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@@ -33,7 +33,7 @@
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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="">3.0.1.9083</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9084</small>
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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@@ -112,7 +112,7 @@
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<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
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</h2>
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<p>The <code>AMR</code> package is a peer-reviewed, <a href="#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="./authors.html">many different researchers</a> from around the globe to make this a successful and durable project! The <code>AMR</code> package was already cited <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC" class="external-link">over 100 times</a> in scientific research.</p>
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<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~97 000 distinct microbial species</strong></a> (updated mei 2026) and all <a href="./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 2011-2026 and EUCAST 2011-2026 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>
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<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~97 000 distinct microbial species</strong></a> (updated May 2026) and all <a href="./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 2011-2026 and EUCAST 2011-2026 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>
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<div class="section level3">
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<h3 id="used-in-over-175-countries-available-in-28-languages">Used in over 175 countries, available in 28 languages<a class="anchor" aria-label="anchor" href="#used-in-over-175-countries-available-in-28-languages"></a>
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</h3>
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@@ -145,13 +145,11 @@
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<span><span class="co">#> ℹ Using column mo as input for `mo_fullname()`</span></span>
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<span><span class="co">#> ℹ Using column mo as input for `mo_is_gram_negative()`</span></span>
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<span><span class="co">#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`</span></span>
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<span><span class="co">#> ℹ Determining intrinsic resistance based on 'EUCAST Expected</span></span>
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<span><span class="co">#> Resistant Phenotypes' v1.2 (2023). This note will be shown</span></span>
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<span><span class="co">#> once per session.</span></span>
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<span><span class="co">#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB</span></span>
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<span><span class="co">#> (tobramycin), AMK (amikacin), and KAN (kanamycin)</span></span>
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<span><span class="co">#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM</span></span>
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<span><span class="co">#> (meropenem)</span></span>
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<span><span class="co">#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023).</span></span>
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<span><span class="co">#> This note will be shown once per session.</span></span>
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<span><span class="co">#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN</span></span>
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<span><span class="co">#> (kanamycin)</span></span>
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<span><span class="co">#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)</span></span>
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<span><span class="co">#> # A tibble: 35 × 7</span></span>
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<span><span class="co">#> bacteria GEN TOB AMK KAN IPM MEM </span></span>
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<span><span class="co">#> <chr> <sir> <sir> <sir> <sir> <sir> <sir></span></span>
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@@ -180,9 +178,9 @@
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<span><span class="co">#> Warning: invalid microorganism code, NA generated</span></span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="24%">
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<col width="37%">
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<col width="37%">
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<col width="33%">
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<col width="33%">
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<col width="33%">
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</colgroup>
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<thead><tr>
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<th align="left">Piperacillin/tazobactam</th>
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@@ -190,9 +188,9 @@
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<th align="left">Piperacillin/tazobactam + Tobramycin</th>
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</tr></thead>
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<tbody><tr>
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<td align="left">70.1% (65.1-75.4%)</td>
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<td align="left">93.6% (92.1-95%)</td>
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<td align="left">89.8% (87.3-92.4%)</td>
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<td align="left">70% (64.8-75.2%)</td>
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<td align="left">93.6% (92-95.1%)</td>
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<td align="left">89.9% (87.1-92.5%)</td>
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</tr></tbody>
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</table>
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<p>WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:</p>
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@@ -204,10 +202,10 @@
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<span><span class="co">#> Warning: invalid microorganism code, NA generated</span></span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="14%">
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<col width="21%">
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<col width="32%">
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<col width="32%">
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<col width="25%">
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<col width="25%">
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<col width="25%">
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<col width="25%">
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</colgroup>
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<thead><tr>
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<th align="left">Syndromic Group</th>
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<tbody>
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<tr>
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<td align="left">Clinical</td>
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<td align="left">74.4% (68.2-79.9%)</td>
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<td align="left">93.6% (91.9-95.1%)</td>
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<td align="left">90.4% (86.9-93.3%)</td>
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<td align="left">74.6% (69.3-80.3%)</td>
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<td align="left">93.6% (92.1-95%)</td>
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<td align="left">90.4% (87-93.2%)</td>
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</tr>
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<tr>
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<td align="left">ICU</td>
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<td align="left">57% (48.6-65.9%)</td>
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<td align="left">86.8% (83.4-89.8%)</td>
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<td align="left">82.9% (77.5-87.1%)</td>
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<td align="left">56.9% (48.2-66.3%)</td>
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<td align="left">86.7% (83.4-89.7%)</td>
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<td align="left">82.9% (78.1-87.3%)</td>
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</tr>
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<tr>
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<td align="left">Outpatient</td>
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<td align="left">57.5% (45.9-69.3%)</td>
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<td align="left">76.6% (70.6-82.3%)</td>
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<td align="left">67.9% (57.6-77.2%)</td>
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<td align="left">57.3% (45.8-69.1%)</td>
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<td align="left">76.6% (70.6-81.9%)</td>
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<td align="left">67.9% (58-76.9%)</td>
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</tr>
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</tbody>
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</table>
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@@ -241,15 +239,14 @@
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<code class="sourceCode R"><span><span class="fu"><a href="reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
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<span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span>
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<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="fu"><a href="reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"TZP"</span><span class="op">)</span><span class="op">)</span></span>
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<span><span class="co">#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM</span></span>
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<span><span class="co">#> (meropenem)</span></span></code></pre></div>
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<span><span class="co">#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)</span></span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="13%">
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<col width="25%">
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<col width="18%">
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<col width="19%">
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<col width="22%">
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<col width="20%">
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<col width="20%">
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<col width="20%">
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<col width="20%">
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<col width="20%">
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</colgroup>
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<thead><tr>
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<th align="left">Pathogen</th>
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@@ -282,10 +279,10 @@
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<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">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="12%">
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<col width="21%">
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<col width="32%">
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<col width="32%">
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<col width="25%">
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<col width="25%">
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<col width="25%">
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<col width="25%">
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</colgroup>
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<thead><tr>
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<th align="left">Pathogen</th>
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@@ -407,16 +404,15 @@
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<span> <span class="co"># calculate AMR using resistance(), over all aminoglycosides and polymyxins:</span></span>
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<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><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="fu"><a href="reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antimicrobial_selectors.html">polymyxins</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
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<span> <span class="va">resistance</span><span class="op">)</span><span class="op">)</span></span>
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<span><span class="co">#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB</span></span>
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<span><span class="co">#> (tobramycin), AMK (amikacin), and KAN (kanamycin)</span></span>
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<span><span class="co">#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN</span></span>
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<span><span class="co">#> (kanamycin)</span></span>
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<span><span class="co">#> ℹ For `polymyxins()` using column COL (colistin)</span></span>
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<span><span class="co">#> Warning: There was 1 warning in `summarise()`.</span></span>
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<span><span class="co">#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),</span></span>
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<span><span class="co">#> resistance)`.</span></span>
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<span><span class="co">#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.</span></span>
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<span><span class="co">#> ℹ In group 3: `ward = "Outpatient"`.</span></span>
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<span><span class="co">#> Caused by warning:</span></span>
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<span><span class="co">#> ! Introducing NA: only 23 results available for KAN in group:</span></span>
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<span><span class="co">#> ward = "Outpatient" (whilst `minimum = 30`).</span></span>
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<span><span class="co">#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (whilst `minimum =</span></span>
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<span><span class="co">#> 30`).</span></span>
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<span><span class="va">out</span></span>
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<span><span class="co">#> # A tibble: 3 × 6</span></span>
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<span><span class="co">#> ward GEN TOB AMK KAN COL</span></span>
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