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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9057</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
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@@ -87,89 +87,98 @@
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<blockquote>
|
||||
<p>This explainer was largely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
|
||||
manually-trained model able to answer any question about the
|
||||
<code>AMR</code> package.</p>
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</blockquote>
|
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<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
<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>Clinical guidelines for empirical antimicrobial therapy require
|
||||
<em>probabilistic reasoning</em>: what is the chance that a regimen will
|
||||
cover the likely infecting organisms, before culture results are
|
||||
available?</p>
|
||||
<p>This is the purpose of <strong>WISCA</strong>, or
|
||||
<strong>Weighted-Incidence Syndromic Combination
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||||
Antibiogram</strong>.</p>
|
||||
<p>WISCA is a Bayesian approach that integrates:</p>
|
||||
<p>When a clinician starts empirical antimicrobial therapy, the
|
||||
causative pathogen is unknown. The question they need answered is not
|
||||
<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
|
||||
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,
|
||||
cannot answer that question. It presents susceptibility percentages per
|
||||
species per antibiotic, but:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Pathogen prevalence</strong> (how often each species causes
|
||||
the syndrome),</li>
|
||||
<strong>It fragments information by organism.</strong> The clinician
|
||||
must mentally combine susceptibility rates across multiple species,
|
||||
weighting by how often each species causes the syndrome, a calculation
|
||||
nobody does at the bedside.</li>
|
||||
<li>
|
||||
<strong>Regimen susceptibility</strong> (how often a regimen works
|
||||
<em>if</em> the pathogen is known),</li>
|
||||
<strong>It ignores pathogen incidence.</strong> A species that
|
||||
causes 2% of infections is given the same visual weight as one that
|
||||
causes 60%.</li>
|
||||
<li>
|
||||
<strong>It does not evaluate combination regimens.</strong> Much
|
||||
empirical therapy consists of two or more agents, but the traditional
|
||||
antibiogram only shows monotherapy per organism.</li>
|
||||
<li>
|
||||
<strong>It provides no measure of uncertainty.</strong> A reported
|
||||
“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>
|
||||
</ul>
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<p>to estimate the <strong>overall empirical coverage</strong> of
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antimicrobial regimens, with quantified uncertainty.</p>
|
||||
<p>This vignette explains how WISCA works, why it is useful, and how to
|
||||
apply it using the <code>AMR</code> package.</p>
|
||||
<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
|
||||
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
|
||||
uncertainty quantification via Bayesian inference.</p>
|
||||
<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
|
||||
demonstrated that traditional antibiogram susceptibility rates could be
|
||||
misleading: ciprofloxacin appeared 84% effective against <em>E.
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coli</em> in the traditional antibiogram, but WISCA revealed only 62%
|
||||
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
|
||||
WISCA-guided regimen selection could improve time-to-adequate-coverage
|
||||
on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
|
||||
Bayesian framework now used in this package, enabling credible intervals
|
||||
and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
|
||||
globally across 52 hospitals in 23 countries.</p>
|
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</div>
|
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<div class="section level2">
|
||||
<h2 id="why-traditional-antibiograms-fall-short">Why traditional antibiograms fall short<a class="anchor" aria-label="anchor" href="#why-traditional-antibiograms-fall-short"></a>
|
||||
</h2>
|
||||
<p>A standard antibiogram gives you:</p>
|
||||
<pre><code>Species → Antibiotic → Susceptibility %</code></pre>
|
||||
<p>But clinicians don’t know the species <em>a priori</em>. They need to
|
||||
choose a regimen that covers the <strong>likely pathogens</strong>,
|
||||
without knowing which one is present.</p>
|
||||
<p>Traditional antibiograms calculate the susceptibility % as just the
|
||||
number of resistant isolates divided by the total number of tested
|
||||
isolates. Therefore, traditional antibiograms:</p>
|
||||
<ul>
|
||||
<li>Fragment information by organism,</li>
|
||||
<li>Do not weight by real-world prevalence,</li>
|
||||
<li>Do not account for combination therapy or sample size,</li>
|
||||
<li>Do not provide uncertainty.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-idea-of-wisca">The idea of WISCA<a class="anchor" aria-label="anchor" href="#the-idea-of-wisca"></a>
|
||||
<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
|
||||
</h2>
|
||||
<p>WISCA asks:</p>
|
||||
<blockquote>
|
||||
<p>“What is the <strong>probability</strong> that this regimen
|
||||
<strong>will cover</strong> the pathogen, given the syndrome?”</p>
|
||||
</blockquote>
|
||||
<p>This means combining two things:</p>
|
||||
<p>This means combining two quantities:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Incidence</strong> of each pathogen in the syndrome,</li>
|
||||
<strong>Pathogen incidence</strong> in the syndrome (how often each
|
||||
species causes it),</li>
|
||||
<li>
|
||||
<strong>Susceptibility</strong> of each pathogen to the
|
||||
regimen.</li>
|
||||
</ul>
|
||||
<p>We can write this as:</p>
|
||||
<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>
|
||||
<p>For example, suppose:</p>
|
||||
<p>For example, suppose in your hospital:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<em>E. coli</em> causes 60% of cases, and 90% of <em>E. coli</em>
|
||||
are susceptible to a drug.</li>
|
||||
<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
|
||||
susceptible to a drug.</li>
|
||||
<li>
|
||||
<em>Klebsiella</em> causes 40% of cases, and 70% of
|
||||
<em>Klebsiella</em> causes 40% of UTIs, and 70% of
|
||||
<em>Klebsiella</em> are susceptible.</li>
|
||||
</ul>
|
||||
<p>Then:</p>
|
||||
<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>
|
||||
<p>But in real data, incidence and susceptibility are <strong>estimated
|
||||
from samples</strong>, so they carry uncertainty. WISCA models this
|
||||
<strong>probabilistically</strong>, using conjugate Bayesian
|
||||
<p>That 82% is a far more clinically meaningful number than the
|
||||
species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
|
||||
reported separately in a traditional antibiogram, because it directly
|
||||
answers the question the clinician actually faces.</p>
|
||||
<p>But in real data, both incidence and susceptibility are
|
||||
<strong>estimated from finite samples</strong>, so they carry
|
||||
uncertainty. A sample of 50 isolates is not a census. WISCA models this
|
||||
uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
|
||||
distributions.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-bayesian-engine-behind-wisca">The Bayesian engine behind WISCA<a class="anchor" aria-label="anchor" href="#the-bayesian-engine-behind-wisca"></a>
|
||||
<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
|
||||
@@ -180,27 +189,38 @@ distributions.</p>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
|
||||
be the number of pathogens,</li>
|
||||
<li>
|
||||
<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">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math>
|
||||
be a <strong>Dirichlet</strong> prior (uniform),</li>
|
||||
<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>
|
||||
be a
|
||||
<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>
|
||||
prior (uniform, non-informative),</li>
|
||||
<li>
|
||||
<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">n = (n_1, \ldots, n_K)</annotation></semantics></math>
|
||||
be the observed counts per species.</li>
|
||||
<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>
|
||||
be the observed isolate counts per species.</li>
|
||||
</ul>
|
||||
<p>Then the posterior incidence is:</p>
|
||||
<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">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
|
||||
<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>
|
||||
<p>To simulate from this, we use:</p>
|
||||
<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>
|
||||
<p>The Dirichlet is the conjugate prior for multinomial data. With the
|
||||
non-informative prior
|
||||
<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>,
|
||||
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,
|
||||
reflecting genuine uncertainty, and the resulting credible intervals
|
||||
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>
|
||||
</h3>
|
||||
<p>Each pathogen–regimen pair has a prior and data:</p>
|
||||
<p>Each pathogen-regimen pair has a prior and observed data:</p>
|
||||
<ul>
|
||||
<li>Prior:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>,
|
||||
with default
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Default prior:
|
||||
<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>
|
||||
(Jeffreys prior)</li>
|
||||
<li>Intrinsically resistant pairs:
|
||||
<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>,
|
||||
forcing near-zero susceptibility regardless of observed data (based on
|
||||
EUCAST Expected Resistant Phenotypes)</li>
|
||||
<li>Data:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
|
||||
susceptible out of
|
||||
@@ -224,27 +244,76 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<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>
|
||||
</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><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math>
|
||||
<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×) and summarise:</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</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-and-simulate-synthetic-syndrome">Prepare data and simulate synthetic syndrome<a class="anchor" aria-label="anchor" href="#prepare-data-and-simulate-synthetic-syndrome"></a>
|
||||
<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<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>
|
||||
@@ -271,13 +340,13 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<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 fake syndrome column</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">"No UTI"</span><span class="op">)</span></span></code></pre></div>
|
||||
<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-antibiogram">Basic WISCA antibiogram<a class="anchor" aria-label="anchor" href="#basic-wisca-antibiogram"></a>
|
||||
<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<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>
|
||||
@@ -288,16 +357,19 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<th align="left">Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">73.7% (71.7-75.8%)</td>
|
||||
<td align="left">77% (74.3-79.4%)</td>
|
||||
<td align="left">72.8% (70.7-74.8%)</td>
|
||||
<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>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<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>
|
||||
@@ -313,16 +385,18 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">73.8% (71.8-75.7%)</td>
|
||||
<td align="left">87.5% (85.9-89%)</td>
|
||||
<td align="left">89.7% (88.2-91.1%)</td>
|
||||
<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>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<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>
|
||||
@@ -342,22 +416,22 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">No UTI</td>
|
||||
<td align="left">70.1% (67.8-72.3%)</td>
|
||||
<td align="left">85.2% (83.1-87.2%)</td>
|
||||
<td align="left">87.1% (85.3-88.7%)</td>
|
||||
<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">80.9% (77.7-83.8%)</td>
|
||||
<td align="left">88.2% (85.7-90.5%)</td>
|
||||
<td align="left">90.9% (88.7-93%)</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="cb6"><pre class="downlit sourceCode r">
|
||||
<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>
|
||||
@@ -378,20 +452,36 @@ all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></c
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">No UCI</td>
|
||||
<td align="left">70% (67.8-72.4%)</td>
|
||||
<td align="left">85.3% (83.3-87.2%)</td>
|
||||
<td align="left">87% (85.3-88.8%)</td>
|
||||
<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">80.9% (77.7-83.9%)</td>
|
||||
<td align="left">88.2% (85.5-90.6%)</td>
|
||||
<td align="left">90.9% (88.7-93%)</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>
|
||||
@@ -407,14 +497,43 @@ 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</li>
|
||||
<li>No adjustment for patient-level covariates, although these could be
|
||||
passed onto the <code>syndromic_group</code> argument</li>
|
||||
<li>WISCA does not model resistance over time, you might want to use
|
||||
<code>tidymodels</code> for that, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
|
||||
<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>
|
||||
@@ -424,24 +543,48 @@ basic introduction</a>
|
||||
</h2>
|
||||
<p>WISCA enables:</p>
|
||||
<ul>
|
||||
<li>Empirical regimen comparison,</li>
|
||||
<li>Syndrome-specific coverage estimation,</li>
|
||||
<li>Fully probabilistic interpretation.</li>
|
||||
<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="cb7"><pre class="downlit sourceCode r">
|
||||
<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="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
|
||||
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
|
||||
</h2>
|
||||
<p>Bielicki, JA, et al. (2016). <em>Selecting appropriate empirical
|
||||
antibiotic regimens for paediatric bloodstream infections: application
|
||||
of a Bayesian decision model to local and pooled antimicrobial
|
||||
resistance surveillance data.</em> <strong>J Antimicrob
|
||||
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</li>
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||||
</ol>
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</div>
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