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page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
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generated on 29 April 2025.</p>
generated on 30 April 2025.</p>
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<img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
<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>
<div class="d-none name"><code>WISCA.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<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:</p>
<blockquote>
<p><strong>Weighted-Incidence Syndromic Combination
Antibiogram</strong></p>
</blockquote>
<p>WISCA is a Bayesian approach that integrates: - <strong>Pathogen
prevalence</strong> (how often each species causes the syndrome), -
<strong>Regimen susceptibility</strong> (how often a regimen works
<em>if</em> the pathogen is known),</p>
<p>to estimate the <strong>overall empirical coverage</strong> of
antimicrobial regimens — with quantified uncertainty.</p>
<p>This vignette explains how WISCA works, why it is useful, and how to
apply it in <strong>AMR</strong>.</p>
<hr>
</div>
<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>
<p>``` Species → Antibiotic → Susceptibility %</p>
<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: - Fragment information by organism, - Do
not weight by real-world prevalence, - Do not account for combination
therapy or sample size, - Do not provide uncertainty.</p>
<hr>
</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>
<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: - <strong>Incidence</strong> of each
pathogen in the syndrome, - <strong>Susceptibility</strong> of each
pathogen to the regimen.</p>
<p>We can write this as:</p>
<p>``` coverage = ∑ (pathogen incidence × susceptibility)</p>
<p>For example, suppose: - E. coli causes 60% of cases, and 90% of
<em>E. coli</em> are susceptible to a drug. - Klebsiella causes 40% of
cases, and 70% of <em>Klebsiella</em> are susceptible.</p>
<p>Then:</p>
<p>``` coverage = (0.6 × 0.9) + (0.4 × 0.7) = 0.82</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
distributions.</p>
<hr>
</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>
<div class="section level3">
<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
</h3>
<p>Let: - K be the number of pathogens, -
<code>α = (1, 1, ..., 1) be a **Dirichlet** prior (uniform), -</code> n
= (n₁, …, nₖ) be the observed counts per species.</p>
<p>Then the posterior incidence follows:</p>
<p>``` incidence ∼ Dirichlet(α + n)</p>
<p>In simulations, we draw from this posterior using:</p>
<p>``` xᵢ ∼ Gamma(αᵢ + nᵢ, 1)</p>
<p>``` incidenceᵢ = xᵢ / ∑ xⱼ</p>
<hr>
</div>
<div class="section level3">
<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
</h3>
<p>Each pathogen–regimen pair has: - <code>prior: Beta(1, 1) -</code>
data: S susceptible out of N tested</p>
<p>Then:</p>
<p>``` susceptibility ∼ Beta(1 + S, 1 + (N - S))</p>
<p>In each simulation, we draw random susceptibility per species from
this Beta distribution.</p>
<hr>
</div>
<div class="section level3">
<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
</h3>
<p>Putting it together:</p>
<p>``` For each simulation: - Draw incidence ∼ Dirichlet - Draw
susceptibility ∼ Beta - Multiply → coverage estimate</p>
<p>We repeat this (e.g. 1000×) and summarise: - <strong>Mean</strong>:
expected coverage - <strong>Quantiles</strong>: credible interval
(default 95%)</p>
<hr>
</div>
</div>
<div class="section level2">
<h2 id="practical-use-in-amr">Practical use in AMR<a class="anchor" aria-label="anchor" href="#practical-use-in-amr"></a>
</h2>
<div class="section level3">
<h3 id="simulate-a-synthetic-syndrome">Simulate a synthetic syndrome<a class="anchor" aria-label="anchor" href="#simulate-a-synthetic-syndrome"></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">&lt;-</span> <span class="va">example_isolates</span></span>
<span></span>
<span><span class="co"># Add a fake syndrome column for stratification</span></span>
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</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">"Other"</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>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
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<th align="left">Trimethoprim</th>
<th align="left">Trimethoprim/sulfamethoxazole</th>
<th align="left">Vancomycin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">41.7% (37.2-47.5%)</td>
<td align="left">35.7% (33.3-38.2%)</td>
<td align="left">73.7% (71.7-75.8%)</td>
<td align="left">35.8% (33.6-38.1%)</td>
<td align="left">43.8% (41.5-46%)</td>
<td align="left">28.2% (25.8-30.8%)</td>
<td align="left">69.3% (64.9-73.8%)</td>
<td align="left">74.8% (70.5-78.8%)</td>
<td align="left">73.3% (69.2-77.3%)</td>
<td align="left">69.6% (65.5-73.7%)</td>
<td align="left">35.9% (33.6-38.2%)</td>
<td align="left">73.3% (68.9-77.2%)</td>
<td align="left">71.9% (69.8-74%)</td>
<td align="left">64.9% (51.7-78.5%)</td>
<td align="left">77% (74.5-79.6%)</td>
<td align="left">47.3% (44.7-49.6%)</td>
<td align="left">33% (30.8-35.1%)</td>
<td align="left">63.6% (52.1-74.9%)</td>
<td align="left">43.7% (41.6-46%)</td>
<td align="left">59.3% (47-71%)</td>
<td align="left">60.5% (55.5-65.8%)</td>
<td align="left">72.7% (70.7-74.8%)</td>
<td align="left">78.2% (74-82.2%)</td>
<td align="left">25.6% (13.5-37.7%)</td>
<td align="left">54.9% (50.4-59%)</td>
<td align="left">77.1% (72.8-81.2%)</td>
<td align="left">56.1% (39.5-70.7%)</td>
<td align="left">49.6% (43.6-55.6%)</td>
<td align="left">65.2% (52.7-78.1%)</td>
<td align="left">76.5% (69.4-82.3%)</td>
<td align="left">57.8% (45.4-69.6%)</td>
<td align="left">69.4% (64.2-74.2%)</td>
<td align="left">52.4% (47.6-56.8%)</td>
<td align="left">48.1% (43.4-52.9%)</td>
<td align="left">61.4% (53.6-70.5%)</td>
<td align="left">81.9% (78.1-85.5%)</td>
<td align="left">60.7% (57.8-63.5%)</td>
<td align="left">61% (58.8-63.5%)</td>
<td align="left">76.5% (74.5-78.5%)</td>
<td align="left">61.9% (59.8-64.2%)</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="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span>,</span>
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="2%">
<col width="2%">
<col width="2%">
<col width="3%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="3%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="2%">
<col width="3%">
<col width="2%">
</colgroup>
<thead><tr class="header">
<th align="left">Syndromic Group</th>
<th align="left">Amikacin</th>
<th align="left">Amoxicillin</th>
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Ampicillin</th>
<th align="left">Azithromycin</th>
<th align="left">Benzylpenicillin</th>
<th align="left">Cefazolin</th>
<th align="left">Cefepime</th>
<th align="left">Cefotaxime</th>
<th align="left">Cefoxitin</th>
<th align="left">Ceftazidime</th>
<th align="left">Ceftriaxone</th>
<th align="left">Cefuroxime</th>
<th align="left">Chloramphenicol</th>
<th align="left">Ciprofloxacin</th>
<th align="left">Clindamycin</th>
<th align="left">Colistin</th>
<th align="left">Doxycycline</th>
<th align="left">Erythromycin</th>
<th align="left">Flucloxacillin</th>
<th align="left">Fosfomycin</th>
<th align="left">Gentamicin</th>
<th align="left">Imipenem</th>
<th align="left">Kanamycin</th>
<th align="left">Linezolid</th>
<th align="left">Meropenem</th>
<th align="left">Metronidazole</th>
<th align="left">Moxifloxacin</th>
<th align="left">Mupirocin</th>
<th align="left">Nitrofurantoin</th>
<th align="left">Oxacillin</th>
<th align="left">Piperacillin/tazobactam</th>
<th align="left">Rifampicin</th>
<th align="left">Teicoplanin</th>
<th align="left">Tetracycline</th>
<th align="left">Tigecycline</th>
<th align="left">Tobramycin</th>
<th align="left">Trimethoprim</th>
<th align="left">Trimethoprim/sulfamethoxazole</th>
<th align="left">Vancomycin</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Other</td>
<td align="left">25% (20.2-31.7%)</td>
<td align="left">31.6% (28.7-34%)</td>
<td align="left">70.1% (67.7-72.4%)</td>
<td align="left">31.6% (29.1-34.1%)</td>
<td align="left">56.4% (53.8-58.8%)</td>
<td align="left">36.3% (33.1-39.4%)</td>
<td align="left">61.5% (55.7-66.5%)</td>
<td align="left">68.5% (63.4-73.8%)</td>
<td align="left">66.7% (61.4-71.9%)</td>
<td align="left">63% (57.7-68.6%)</td>
<td align="left">18.3% (15.9-20.8%)</td>
<td align="left">66.6% (61.4-71.5%)</td>
<td align="left">65.5% (62.7-68%)</td>
<td align="left">69.6% (60-77.2%)</td>
<td align="left">74% (70.8-77.2%)</td>
<td align="left">60.9% (58.1-63.6%)</td>
<td align="left">13.9% (11.8-15.8%)</td>
<td align="left">67.4% (63.7-70.9%)</td>
<td align="left">56.4% (54-58.9%)</td>
<td align="left">61.4% (56-67.6%)</td>
<td align="left">49.6% (43.2-56.3%)</td>
<td align="left">65.6% (62.8-68.1%)</td>
<td align="left">71.8% (66.7-77%)</td>
<td align="left">18.6% (13.1-25.9%)</td>
<td align="left">70.8% (65.1-75.8%)</td>
<td align="left">70.6% (65.1-75.7%)</td>
<td align="left">49.8% (34.2-66.6%)</td>
<td align="left">63.3% (56.2-70.3%)</td>
<td align="left">69.8% (62.6-76.4%)</td>
<td align="left">70.5% (61.2-77.5%)</td>
<td align="left">60% (54.4-65.4%)</td>
<td align="left">62.4% (56.4-68.6%)</td>
<td align="left">67.6% (61.9-73.2%)</td>
<td align="left">61.9% (55.4-67.6%)</td>
<td align="left">67.8% (64.8-70.6%)</td>
<td align="left">77% (72.3-81.8%)</td>
<td align="left">50.1% (46.7-53.6%)</td>
<td align="left">61.1% (58.4-64%)</td>
<td align="left">78.8% (76.4-80.9%)</td>
<td align="left">79.6% (77.4-81.8%)</td>
</tr>
<tr class="even">
<td align="left">UTI</td>
<td align="left">91.5% (88.8-93.5%)</td>
<td align="left">50% (45.5-54.6%)</td>
<td align="left">80.9% (77.8-84%)</td>
<td align="left">49.9% (45.6-54.3%)</td>
<td align="left">8.2% (6.4-10.5%)</td>
<td align="left">8.2% (6.3-10.3%)</td>
<td align="left">88.9% (84.2-92.3%)</td>
<td align="left">89.4% (86.5-91.8%)</td>
<td align="left">89.9% (87.4-92.1%)</td>
<td align="left">86.1% (82.9-88.9%)</td>
<td align="left">89.8% (87.2-91.9%)</td>
<td align="left">89.8% (87.1-92.1%)</td>
<td align="left">87.4% (84.5-89.8%)</td>
<td align="left">NA</td>
<td align="left">81.4% (78.3-84.3%)</td>
<td align="left">8.2% (6.3-10.4%)</td>
<td align="left">91.7% (89.6-93.8%)</td>
<td align="left">NA</td>
<td align="left">8.1% (6.3-10.4%)</td>
<td align="left">NA</td>
<td align="left">90.6% (86.5-93.3%)</td>
<td align="left">90.2% (87.9-92.2%)</td>
<td align="left">91.8% (89.7-93.8%)</td>
<td align="left">NA</td>
<td align="left">8.1% (6.1-10.2%)</td>
<td align="left">91.8% (89.6-93.8%)</td>
<td align="left">71.4% (31.8-91.6%)</td>
<td align="left">9.3% (6.7-13.3%)</td>
<td align="left">NA</td>
<td align="left">89.4% (86.9-91.7%)</td>
<td align="left">NA</td>
<td align="left">87.2% (84.4-89.6%)</td>
<td align="left">8.2% (6.3-10.4%)</td>
<td align="left">8.2% (6.3-10.3%)</td>
<td align="left">41.2% (14.3-74.4%)</td>
<td align="left">90.9% (87.7-93.3%)</td>
<td align="left">89.6% (87.1-91.8%)</td>
<td align="left">59.1% (54.7-63.4%)</td>
<td align="left">65.3% (61.3-69.2%)</td>
<td align="left">8.2% (6.2-10.3%)</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>The <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function supports combination
regimens:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</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">"GEN"</span>, <span class="st">"AMC + GEN"</span>, <span class="st">"CIP"</span><span class="op">)</span>,</span>
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="26%">
<col width="39%">
<col width="16%">
<col width="18%">
</colgroup>
<thead><tr class="header">
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
<th align="left">Ciprofloxacin</th>
<th align="left">Gentamicin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">73.8% (71.7-75.8%)</td>
<td align="left">89.7% (88.2-91.2%)</td>
<td align="left">77% (74.3-79.6%)</td>
<td align="left">72.8% (70.6-74.9%)</td>
</tr></tbody>
</table>
<hr>
</div>
</div>
<div class="section level2">
<h2 id="interpretation">Interpretation<a class="anchor" aria-label="anchor" href="#interpretation"></a>
</h2>
<p>Suppose you get this output:</p>
<table class="table">
<thead><tr class="header">
<th>Regimen</th>
<th>Coverage</th>
<th>Lower_CI</th>
<th>Upper_CI</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>AMC</td>
<td>0.72</td>
<td>0.65</td>
<td>0.78</td>
</tr>
<tr class="even">
<td>AMC + GEN</td>
<td>0.88</td>
<td>0.83</td>
<td>0.93</td>
</tr>
</tbody>
</table>
<p>Interpretation:</p>
<blockquote>
<p><em>“AMC + GEN covers 88% of expected pathogens for this syndrome,
with 95% certainty that the true coverage lies between 83% and
93%.”</em></p>
</blockquote>
<p>Regimens with few tested isolates will show <strong>wider
intervals</strong>.</p>
<hr>
</div>
<div class="section level2">
<h2 id="sensible-defaults-but-you-can-customise">Sensible defaults, but you can customise<a class="anchor" aria-label="anchor" href="#sensible-defaults-but-you-can-customise"></a>
</h2>
<ul>
<li>
<code>minimum = 30</code>: exclude regimens with &lt;30 isolates
tested.</li>
<li>
<code>simulations = 1000</code>: number of Monte Carlo samples.</li>
<li>
<code>conf_interval = 0.95</code>: coverage interval width.</li>
<li>
<code>combine_SI = TRUE</code>: count “I”/“SDD” as susceptible.</li>
</ul>
<hr>
</div>
<div class="section level2">
<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
</h2>
<ul>
<li>WISCA does not model time trends or temporal resistance shifts.</li>
<li>It assumes data are representative of current clinical
practice.</li>
<li>It does not account for patient-level covariates (yet).</li>
<li>Species-specific data are abstracted into syndrome-level
estimates.</li>
</ul>
<hr>
</div>
<div class="section level2">
<h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
</h2>
<p>Bielicki JA et al. (2016).<br><em>Weighted-incidence syndromic combination antibiograms to guide
empiric treatment in pediatric bloodstream infections.</em><br><strong>J Antimicrob Chemother</strong>, 71(2):529–536. <a href="doi:10.1093/jac/dkv397" class="uri">doi:10.1093/jac/dkv397</a></p>
<hr>
</div>
<div class="section level2">
<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
</h2>
<p>WISCA shifts empirical therapy from simple percent susceptible toward
<strong>probabilistic, syndrome-based decision support</strong>. It is a
statistically principled, clinically intuitive method to guide regimen
selection — and easy to use via the <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function
in the <strong>AMR</strong> package.</p>
<p>For antimicrobial stewardship teams, it enables
<strong>disease-specific, reproducible, and data-driven
guidance</strong> — even in the face of sparse data.</p>
</div>
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<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">29 April 2025</h4>
<h4 data-toc-skip class="date">30 April 2025</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
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</dd><dt><a href="PCA.html">How to conduct principal component analysis (PCA) for AMR</a></dt>
<dd>
</dd><dt><a href="welcome_to_AMR.html">Welcome to the `AMR` package</a></dt>
<dd>
</dd><dt><a href="WHONET.html">How to work with WHONET data</a></dt>
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</dd><dt><a href="WISCA.html">Estimating Empirical Coverage with WISCA</a></dt>
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<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
<li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
<li><a class="dropdown-item" href="../articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
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<img src="../logo.svg" class="logo" alt=""><h1>Welcome to the `AMR` package</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/welcome_to_AMR.Rmd" class="external-link"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
<div class="d-none name"><code>welcome_to_AMR.Rmd</code></div>
</div>
<p>Note: to keep the package size as small as possible, we only include
this vignette on CRAN. You can read more vignettes on our website about
how to conduct AMR data analysis, determine MDROs, find explanation of
EUCAST and CLSI breakpoints, and much more: <a href="https://amr-for-r.org" class="uri">https://amr-for-r.org</a>.</p>
<hr>
<p>The <code>AMR</code> package is a peer-reviewed, <a href="https://amr-for-r.org/#copyright">free and open-source</a> R
package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
dependencies</a> to simplify the analysis and prediction of
Antimicrobial Resistance (AMR) and to work with microbial and
antimicrobial data and properties, by using evidence-based methods.
<strong>Our aim is to provide a standard</strong> for clean and
reproducible AMR data analysis, that can therefore empower
epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting. We are a team of <a href="https://amr-for-r.org/authors.html">many different researchers</a>
from around the globe to make this a successful and durable project!</p>
<p>This work was published in the Journal of Statistical Software
(Volume 104(3); ) and formed the basis of two PhD theses ( and ).</p>
<p>After installing this package, R knows <a href="https://amr-for-r.org/reference/microorganisms.html"><strong>~79
000 distinct microbial species</strong></a> (updated June 2024) and all
<a href="https://amr-for-r.org/reference/antimicrobials.html"><strong>~620
antimicrobial and antiviral drugs</strong></a> by name and code
(including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and
knows all about valid SIR and MIC values. The integral clinical
breakpoint guidelines from CLSI 2011-2025 and EUCAST 2011-2025 are
included, even with epidemiological cut-off (ECOFF) values. It supports
and can read any data format, including WHONET data. This package works
on Windows, macOS and Linux with all versions of R since R-3.0 (April
2013). <strong>It was designed to work in any setting, including those
with very limited resources</strong>. It was created for both routine
data analysis and academic research at the Faculty of Medical Sciences
of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a> and the
<a href="https://www.umcg.nl" class="external-link">University Medical Center
Groningen</a>.</p>
<p>The <code>AMR</code> package is available in English, Chinese, Czech,
Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese,
Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish,
Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial
microorganism names are provided in these languages.</p>
<p>This package was intended as a comprehensive toolbox for integrated
AMR data analysis. This package can be used for:</p>
<ul>
<li>Reference for the taxonomy of microorganisms, since the package
contains all microbial (sub)species from the List of Prokaryotic names
with Standing in Nomenclature (<a href="(https://lpsn.dsmz.de)" class="external-link">LPSN</a>) and the Global Biodiversity
Information Facility (<a href="https://www.gbif.org" class="external-link">GBIF</a>) (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Interpreting raw MIC and disk diffusion values, based on any CLSI or
EUCAST guideline (<a href="https://amr-for-r.org/reference/as.sir.html">manual</a>)</li>
<li>Retrieving antimicrobial drug names, doses and forms of
administration from clinical health care records (<a href="https://amr-for-r.org/reference/ab_from_text.html">manual</a>)</li>
<li>Determining first isolates to be used for AMR data analysis (<a href="https://amr-for-r.org/reference/first_isolate.html">manual</a>)</li>
<li>Calculating antimicrobial resistance (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant
organisms (MDRO) (<a href="https://amr-for-r.org/articles/MDR.html">tutorial</a>)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and
combination therapies (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Apply AMR function in predictive modelling (<a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">tutorial</a>)</li>
<li>Getting properties for any microorganism (like Gram stain, species,
genus or family) (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Getting properties for any antimicrobial (like name, code of
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) (<a href="https://amr-for-r.org/reference/ab_property.html">manual</a>)</li>
<li>Plotting antimicrobial resistance (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Applying EUCAST expert rules (<a href="https://amr-for-r.org/reference/eucast_rules.html">manual</a>)</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a
microorganism based on a SNOMED code (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an
antibiotic based on a LOINC code (<a href="https://amr-for-r.org/reference/ab_property.html">manual</a>)</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2021 to
translate MIC values and disk diffusion diameters to SIR (<a href="https://amr-for-r.org/articles/datasets.html">link</a>)</li>
<li>Principal component analysis for AMR (<a href="https://amr-for-r.org/articles/PCA.html">tutorial</a>)</li>
</ul>
<p>All reference data sets in the AMR package - including information on
microorganisms, antimicrobials, and clinical breakpoints - are freely
available for download in multiple formats: R, MS Excel, Apache Feather,
Apache Parquet, SPSS, and Stata.</p>
<p>For maximum compatibility, we also provide machine-readable,
tab-separated plain text files suitable for use in any software,
including laboratory information systems.</p>
<p>Visit <a href="https://amr-for-r.org/articles/datasets.html">our
website for direct download links</a>, or explore the actual files in <a href="https://github.com/msberends/AMR/tree/main/data-raw/datasets" class="external-link">our
GitHub repository</a>.</p>
<hr>
<p><small> This AMR package for R is free, open-source software and
licensed under the <a href="https://amr-for-r.org/LICENSE-text.html">GNU
General Public License v2.0 (GPL-2)</a>. These requirements are
consequently legally binding: modifications must be released under the
same license when distributing the package, changes made to the code
must be documented, source code must be made available when the package
is distributed, and a copy of the license and copyright notice must be
included with the package. </small></p>
</main>
</div>
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<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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<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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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9263</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9266</small>
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@ -49,20 +49,79 @@
</div>
<div class="section level2">
<h2 class="pkg-version" data-toc-text="2.1.1.9263" id="amr-2119263">AMR 2.1.1.9263<a class="anchor" aria-label="anchor" href="#amr-2119263"></a></h2>
<h2 class="pkg-version" data-toc-text="2.1.1.9266" id="amr-2119266">AMR 2.1.1.9266<a class="anchor" aria-label="anchor" href="#amr-2119266"></a></h2>
<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://amr-for-r.org/#get-this-package">the instructions here</a>.)</em></p>
<div class="section level5">
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9263">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9263"></a></h5>
<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
</div>
<div class="section level3">
<h3 id="breaking-2-1-1-9263">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9263"></a></h3>
<h3 id="tldr-2-1-1-9266">tl;dr<a class="anchor" aria-label="anchor" href="#tldr-2-1-1-9266"></a></h3>
<ul><li>
<strong>Scope Expansion</strong>: One Health support (Human + Veterinary + Environmental microbiology).</li>
<li>
<strong>Data Updates</strong>:
<ul><li>
<code>antibiotics</code> renamed to <code>antimicrobials</code>.</li>
<li>Veterinary antimicrobials and WHOCC codes added.</li>
<li>MycoBank fungal taxonomy integrated (+20,000 fungi).</li>
</ul></li>
<li>
<strong>Breakpoints &amp; Interpretations</strong>:
<ul><li>CLSI/EUCAST 2024-2025 breakpoints added; EUCAST 2025 default.</li>
<li>
<code><a href="../reference/as.sir.html">as.sir()</a></code> supports NI/SDD levels; parallel computation enabled.</li>
<li>Custom S/I/R/SDD/NI definitions allowed.</li>
<li>Improved handling of capped MICs.</li>
</ul></li>
<li>
<strong>New Tools &amp; Functions</strong>:
<ul><li>WISCA antibiogram support (<code><a href="../reference/antibiogram.html">antibiogram()</a></code>, <code><a href="../reference/antibiogram.html">wisca()</a></code>).</li>
<li>New ggplot2 extensions: <code>scale_*_mic()</code>, <code>scale_*_sir()</code>, <code><a href="../reference/as.mic.html">rescale_mic()</a></code>.</li>
<li>New utility functions: <code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>, <code><a href="../reference/mo_property.html">mo_group_members()</a></code>, <code><a href="../reference/as.mic.html">mic_p50()</a></code>, <code><a href="../reference/as.mic.html">mic_p90()</a></code>.</li>
</ul></li>
<li>
<strong>Predictive Modelling</strong>:
<ul><li>Full tidymodels compatibility for antimicrobial selectors.</li>
<li>Deprecated <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code> and <code><a href="../reference/resistance_predict.html">sir_predict()</a></code>.</li>
</ul></li>
<li>
<strong>Python Compatibility</strong>: AMR R package now runs in Python.</li>
<li>
<strong>Selector Improvements</strong>:
<ul><li>Added selectors (<code><a href="../reference/antimicrobial_selectors.html">isoxazolylpenicillins()</a></code>, <code><a href="../reference/antimicrobial_selectors.html">monobactams()</a></code>, <code><a href="../reference/antimicrobial_selectors.html">nitrofurans()</a></code>, <code><a href="../reference/antimicrobial_selectors.html">phenicols()</a></code>, <code><a href="../reference/antimicrobial_selectors.html">rifamycins()</a></code>, and <code><a href="../reference/antimicrobial_selectors.html">sulfonamides()</a></code>)</li>
<li>Selectors renamed from <code>ab_*</code> to <code>amr_*</code>; old names deprecated.</li>
</ul></li>
<li>
<strong>MIC/Disks Handling</strong>:
<ul><li>MIC strict comparisons, added levels.</li>
<li>Disk diffusion range expanded (0–50 mm).</li>
</ul></li>
<li>
<strong>EUCAST Rules and MDROs</strong>:
<ul><li>EUCAST v12–v15 rules implemented.</li>
<li>Dutch MDRO 2024 guideline support in <code><a href="../reference/mdro.html">mdro()</a></code>.</li>
</ul></li>
<li>
<strong>Infrastructure</strong>:
<ul><li>New website: <a href="https://amr-for-r.org" class="uri">https://amr-for-r.org</a>.</li>
<li>Improved <code>vctrs</code> integration for tidyverse workflows.</li>
<li>Dropped SAS <code>.xpt</code> file support.</li>
</ul></li>
<li>
<strong>Other Fixes &amp; Enhancements</strong>:
<ul><li>Faster microorganism identification.</li>
<li>Improved antimicrobial and MIC handling.</li>
<li>Extended documentation, additional contributors acknowledged.</li>
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="full-changelog-2-1-1-9266">Full Changelog<a class="anchor" aria-label="anchor" href="#full-changelog-2-1-1-9266"></a></h3>
<div class="section level4">
<h4 id="breaking-2-1-1-9266">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9266"></a></h4>
<ul><li>Dataset <code>antibiotics</code> has been renamed to <code>antimicrobials</code> as the data set contains more than just antibiotics. Using <code>antibiotics</code> will still work, but now returns a warning.</li>
<li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over two years ago.</li>
<li>Functions <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code> and <code><a href="../reference/resistance_predict.html">sir_predict()</a></code> is now deprecated and will be removed in a future version. Use the <code>tidymodels</code> framework instead, 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 level3">
<h3 id="new-2-1-1-9263">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9263"></a></h3>
<div class="section level4">
<h4 id="new-2-1-1-9266">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9266"></a></h4>
<ul><li>
<strong>One Health implementation</strong>
<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
@ -116,8 +175,8 @@
<li>New functions <code><a href="../reference/as.mic.html">mic_p50()</a></code> and <code><a href="../reference/as.mic.html">mic_p90()</a></code> to retrieve the 50th and 90th percentile of MIC values.</li>
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="changed-2-1-1-9263">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9263"></a></h3>
<div class="section level4">
<h4 id="changed-2-1-1-9266">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9266"></a></h4>
<ul><li>SIR interpretation
<ul><li>Support for parallel computing to greatly improve speed using the <code>parallel</code> package (part of base R). Use <code>as.sir(your_data, parallel = TRUE)</code> to run SIR interpretation using multiple cores.</li>
<li>It is now possible to use column names for arguments <code>guideline</code>, <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
@ -198,9 +257,9 @@
</ul></li>
<li>Added console colours support of <code>sir</code> class for Positron</li>
</ul></div>
<div class="section level3">
<h3 id="other-2-1-1-9263">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9263"></a></h3>
<ul><li>New website domain: <a href="https://amr-for-r.org" class="uri">https://amr-for-r.org</a>! The old domain (<a href="http://amr-for-r.org" class="external-link uri">http://amr-for-r.org</a>) will remain to work.</li>
<div class="section level4">
<h4 id="other-2-1-1-9266">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9266"></a></h4>
<ul><li>New website domain: <a href="https://amr-for-r.org" class="uri">https://amr-for-r.org</a>! The old domain (<a href="https://msberends.github.io/AMR/" class="external-link uri">https://msberends.github.io/AMR/</a>) will remain to work.</li>
<li>Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way</li>
<li>Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations</li>
<li>Added Prof. Kathryn Holt, Dr. Jane Hawkey, and Dr. Natacha Couto as contributors for their many suggestions, ideas and bugfixes</li>
@ -208,8 +267,9 @@
<li>Greatly updated and expanded documentation</li>
<li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
</ul></div>
</div>
<div class="section level3">
<h3 id="older-versions-2-1-1-9263">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9263"></a></h3>
<h3 id="older-versions-2-1-1-9266">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9266"></a></h3>
<p>This changelog only contains changes from AMR v3.0 (March 2025) and later.</p>
<ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
<li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>

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@ -9,9 +9,9 @@ articles:
EUCAST: EUCAST.html
MDR: MDR.html
PCA: PCA.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2025-04-29T15:20Z
WISCA: WISCA.html
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@ -112,16 +112,16 @@
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 25 25.83014 0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 57 57.24658 31</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 59 59.39726 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 45 45.16164 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 75 75.49041 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 78 78.20274 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 85 85.18904 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 37 37.29863 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 27 27.67123 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 47 47.25479 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 25 25.83288 0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 57 57.24932 31</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 59 59.40000 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 45 45.16438 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 75 75.49315 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 78 78.20548 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 85 85.19178 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 37 37.30137 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 27 27.67397 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 47 47.25753 21</span>
</code></pre></div>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>

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@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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@ -361,26 +361,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
<p>WISCA, as outlined by Bielicki <em>et al.</em> (<a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a>
), 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 decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.</p>
<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>
<p>$$\theta \sim \text{Beta}(\alpha_0, \beta_0)$$</p>
<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>
<p>$$y \sim \text{Binomial}(n, \theta)$$</p>
<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>
<p>$$\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)$$</p>
<p>Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \(\alpha\), where each \(\alpha_i\) corresponds to a specific pathogen. The prior is typically chosen to be uniform (\(\alpha_i = 1\)), reflecting an assumption of equal prior probability across pathogens.</p>
<p>The posterior distribution of pathogen incidence is then given by:</p>
<p>$$\text{Dirichlet}(\alpha_1 + n_1, \alpha_2 + n_2, \dots, \alpha_K + n_K)$$</p>
<p>where \(n_i\) is the number of infections caused by pathogen \(i\) observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:</p>
<p>$$x_i \sim \text{Gamma}(\alpha_i + n_i, 1)$$
$$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
<p>where \(x_i\) represents unnormalised pathogen counts, and \(p_i\) is the normalised proportion for pathogen \(i\).</p>
<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>
<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>. Posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:</p>
<p>$$\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}$$</p>
<p>By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.</p>
<p><strong>Note:</strong> WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.</p>
<p>WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates the probability of empirical coverage for combination regimens.</p>
<p>It weights susceptibility by pathogen prevalence within a clinical syndrome and provides credible intervals around the expected coverage.</p>
<p>For more background, interpretation, and examples, see <a href="https://amr-for-r.org/articles/WISCA.html">the WISCA vignette</a>.</p>
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<h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>

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@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
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@ -489,20 +489,20 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 14 × 18</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index method ab_given mo_given host_given input_given</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-04-29 <span style="color: #949494;">15:21:26</span> 1 DISK ampicillin Strep pn… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-04-29 <span style="color: #949494;">15:21:26</span> 1 DISK AMP Escheric… human 20 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-04-30 <span style="color: #949494;">15:38:32</span> 1 DISK ampicillin Strep pn… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK AMP Escheric… human 20 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-04-29 <span style="color: #949494;">15:21:27</span> 1 DISK GEN Escheric… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-04-29 <span style="color: #949494;">15:21:27</span> 1 DISK TOB Escheric… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 1 MIC AMX B_STRPT_… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 1 MIC AMX B_STRPT_… human 0.01 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 2 MIC AMX B_STRPT_… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 3 MIC AMX B_STRPT_… human 4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 4 MIC AMX B_STRPT_… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> 2025-04-29 <span style="color: #949494;">15:21:35</span> 1 MIC amoxicillin Escheric… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">12</span> 2025-04-29 <span style="color: #949494;">15:21:35</span> 1 MIC cipro Escheric… human 0.256 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">13</span> 2025-04-29 <span style="color: #949494;">15:21:35</span> 1 DISK tobra Escheric… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">14</span> 2025-04-29 <span style="color: #949494;">15:21:35</span> 1 DISK genta Escheric… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK GEN Escheric… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK TOB Escheric… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 1 MIC AMX B_STRPT_… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 1 MIC AMX B_STRPT_… human 0.01 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 2 MIC AMX B_STRPT_… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 3 MIC AMX B_STRPT_… human 4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 4 MIC AMX B_STRPT_… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> 2025-04-30 <span style="color: #949494;">15:38:40</span> 1 MIC amoxicillin Escheric… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">12</span> 2025-04-30 <span style="color: #949494;">15:38:40</span> 1 MIC cipro Escheric… human 0.256 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">13</span> 2025-04-30 <span style="color: #949494;">15:38:41</span> 1 DISK tobra Escheric… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">14</span> 2025-04-30 <span style="color: #949494;">15:38:41</span> 1 DISK genta Escheric… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 11 more variables: ab &lt;ab&gt;, mo &lt;mo&gt;, host &lt;chr&gt;, input &lt;chr&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># outcome &lt;sir&gt;, notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># breakpoint_S_R &lt;chr&gt;, site &lt;chr&gt;</span></span>
@ -684,16 +684,16 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 114 × 18</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index method ab_given mo_given host_given input_given</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-04-29 <span style="color: #949494;">15:21:26</span> 1 DISK ampicillin Strep pneu human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-04-29 <span style="color: #949494;">15:21:26</span> 1 DISK AMP Escherich… human 20 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-04-30 <span style="color: #949494;">15:38:32</span> 1 DISK ampicillin Strep pneu human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK AMP Escherich… human 20 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-04-29 <span style="color: #949494;">15:21:27</span> 1 DISK GEN Escherich… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-04-29 <span style="color: #949494;">15:21:27</span> 1 DISK TOB Escherich… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 1 MIC AMX B_STRPT_P… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 1 MIC AMX B_STRPT_P… human 0.01 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 2 MIC AMX B_STRPT_P… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 3 MIC AMX B_STRPT_P… human 4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-04-29 <span style="color: #949494;">15:21:28</span> 4 MIC AMX B_STRPT_P… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK GEN Escherich… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-04-30 <span style="color: #949494;">15:38:33</span> 1 DISK TOB Escherich… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 1 MIC AMX B_STRPT_P… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 1 MIC AMX B_STRPT_P… human 0.01 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 2 MIC AMX B_STRPT_P… human 2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 3 MIC AMX B_STRPT_P… human 4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-04-30 <span style="color: #949494;">15:38:34</span> 4 MIC AMX B_STRPT_P… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 104 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 11 more variables: ab &lt;ab&gt;, mo &lt;mo&gt;, host &lt;chr&gt;, input &lt;chr&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># outcome &lt;sir&gt;, notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>

2
reference/atc_online.html
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@ -7,7 +7,7 @@
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reference/av_from_text.html
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@ -7,7 +7,7 @@
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reference/av_property.html
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reference/availability.html
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@ -7,7 +7,7 @@
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reference/bug_drug_combinations.html
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@ -7,7 +7,7 @@
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reference/clinical_breakpoints.html
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@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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reference/count.html
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@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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reference/custom_eucast_rules.html
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@ -7,7 +7,7 @@
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reference/dosage.html
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@ -7,7 +7,7 @@
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reference/eucast_rules.html
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@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9263</small>
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@ -130,7 +130,7 @@ Leclercq et al. <strong>EUCAST expert rules in antimicrobial susceptibility test
<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
<dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> indicating whether to overwrite non-<code>NA</code> values (default: <code>FALSE</code>). When <code>FALSE</code>, only non-SIR values are modified (i.e., any value that is not already S, I or R). To ensure compliance with EUCAST guidelines, <strong>this should remain</strong> <code>FALSE</code>, as EUCAST notes often state that an organism "should be tested for susceptibility to individual agents or be reported resistant".</p></dd>
<dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> indicating whether to overwrite existing SIR values (default: <code>FALSE</code>). When <code>FALSE</code>, only non-SIR values are modified (i.e., any value that is not already S, I or R). To ensure compliance with EUCAST guidelines, <strong>this should remain</strong> <code>FALSE</code>, as EUCAST notes often state that an organism "should be tested for susceptibility to individual agents or be reported resistant".</p></dd>
<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>

2
reference/example_isolates.html
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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@ -9,7 +9,7 @@
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@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
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@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
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