5-90.2%)</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td align="left">UCI</td>
|
||
<td align="left">80.3% (77.2-83.5%)</td>
|
||
<td align="left">88.4% (85.5-90.8%)</td>
|
||
<td align="left">91% (88.4-93.1%)</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
|
||
</h3>
|
||
<p>Each row shows the estimated empirical coverage for a regimen, with a
|
||
95% credible interval. When comparing regimens:</p>
|
||
<ul>
|
||
<li>
|
||
<strong>Overlapping credible intervals</strong> mean there is no
|
||
statistically significant difference in coverage. If a narrower-spectrum
|
||
regimen overlaps with a broader one, the narrower-spectrum option can be
|
||
preferred on stewardship grounds.</li>
|
||
<li>
|
||
<strong>Non-overlapping credible intervals</strong> indicate a
|
||
clinically meaningful difference in coverage.</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
|
||
</h2>
|
||
<ul>
|
||
<li>
|
||
<code>simulations = 1000</code>: number of Monte Carlo draws</li>
|
||
<li>
|
||
<code>conf_interval = 0.95</code>: coverage interval width</li>
|
||
<li>
|
||
<code>combine_SI = TRUE</code>: count “I” and “SDD” as
|
||
susceptible</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
|
||
</h2>
|
||
<ul>
|
||
<li>
|
||
<strong>First isolates only</strong>: always deduplicate using
|
||
<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
|
||
introduce bias.</li>
|
||
<li>
|
||
<strong>Pathogen selection</strong>: consider filtering with
|
||
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
|
||
(e.g. CoNS without clinical context) can distort estimates and may
|
||
artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
|
||
<li>
|
||
<strong>Sample size</strong>: coverage estimates become reliable
|
||
with approximately 100+ isolates. For smaller datasets, consider pooling
|
||
data from multiple sites, but only after verifying that pathogen
|
||
distributions are sufficiently similar (Bielicki <em>et al.</em>,
|
||
2016).</li>
|
||
<li>
|
||
<strong>Culture request bias</strong>: WISCA is only as good as the
|
||
data it is based on. If cultures are selectively requested (e.g. only
|
||
after treatment failure), the dataset will be biased towards resistant
|
||
isolates. A robust culture policy is essential for reliable
|
||
estimates.</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
|
||
</h2>
|
||
<ul>
|
||
<li>It assumes your data are representative of the patient population
|
||
you are treating</li>
|
||
<li>No direct adjustment for patient-level covariates, although these
|
||
can be passed onto the <code>syndromic_group</code> argument for
|
||
stratification</li>
|
||
<li>WISCA does not model resistance trends over time; for that, you
|
||
might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
|
||
basic introduction</a>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
|
||
</h2>
|
||
<p>WISCA enables:</p>
|
||
<ul>
|
||
<li>
|
||
<strong>Empirical regimen comparison</strong>, answering the
|
||
clinician’s actual question</li>
|
||
<li>
|
||
<strong>Syndrome-specific coverage estimation</strong>, stratifiable
|
||
by any clinical variable</li>
|
||
<li>
|
||
<strong>Fully probabilistic interpretation</strong>, with credible
|
||
intervals that honestly communicate uncertainty</li>
|
||
</ul>
|
||
<p>It is available in the <code>AMR</code> package via either:</p>
|
||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
|
||
<span></span>
|
||
<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
|
||
</h2>
|
||
<ol style="list-style-type: decimal">
|
||
<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
|
||
Demonstration of the weighted-incidence syndromic combination
|
||
antibiogram: an empiric prescribing decision aid. <em>Infect Control
|
||
Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
|
||
</li>
|
||
<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
|
||
Weighted-incidence syndromic combination antibiograms to guide empiric
|
||
treatment of critical care infections: a retrospective cohort study.
|
||
<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
|
||
</li>
|
||
<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
|
||
Selecting appropriate empirical antibiotic regimens for paediatric
|
||
bloodstream infections: application of a Bayesian decision model to
|
||
local and pooled antimicrobial resistance surveillance data. <em>J
|
||
Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
|
||
</li>
|
||
<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
|
||
prescribing in pediatric bloodstream infections: a potential application
|
||
of weighted-incidence syndromic combination antibiograms (WISCA).
|
||
<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
|
||
</li>
|
||
</ol>
|
||
</div>
|
||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||
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|
||
</div>
|
||
|
||
|
||
|
||
<footer><div class="pkgdown-footer-left">
|
||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
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<div class="pkgdown-footer-right">
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<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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|
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