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@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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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="">2.1.1.9146</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9147</small>
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@ -60,18 +60,19 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">x</span>, antibiotics <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>, mo_transform <span class="op">=</span> <span class="st">"shortname"</span>,</span>
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<span> ab_transform <span class="op">=</span> <span class="st">"name"</span>, syndromic_group <span class="op">=</span> <span class="cn">NULL</span>, add_total_n <span class="op">=</span> <span class="cn">FALSE</span>,</span>
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<span> only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">wisca</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>,</span>
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<span> formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>,</span>
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<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">wisca</span>, <span class="fl">14</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span>, col_mo <span class="op">=</span> <span class="cn">NULL</span>, language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
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<span> minimum <span class="op">=</span> <span class="fl">30</span>, combine_SI <span class="op">=</span> <span class="cn">TRUE</span>, sep <span class="op">=</span> <span class="st">" + "</span>, wisca <span class="op">=</span> <span class="cn">FALSE</span>,</span>
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<span> simulations <span class="op">=</span> <span class="fl">1000</span>, conf_interval <span class="op">=</span> <span class="fl">0.95</span>, interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
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<span> formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>, <span class="fl">14</span><span class="op">)</span>,</span>
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<span> col_mo <span class="op">=</span> <span class="cn">NULL</span>, language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>, minimum <span class="op">=</span> <span class="fl">30</span>,</span>
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<span> combine_SI <span class="op">=</span> <span class="cn">TRUE</span>, sep <span class="op">=</span> <span class="st">" + "</span>, wisca <span class="op">=</span> <span class="cn">FALSE</span>, simulations <span class="op">=</span> <span class="fl">1000</span>,</span>
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<span> conf_interval <span class="op">=</span> <span class="fl">0.95</span>, interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
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<span> info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
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<span></span>
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<span><span class="fu">wisca</span><span class="op">(</span><span class="va">x</span>, antibiotics <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>, mo_transform <span class="op">=</span> <span class="st">"shortname"</span>,</span>
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<span> ab_transform <span class="op">=</span> <span class="st">"name"</span>, syndromic_group <span class="op">=</span> <span class="cn">NULL</span>, add_total_n <span class="op">=</span> <span class="cn">FALSE</span>,</span>
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<span> only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>, digits <span class="op">=</span> <span class="fl">1</span>,</span>
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<span><span class="fu">wisca</span><span class="op">(</span><span class="va">x</span>, antibiotics <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>, ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
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<span> syndromic_group <span class="op">=</span> <span class="cn">NULL</span>, add_total_n <span class="op">=</span> <span class="cn">FALSE</span>, only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>,</span>
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<span> digits <span class="op">=</span> <span class="fl">1</span>,</span>
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<span> formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>, <span class="fl">14</span><span class="op">)</span>,</span>
|
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<span> col_mo <span class="op">=</span> <span class="cn">NULL</span>, language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>, minimum <span class="op">=</span> <span class="fl">30</span>,</span>
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<span> combine_SI <span class="op">=</span> <span class="cn">TRUE</span>, sep <span class="op">=</span> <span class="st">" + "</span>, simulations <span class="op">=</span> <span class="fl">1000</span>,</span>
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<span> conf_interval <span class="op">=</span> <span class="fl">0.95</span>, interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
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<span> info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
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<span></span>
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<span><span class="fu">retrieve_wisca_parameters</span><span class="op">(</span><span class="va">wisca_model</span>, <span class="va">...</span><span class="op">)</span></span>
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@ -121,7 +122,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<dt id="arg-add-total-n">add_total_n<a class="anchor" aria-label="anchor" href="#arg-add-total-n"></a></dt>
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<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether total available numbers per pathogen should be added to the table (default is <code>TRUE</code>). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for <em>E. coli</em> 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").</p></dd>
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<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether <code>n_tested</code> available numbers per pathogen should be added to the table (default is <code>TRUE</code>). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for <em>E. coli</em> 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when <code>wisca = TRUE</code>; in that case, use <code>retrieve_wisca_parameters()</code> to get the parameters used for WISCA.</p></dd>
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<dt id="arg-only-all-tested">only_all_tested<a class="anchor" aria-label="anchor" href="#arg-only-all-tested"></a></dt>
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@ -157,7 +158,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<dt id="arg-wisca">wisca<a class="anchor" aria-label="anchor" href="#arg-wisca"></a></dt>
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<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is <code>FALSE</code>). This will use a Bayesian decision model to estimate regimen coverage probabilities using <a href="https://en.wikipedia.org/wiki/Monte_Carlo_method" class="external-link">Monte Carlo simulations</a>. Set <code>simulations</code> to adjust.</p></dd>
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<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is <code>FALSE</code>). This will use a Bayesian decision model to estimate regimen coverage probabilities using <a href="https://en.wikipedia.org/wiki/Monte_Carlo_method" class="external-link">Monte Carlo simulations</a>. Set <code>simulations</code>, <code>conf_interval</code>, and <code>interval_side</code> to adjust.</p></dd>
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<dt id="arg-simulations">simulations<a class="anchor" aria-label="anchor" href="#arg-simulations"></a></dt>
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@ -205,7 +206,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<h3 id="formatting-type">Formatting Type<a class="anchor" aria-label="anchor" href="#formatting-type"></a></h3>
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<p>The formatting of the 'cells' of the table can be set with the argument <code>formatting_type</code>. In these examples, <code>5</code> is the antimicrobial coverage (for WISCA: <code>4-6</code> indicates the confidence level), <code>15</code> the numerator, and <code>300</code> the denominator:</p><ol><li><p>5</p></li>
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<p>The formatting of the 'cells' of the table can be set with the argument <code>formatting_type</code>. In these examples, <code>5</code> is the antimicrobial coverage (<code>4-6</code> indicates the confidence level), <code>15</code> the number of susceptible isolates, and <code>300</code> the number of tested (i.e., available) isolates:</p><ol><li><p>5</p></li>
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<li><p>15</p></li>
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<li><p>300</p></li>
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<li><p>15/300</p></li>
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@ -214,12 +215,11 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<li><p>5 (N=300)</p></li>
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<li><p>5% (N=300)</p></li>
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<li><p>5 (15/300)</p></li>
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<li><p>5% (15/300) - <strong>default for non-WISCA</strong></p></li>
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<li><p>5% (15/300)</p></li>
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<li><p>5 (N=15/300)</p></li>
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<li><p>5% (N=15/300)</p>
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<p>Additional options for WISCA (using <code>antibiogram(..., wisca = TRUE)</code> or <code>wisca()</code>):</p></li>
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<li><p>5% (N=15/300)</p></li>
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<li><p>5 (4-6)</p></li>
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||||
<li><p>5% (4-6%) - <strong>default for WISCA</strong></p></li>
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||||
<li><p>5% (4-6%) - <strong>default</strong></p></li>
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||||
<li><p>5 (4-6,300)</p></li>
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<li><p>5% (4-6%,300)</p></li>
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<li><p>5 (4-6,N=300)</p></li>
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@ -228,7 +228,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<li><p>5% (4-6%,15/300)</p></li>
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<li><p>5 (4-6,N=15/300)</p></li>
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<li><p>5% (4-6%,N=15/300)</p></li>
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</ol><p>The default is <code>14</code> for WISCA and <code>10</code> for non-WISCA, which can be set globally with the package option <code><a href="AMR-options.html">AMR_antibiogram_formatting_type</a></code>, e.g. <code>options(AMR_antibiogram_formatting_type = 5)</code>. Do note that for WISCA, the numerator and denominator are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.</p>
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</ol><p>The default is <code>14</code>, which can be set globally with the package option <code><a href="AMR-options.html">AMR_antibiogram_formatting_type</a></code>, e.g. <code>options(AMR_antibiogram_formatting_type = 5)</code>. Do note that for WISCA, the total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.</p>
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<p>Set <code>digits</code> (defaults to <code>0</code>) to alter the rounding of the susceptibility percentages.</p>
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</div>
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@ -238,8 +238,8 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<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>
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), and they are all supported by <code>antibiogram()</code>.</p>
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<p><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>
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). See the section <em>Explaining WISCA</em> on this page.</p><ol><li><p><strong>Traditional Antibiogram</strong></p>
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<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>
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). 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>
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<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to piperacillin/tazobactam (TZP)</p>
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<p>Code example:</p>
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<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
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@ -285,19 +285,21 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<p>In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:</p><ol><li><p><strong>Initial Empirical Therapy (Admission / Pre-Culture Data)</strong></p>
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<p>At admission, no pathogen information is available.</p><ul><li><p>Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.</p></li>
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<p>At admission, no pathogen information is available.</p><ul><li><p>Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.</p></li>
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<li><p>Code example:</p>
|
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<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
|
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<span> antibiotics <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
|
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<span> wisca <span class="op">=</span> <span class="cn">TRUE</span>,</span>
|
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<span> mo_transform <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="co"># all pathogens set to `NA`</span></span></code></pre><p></p></div></li>
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<span> mo_transform <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="co"># all pathogens set to `NA`</span></span>
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<span></span>
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<span><span class="co"># preferred: use WISCA</span></span>
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<span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
|
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<span> antibiotics <span class="op">=</span> <span class="va">selected_regimens</span><span class="op">)</span></span></code></pre><p></p></div></li>
|
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</ul></li>
|
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<li><p><strong>Refinement with Gram Stain Results</strong></p>
|
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<p>When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).</p><ul><li><p>Action: narrow coverage based on Gram stain-specific resistance patterns.</p></li>
|
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<li><p>Code example:</p>
|
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<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
|
||||
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span>,</span>
|
||||
<span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span> <span class="co"># all pathogens set to Gram-pos/Gram-neg</span></span></code></pre><p></p></div></li>
|
||||
</ul></li>
|
||||
<li><p><strong>Definitive Therapy Based on Species Identification</strong></p>
|
||||
@ -305,7 +307,6 @@ Adhering to previously described approaches (see Source) and especially the Baye
|
||||
<li><p>Code example:</p>
|
||||
<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
|
||||
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span>,</span>
|
||||
<span> mo_transform <span class="op">=</span> <span class="st">"shortname"</span><span class="op">)</span> <span class="co"># all pathogens set to 'G. species', e.g., E. coli</span></span></code></pre><p></p></div></li>
|
||||
</ul></li>
|
||||
</ol><p>By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.</p>
|
||||
@ -350,7 +351,7 @@ 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 hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.</p>
|
||||
), 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>
|
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@ -368,6 +369,7 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
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<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>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
|
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@ -409,51 +411,49 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;"> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">carbapenems()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 10 × 7</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 10 × 7</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> CoNS 0% (0/43) 86% (267/… 52% (25… 0% (0/43) 52% (25/… 22% (12/5…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> E. coli 100% (171/… 98% (451/… 100% (4… <span style="color: #BB0000;">NA</span> 100% (41… 97% (450/…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> E. faecalis 0% (0/39) 0% (0/39) 100% (3… 0% (0/39) <span style="color: #BB0000;">NA</span> 0% (0/39) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 4</span> K. pneumoniae <span style="color: #BB0000;">NA</span> 90% (52/5… 100% (5… <span style="color: #BB0000;">NA</span> 100% (53… 90% (52/5…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> P. aeruginosa <span style="color: #BB0000;">NA</span> 100% (30/… <span style="color: #BB0000;">NA</span> 0% (0/30) <span style="color: #BB0000;">NA</span> 100% (30/…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> P. mirabilis <span style="color: #BB0000;">NA</span> 94% (32/3… 94% (30… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 94% (32/3…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> S. aureus <span style="color: #BB0000;">NA</span> 99% (231/… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 98% (84/8…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 8</span> S. epidermidis 0% (0/44) 79% (128/… <span style="color: #BB0000;">NA</span> 0% (0/44) <span style="color: #BB0000;">NA</span> 51% (45/8…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 9</span> S. hominis <span style="color: #BB0000;">NA</span> 92% (74/8… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 85% (53/6…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">10</span> S. pneumoniae 0% (0/117) 0% (0/117) <span style="color: #BB0000;">NA</span> 0% (0/11… <span style="color: #BB0000;">NA</span> 0% (0/117)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> CoNS 0% (0-8%) 86% (82-9… 52% (37… 0% (0-8%) 52% (37-… 22% (12-3…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> E. coli 100% (98-1… 98% (96-9… 100% (9… <span style="color: #BB0000;">NA</span> 100% (99… 97% (96-9…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> E. faecalis 0% (0-9%) 0% (0-9%) 100% (9… 0% (0-9%) <span style="color: #BB0000;">NA</span> 0% (0-9%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 4</span> K. pneumoniae <span style="color: #BB0000;">NA</span> 90% (79-9… 100% (9… <span style="color: #BB0000;">NA</span> 100% (93… 90% (79-9…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> P. aeruginosa <span style="color: #BB0000;">NA</span> 100% (88-… <span style="color: #BB0000;">NA</span> 0% (0-12… <span style="color: #BB0000;">NA</span> 100% (88-…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> P. mirabilis <span style="color: #BB0000;">NA</span> 94% (80-9… 94% (79… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 94% (80-9…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> S. aureus <span style="color: #BB0000;">NA</span> 99% (97-1… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 98% (92-1…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 8</span> S. epidermidis 0% (0-8%) 79% (71-8… <span style="color: #BB0000;">NA</span> 0% (0-8%) <span style="color: #BB0000;">NA</span> 51% (40-6…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 9</span> S. hominis <span style="color: #BB0000;">NA</span> 92% (84-9… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 85% (74-9…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">10</span> S. pneumoniae 0% (0-3%) 0% (0-3%) <span style="color: #BB0000;">NA</span> 0% (0-3%) <span style="color: #BB0000;">NA</span> 0% (0-3%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"atc"</span>,</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;"> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 2 × 5</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-negative 96% (658/686) 96% (659/684) 0% (0/35) 98% (251/256)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-positive 34% (228/665) 63% (740/1170) 0% (0/436) 0% (0/436) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 2 × 5</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-negative 96% (94-97%) 96% (95-98%) 0% (0-10%) 98% (96-99%)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-positive 34% (31-38%) 63% (60-66%) 0% (0-1%) 0% (0-1%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="antimicrobial_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"name"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"name"</span><span class="op">)</span></span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">carbapenems()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 5 × 3</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 5 × 3</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen Imipenem Meropenem </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Coagulase-negative Staphylococcus (CoNS) 52% (25/48) 52% (25/48) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Enterococcus faecalis 100% (38/38) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">3</span> Escherichia coli 100% (422/422) 100% (418/418)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 100% (51/51) 100% (53/53) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">5</span> Proteus mirabilis 94% (30/32) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Coagulase-negative Staphylococcus (CoNS) 52% (37-67%) 52% (37-67%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Enterococcus faecalis 100% (91-100%) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">3</span> Escherichia coli 100% (99-100%) 100% (99-100%)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 100% (93-100%) 100% (93-100%)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">5</span> Proteus mirabilis 94% (79-99%) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
@ -463,13 +463,12 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span><span class="co"># combined antibiotics yield higher empiric coverage</span></span></span>
|
||||
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <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></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 2 × 4</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 2 × 4</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-neg… 88% (565/641) 99% (681/691) 98% (679/693) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-pos… 86% (296/345) 98% (1018/1044) 95% (524/550) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-neg… 88% (85-91%) 99% (97-99%) 98% (97-99%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-pos… 86% (82-89%) 98% (96-98%) 95% (93-97%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ abbreviated names: ¹`Piperacillin/tazobactam`,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ²`Piperacillin/tazobactam + Gentamicin`,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ³`Piperacillin/tazobactam + Tobramycin`</span></span>
|
||||
@ -481,13 +480,12 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span> antibiotics <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">"Cipro"</span>, <span class="st">"cipro + genta"</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
|
||||
<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
|
||||
<span class="r-in"><span> sep <span class="op">=</span> <span class="st">" & "</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 2 × 3</span></span>
|
||||
<span class="r-in"><span> sep <span class="op">=</span> <span class="st">" & "</span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 2 × 3</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin`</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-negative 91% (621/684) 99% (684/694) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-positive 77% (560/724) 93% (784/847) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-negative 91% (88-93%) 99% (97-99%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-positive 77% (74-80%) 93% (91-94%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
@ -497,28 +495,27 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span><span class="co"># the data set could contain a filter for e.g. respiratory specimens</span></span></span>
|
||||
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <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="antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="antimicrobial_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;"> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">carbapenems()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 14 × 8</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 14 × 8</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> Clinical CoNS <span style="color: #BB0000;">NA</span> 89% (183/… 57% (20… <span style="color: #BB0000;">NA</span> 57% (20/…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> ICU CoNS <span style="color: #BB0000;">NA</span> 79% (58/7… <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">#></span> <span style="color: #BCBCBC;"> 3</span> Outpatient CoNS <span style="color: #BB0000;">NA</span> 84% (26/3… <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">#></span> <span style="color: #BCBCBC;"> 4</span> Clinical E. coli 100% (1… 98% (291/… 100% (2… <span style="color: #BB0000;">NA</span> 100% (27…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> ICU E. coli 100% (5… 99% (135/… 100% (1… <span style="color: #BB0000;">NA</span> 100% (11…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> Clinical K. pneumo… <span style="color: #BB0000;">NA</span> 92% (47/5… 100% (4… <span style="color: #BB0000;">NA</span> 100% (46…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> Clinical P. mirabi… <span style="color: #BB0000;">NA</span> 100% (30/… <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">#></span> <span style="color: #BCBCBC;"> 8</span> Clinical S. aureus <span style="color: #BB0000;">NA</span> 99% (148/… <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">#></span> <span style="color: #BCBCBC;"> 9</span> ICU S. aureus <span style="color: #BB0000;">NA</span> 100% (66/… <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">#></span> <span style="color: #BCBCBC;">10</span> Clinical S. epider… <span style="color: #BB0000;">NA</span> 82% (65/7… <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">#></span> <span style="color: #BCBCBC;">11</span> ICU S. epider… <span style="color: #BB0000;">NA</span> 72% (54/7… <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">#></span> <span style="color: #BCBCBC;">12</span> Clinical S. hominis <span style="color: #BB0000;">NA</span> 96% (43/4… <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">#></span> <span style="color: #BCBCBC;">13</span> Clinical S. pneumo… 0% (0/7… 0% (0/78) <span style="color: #BB0000;">NA</span> 0% (0/78) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">14</span> ICU S. pneumo… 0% (0/3… 0% (0/30) <span style="color: #BB0000;">NA</span> 0% (0/30) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> Clinical CoNS <span style="color: #BB0000;">NA</span> 89% (84-9… 57% (39… <span style="color: #BB0000;">NA</span> 57% (39-…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> ICU CoNS <span style="color: #BB0000;">NA</span> 79% (68-8… <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">#></span> <span style="color: #BCBCBC;"> 3</span> Outpatient CoNS <span style="color: #BB0000;">NA</span> 84% (66-9… <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">#></span> <span style="color: #BCBCBC;"> 4</span> Clinical E. coli 100% (9… 98% (96-9… 100% (9… <span style="color: #BB0000;">NA</span> 100% (99…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> ICU E. coli 100% (9… 99% (95-1… 100% (9… <span style="color: #BB0000;">NA</span> 100% (97…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> Clinical K. pneumo… <span style="color: #BB0000;">NA</span> 92% (81-9… 100% (9… <span style="color: #BB0000;">NA</span> 100% (92…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> Clinical P. mirabi… <span style="color: #BB0000;">NA</span> 100% (88-… <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">#></span> <span style="color: #BCBCBC;"> 8</span> Clinical S. aureus <span style="color: #BB0000;">NA</span> 99% (95-1… <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">#></span> <span style="color: #BCBCBC;"> 9</span> ICU S. aureus <span style="color: #BB0000;">NA</span> 100% (95-… <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">#></span> <span style="color: #BCBCBC;">10</span> Clinical S. epider… <span style="color: #BB0000;">NA</span> 82% (72-9… <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">#></span> <span style="color: #BCBCBC;">11</span> ICU S. epider… <span style="color: #BB0000;">NA</span> 72% (60-8… <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">#></span> <span style="color: #BCBCBC;">12</span> Clinical S. hominis <span style="color: #BB0000;">NA</span> 96% (85-9… <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">#></span> <span style="color: #BCBCBC;">13</span> Clinical S. pneumo… 0% (0-5… 0% (0-5%) <span style="color: #BB0000;">NA</span> 0% (0-5%) <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">14</span> ICU S. pneumo… 0% (0-1… 0% (0-12%) <span style="color: #BB0000;">NA</span> 0% (0-12… <span style="color: #BB0000;">NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 1 more variable: Tobramycin <chr></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
@ -535,35 +532,33 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">ex1</span><span class="op">$</span><span class="va">ward</span> <span class="op">==</span> <span class="st">"ICU"</span>,</span></span>
|
||||
<span class="r-in"><span> <span class="st">"UCI"</span>, <span class="st">"No UCI"</span></span></span>
|
||||
<span class="r-in"><span> <span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> language <span class="op">=</span> <span class="st">"es"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span></span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;"> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 2 × 5</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> No UCI E. coli 100% (119/119) 98% (316/323) 98% (318/325)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> UCI E. coli 100% (52/52) 99% (135/137) 96% (132/137)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA / 95% CI): 2 × 5</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> No UCI E. coli 100% (97-100%) 98% (96-99%) 98% (96-99%)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> UCI E. coli 100% (93-100%) 99% (95-100%) 96% (92-99%)</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="co"># WISCA antibiogram ----------------------------------------------------</span></span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="co"># can be used for any of the above types - just add `wisca = TRUE`</span></span></span>
|
||||
<span class="r-in"><span><span class="co"># WISCA are not stratified by species, but rather on syndromes</span></span></span>
|
||||
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <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></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
|
||||
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (WISCA / 95% CI): 2 × 4</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Gram-neg… 88% (85.2-90.5%) 98.4% (97.3-99.2%) 97.9% (96.6-98.7%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-pos… 85.6% (81.9-89.1%) 97.4% (96.4-98.3%) 95.1% (93.2-96.7%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ abbreviated names: ¹`Piperacillin/tazobactam`,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ²`Piperacillin/tazobactam + Gentamicin`,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ³`Piperacillin/tazobactam + Tobramycin`</span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span>,</span></span>
|
||||
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># An Antibiogram (WISCA / 95% CI): 3 × 4</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> Clinical 78% (70-85.4%) 98.3% (94.9-99.7%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> ICU 90.6% (84.4-94.9%) 97.6% (92.7-99.7%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">3</span> Outpatient 92.2% (79.7-99%) 94.7% (82.7-99.5%) </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ abbreviated name: ¹`Piperacillin/tazobactam + Gentamicin`</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
@ -572,9 +567,8 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="va">ureido</span> <span class="op"><-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="antimicrobial_class_selectors.html">ureidopenicillins</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
|
||||
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"name"</span>,</span></span>
|
||||
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
|
||||
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">ureidopenicillins()</span><span style="color: #0000BB;"> using column '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">' (piperacillin/tazobactam)</span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="co"># in an Rmd file, you would just need to return `ureido` in a chunk,</span></span></span>
|
||||
@ -584,36 +578,20 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
|
||||
<span class="r-in"><span><span class="op">}</span></span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |Pathogen |Piperacillin/tazobactam |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |Syndromic Group |Piperacillin/tazobactam |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |:---------------|:-----------------------|</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*B. fragilis* |4.4% (0.1-15.3%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |CoNS |31.4% (17.4-47.1%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*E. cloacae* |73.1% (52.1-88.2%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*E. coli* |94.3% (91.9-96.3%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*E. faecalis* |94.8% (81.5-99.9%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*E. faecium* |9.7% (1.4-25.6%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |GBS |94.9% (82.5-99.9%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*K. pneumoniae* |87% (77.8-94.3%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*P. aeruginosa* |96.4% (86.8-99.9%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*P. mirabilis* |96.6% (86.9-100%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*S. anginosus* |94.1% (79.6-99.8%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*S. marcescens* |49.9% (30.8-67.8%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*S. pneumoniae* |99.1% (96.9-100%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |*S. pyogenes* |94.2% (81.1-99.8%) |</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> |name |73.6% (66-81%) |</span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="co"># Generate plots with ggplot2 or base R --------------------------------</span></span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="va">ab1</span> <span class="op"><-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
|
||||
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span><span class="va">ab2</span> <span class="op"><-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
|
||||
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
|
||||
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
|
||||
<span class="r-in"><span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span></span>
|
||||
<span class="r-in"><span></span></span>
|
||||
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"ggplot2"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
|
||||
<span class="r-in"><span> <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">ab1</span><span class="op">)</span></span></span>
|
||||
|
||||
Reference in New Issue
Block a user