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@ -21,7 +21,7 @@ The AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish
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reference/age.html
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@ -7,7 +7,7 @@
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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 25.92055 0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 57 57.33699 31</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 59 59.48767 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 45 45.25205 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 75 75.58082 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 78 78.29315 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 85 85.27945 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 37 37.38904 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 27 27.76164 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 47 47.34521 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 25 25.92329 0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 57 57.33973 31</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 59 59.49041 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 45 45.25479 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 75 75.58356 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 78 78.29589 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 85 85.28219 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 37 37.39178 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 27 27.76438 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 47 47.34795 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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@ -92,7 +92,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
<ul><li><p>Bielicki JA <em>et al.</em> (2016). <strong>Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data</strong> <em>Journal of Antimicrobial Chemotherapy</em> 71(3); <a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a></p></li>
<li><p>Bielicki JA <em>et al.</em> (2020). <strong>Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries</strong> <em>JAMA Netw Open.</em> 3(2):e1921124; <a href="https://doi.org/10.1001.jamanetworkopen.2019.21124" class="external-link">doi:10.1001.jamanetworkopen.2019.21124</a></p></li>
<li><p>Bielicki JA <em>et al.</em> (2020). <strong>Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries</strong> <em>JAMA Netw Open.</em> 3(2):e1921124; <a href="https://doi.org/10.1001/jamanetworkopen.2019.21124" class="external-link">doi:10.1001/jamanetworkopen.2019.21124</a></p></li>
<li><p>Klinker KP <em>et al.</em> (2021). <strong>Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms</strong>. <em>Therapeutic Advances in Infectious Disease</em>, May 5;8:20499361211011373; <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a></p></li>
<li><p>Barbieri E <em>et al.</em> (2021). <strong>Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach</strong> <em>Antimicrobial Resistance &amp; Infection Control</em> May 1;10(1):74; <a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">doi:10.1186/s13756-021-00939-2</a></p></li>
<li><p><strong>M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition</strong>, 2022, <em>Clinical and Laboratory Standards Institute (CLSI)</em>. <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">https://clsi.org/standards/products/microbiology/documents/m39/</a>.</p></li>
@ -254,7 +254,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<p>There are various antibiogram types, as summarised by Klinker <em>et al.</em> (2021, <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a>
), and they are all supported by <code>antibiogram()</code>.</p>
<p>For clinical coverage estimations, <strong>use WISCA whenever possible</strong>, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki <em>et al.</em> (2020, <a href="https://doi.org/10.1001.jamanetworkopen.2019.21124" class="external-link">doi:10.1001.jamanetworkopen.2019.21124</a>
<p>For clinical coverage estimations, <strong>use WISCA whenever possible</strong>, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki <em>et al.</em> (2020, <a href="https://doi.org/10.1001/jamanetworkopen.2019.21124" class="external-link">doi:10.1001/jamanetworkopen.2019.21124</a>
). See the section <em>Explaining WISCA</em> on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.</p><ol><li><p><strong>Traditional Antibiogram</strong></p>
<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to piperacillin/tazobactam (TZP)</p>
<p>Code example:</p>

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reference/antimicrobial_selectors.html
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@ -17,7 +17,7 @@ my_data_with_all_these_columns %&amp;gt;%
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@ -9,7 +9,7 @@ The antibiotics data set has been renamed to antimicrobials. The old name will b
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@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
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@ -414,10 +414,10 @@ 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: 4 × 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> 2025-06-01 <span style="color: #949494;">14:12:43</span> 1 MIC amoxicillin Escherich… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2025-06-01 <span style="color: #949494;">14:12:44</span> 1 MIC cipro Escherich… human 0.256 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2025-06-01 <span style="color: #949494;">14:12:44</span> 1 DISK tobra Escherich… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2025-06-01 <span style="color: #949494;">14:12:44</span> 1 DISK genta Escherich… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2025-06-02 <span style="color: #949494;">10:14:49</span> 1 MIC amoxicillin Escherich… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2025-06-02 <span style="color: #949494;">10:14:50</span> 1 MIC cipro Escherich… human 0.256 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2025-06-02 <span style="color: #949494;">10:14:50</span> 1 DISK tobra Escherich… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2025-06-02 <span style="color: #949494;">10:14:50</span> 1 DISK genta Escherich… 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>

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reference/atc_online.html
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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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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@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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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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@ -7,7 +7,7 @@
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@ -171,7 +171,7 @@
<div class="section level2">
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
<ol><li><p>McDonald, J.H. 2014. <strong>Handbook of Biological Statistics (3rd ed.)</strong>. Sparky House Publishing, Baltimore, Maryland. <a href="http://www.biostathandbook.com/gtestgof.html" class="external-link">http://www.biostathandbook.com/gtestgof.html</a>.</p></li>
<ol><li><p>McDonald, J.H. 2014. <strong>Handbook of Biological Statistics (3rd ed.)</strong>. Sparky House Publishing, Baltimore, Maryland.</p></li>
</ol></div>
<div class="section level2">
<h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>

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@ -7,7 +7,7 @@
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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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