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<img src="../logo.svg" class="logo" alt=""><h1>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/R/antibiogram.R" class="external-link"><code>R/antibiogram.R</code></a></small>
<div class="d-none name"><code>antibiogram.Rd</code></div>
</div>
<div class="ref-description section level2">
<p>Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods. Based on the approaches of Klinker <em>et al.</em>, Barbieri <em>et al.</em>, and the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki <em>et al.</em>, this function provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
</div>
<div class="section level2">
<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">antibiogram</span><span class="op">(</span></span>
<span> <span class="va">x</span>,</span>
<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>,</span>
<span> mo_transform <span class="op">=</span> <span class="st">"shortname"</span>,</span>
<span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> add_total_n <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> digits <span class="op">=</span> <span class="fl">0</span>,</span>
<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">10</span><span class="op">)</span>,</span>
<span> col_mo <span class="op">=</span> <span class="cn">NULL</span>,</span>
<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>
<span> minimum <span class="op">=</span> <span class="fl">30</span>,</span>
<span> combine_SI <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> sep <span class="op">=</span> <span class="st">" + "</span>,</span>
<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>
<span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for class 'antibiogram'</span></span>
<span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for class 'antibiogram'</span></span>
<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">object</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for class 'antibiogram'</span></span>
<span><span class="fu">knit_print</span><span class="op">(</span></span>
<span> <span class="va">x</span>,</span>
<span> italicise <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> na <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">"knitr.kable.NA"</span>, default <span class="op">=</span> <span class="st">""</span><span class="op">)</span>,</span>
<span> <span class="va">...</span></span>
<span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<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>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>
</ul></div>
<div class="section level2">
<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see <code><a href="as.sir.html">as.sir()</a></code>)</p></dd>
<dt id="arg-antibiotics">antibiotics<a class="anchor" aria-label="anchor" href="#arg-antibiotics"></a></dt>
<dd><p>vector of any antibiotic name or code (will be evaluated with <code><a href="as.ab.html">as.ab()</a></code>, column name of <code>x</code>, or (any combinations of) <a href="antibiotic_class_selectors.html">antibiotic selectors</a> such as <code><a href="antibiotic_class_selectors.html">aminoglycosides()</a></code> or <code><a href="antibiotic_class_selectors.html">carbapenems()</a></code>. For combination antibiograms, this can also be set to values separated with <code>"+"</code>, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antibiotics exist in <code>x</code>. See <em>Examples</em>.</p></dd>
<dt id="arg-mo-transform">mo_transform<a class="anchor" aria-label="anchor" href="#arg-mo-transform"></a></dt>
<dd><p>a character to transform microorganism input - must be <code>"name"</code>, <code>"shortname"</code> (default), <code>"gramstain"</code>, or one of the column names of the <a href="microorganisms.html">microorganisms</a> data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be <code>NULL</code> to not transform the input.</p></dd>
<dt id="arg-ab-transform">ab_transform<a class="anchor" aria-label="anchor" href="#arg-ab-transform"></a></dt>
<dd><p>a character to transform antibiotic input - must be one of the column names of the <a href="antibiotics.html">antibiotics</a> data set (defaults to <code>"name"</code>): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be <code>NULL</code> to not transform the input.</p></dd>
<dt id="arg-syndromic-group">syndromic_group<a class="anchor" aria-label="anchor" href="#arg-syndromic-group"></a></dt>
<dd><p>a column name of <code>x</code>, or values calculated to split rows of <code>x</code>, e.g. by using <code><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse()</a></code> or <code><a href="https://dplyr.tidyverse.org/reference/case_when.html" class="external-link">case_when()</a></code>. See <em>Examples</em>.</p></dd>
<dt id="arg-add-total-n">add_total_n<a class="anchor" aria-label="anchor" href="#arg-add-total-n"></a></dt>
<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 isolate per antibiotic (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>
<dt id="arg-only-all-tested">only_all_tested<a class="anchor" aria-label="anchor" href="#arg-only-all-tested"></a></dt>
<dd><p>(for combination antibiograms): a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate that isolates must be tested for all antibiotics, see <em>Details</em></p></dd>
<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
<dd><p>number of digits to use for rounding the susceptibility percentage</p></dd>
<dt id="arg-formatting-type">formatting_type<a class="anchor" aria-label="anchor" href="#arg-formatting-type"></a></dt>
<dd><p>numeric value (1–12) indicating how the 'cells' of the antibiogram table should be formatted. See <em>Details</em> &gt; <em>Formatting Type</em> for a list of options.</p></dd>
<dt id="arg-col-mo">col_mo<a class="anchor" aria-label="anchor" href="#arg-col-mo"></a></dt>
<dd><p>column name of the names or codes of the microorganisms (see <code><a href="as.mo.html">as.mo()</a></code>) - the default is the first column of class <code><a href="as.mo.html">mo</a></code>. Values will be coerced using <code><a href="as.mo.html">as.mo()</a></code>.</p></dd>
<dt id="arg-language">language<a class="anchor" aria-label="anchor" href="#arg-language"></a></dt>
<dd><p>language to translate text, which defaults to the system language (see <code><a href="translate.html">get_AMR_locale()</a></code>)</p></dd>
<dt id="arg-minimum">minimum<a class="anchor" aria-label="anchor" href="#arg-minimum"></a></dt>
<dd><p>the minimum allowed number of available (tested) isolates. Any isolate count lower than <code>minimum</code> will return <code>NA</code> with a warning. The default number of <code>30</code> isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see <em>Source</em>.</p></dd>
<dt id="arg-combine-si">combine_SI<a class="anchor" aria-label="anchor" href="#arg-combine-si"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is <code>TRUE</code>)</p></dd>
<dt id="arg-sep">sep<a class="anchor" aria-label="anchor" href="#arg-sep"></a></dt>
<dd><p>a separating character for antibiotic columns in combination antibiograms</p></dd>
<dt id="arg-info">info<a class="anchor" aria-label="anchor" href="#arg-info"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate info should be printed - the default is <code>TRUE</code> only in interactive mode</p></dd>
<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
<dd><p>when used in <a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">R Markdown or Quarto</a>: arguments passed on to <code><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">knitr::kable()</a></code> (otherwise, has no use)</p></dd>
<dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
<dd><p>an <code>antibiogram()</code> object</p></dd>
<dt id="arg-italicise">italicise<a class="anchor" aria-label="anchor" href="#arg-italicise"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether the microorganism names in the <a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">knitr</a> table should be made italic, using <code><a href="italicise_taxonomy.html">italicise_taxonomy()</a></code>.</p></dd>
<dt id="arg-na">na<a class="anchor" aria-label="anchor" href="#arg-na"></a></dt>
<dd><p>character to use for showing <code>NA</code> values</p></dd>
</dl></div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
<p>This function returns a table with values between 0 and 100 for <em>susceptibility</em>, not resistance.</p>
<p><strong>Remember that you should filter your data to let it contain only first isolates!</strong> This is needed to exclude duplicates and to reduce selection bias. Use <code><a href="first_isolate.html">first_isolate()</a></code> to determine them in your data set with one of the four available algorithms.</p><div class="section">
<h3 id="formatting-type">Formatting Type<a class="anchor" aria-label="anchor" href="#formatting-type"></a></h3>
<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 susceptibility percentage, <code>15</code> the numerator, and <code>300</code> the denominator:</p><ol><li><p>5</p></li>
<li><p>15</p></li>
<li><p>300</p></li>
<li><p>15/300</p></li>
<li><p>5 (300)</p></li>
<li><p>5% (300)</p></li>
<li><p>5 (N=300)</p></li>
<li><p>5% (N=300)</p></li>
<li><p>5 (15/300)</p></li>
<li><p>5% (15/300)</p></li>
<li><p>5 (N=15/300)</p></li>
<li><p>5% (N=15/300)</p></li>
</ol><p>The default is <code>10</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>.</p>
<p>Set <code>digits</code> (defaults to <code>0</code>) to alter the rounding of the susceptibility percentage.</p>
</div>
<div class="section">
<h3 id="antibiogram-types">Antibiogram Types<a class="anchor" aria-label="anchor" href="#antibiogram-types"></a></h3>
<p>There are four 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>. Use WISCA whenever possible, since it provides precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility. See the section <em>Why Use WISCA?</em> on this page.</p>
<p>The four antibiogram types:</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>
<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="st">"TZP"</span><span class="op">)</span></span></code></pre><p></p></div></li>
<li><p><strong>Combination Antibiogram</strong></p>
<p>Case example: Additional susceptibility of <em>Pseudomonas aeruginosa</em> to TZP + tobramycin versus TZP alone</p>
<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="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><p></p></div></li>
<li><p><strong>Syndromic Antibiogram</strong></p>
<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to TZP among respiratory specimens (obtained among ICU patients only)</p>
<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="fu"><a href="../reference/antibiotic_class_selectors.html">penicillins</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span></code></pre><p></p></div></li>
<li><p><strong>Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</strong></p>
<p>WISCA enhances empirical antibiotic selection by weighting the incidence of pathogens in specific clinical syndromes and combining them with their susceptibility data. It provides an estimation of regimen coverage by aggregating pathogen incidences and susceptibilities across potential causative organisms. See also the section <em>Why Use WISCA?</em> on this page.</p>
<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to TZP among respiratory specimens (obtained among ICU patients only) for male patients age &gt;=65 years with heart failure</p>
<p>Code example:</p>
<p></p><div class="sourceCode r"><pre><code><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
<span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">ward</span> <span class="op">==</span> <span class="st">"ICU"</span> <span class="op">&amp;</span> <span class="va">specimen_type</span> <span class="op">==</span> <span class="st">"Respiratory"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</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> 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">.</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span>
<span> <span class="va">.</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"Male"</span> <span class="op">&amp;</span></span>
<span> <span class="va">.</span><span class="op">$</span><span class="va">condition</span> <span class="op">==</span> <span class="st">"Heart Disease"</span>,</span>
<span> <span class="st">"Study Group"</span>, <span class="st">"Control Group"</span><span class="op">)</span><span class="op">)</span></span></code></pre><p></p></div>
<p>WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).</p></li>
</ol></div>
<div class="section">
<h3 id="inclusion-in-combination-antibiogram-and-syndromic-antibiogram">Inclusion in Combination Antibiogram and Syndromic Antibiogram<a class="anchor" aria-label="anchor" href="#inclusion-in-combination-antibiogram-and-syndromic-antibiogram"></a></h3>
<p>Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the <code>only_all_tested</code> argument (default is <code>FALSE</code>). See this example for two antibiotics, Drug A and Drug B, about how <code>antibiogram()</code> works to calculate the %SI:</p>
<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="sc">--------------------------------------------------------------------</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a> only_all_tested <span class="ot">=</span> <span class="cn">FALSE</span> only_all_tested <span class="ot">=</span> <span class="cn">TRUE</span></span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a> <span class="sc">-----------------------</span> <span class="sc">-----------------------</span></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> Drug A Drug B include as include as include as include as</span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> numerator denominator numerator denominator</span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="sc">--------</span> <span class="sc">--------</span> <span class="sc">----------</span> <span class="sc">-----------</span> <span class="sc">----------</span> <span class="sc">-----------</span></span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a> S or I S or I X X X X</span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a> R S or I X X X X</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a> <span class="sc">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> S or I X X <span class="sc">-</span> <span class="sc">-</span></span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a> S or I R X X X X</span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a> R R <span class="sc">-</span> X <span class="sc">-</span> X</span>
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a> <span class="sc">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> R <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span></span>
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a> S or I <span class="sc">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> X X <span class="sc">-</span> <span class="sc">-</span></span>
<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a> R <span class="sc">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span></span>
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a> <span class="er">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> <span class="er">&lt;</span><span class="cn">NA</span><span class="sc">&gt;</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span></span>
<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a><span class="sc">--------------------------------------------------------------------</span></span></code></pre><p></p></div>
</div>
<div class="section">
<h3 id="plotting">Plotting<a class="anchor" aria-label="anchor" href="#plotting"></a></h3>
<p>All types of antibiograms as listed above can be plotted (using <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">ggplot2::autoplot()</a></code> or base <span style="R">R</span>'s <code><a href="plot.html">plot()</a></code> and <code><a href="https://rdrr.io/r/graphics/barplot.html" class="external-link">barplot()</a></code>).</p>
<p>THe outcome of <code>antibiogram()</code> can also be used directly in R Markdown / Quarto (i.e., <code>knitr</code>) for reports. In this case, <code><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">knitr::kable()</a></code> will be applied automatically and microorganism names will even be printed in italics at default (see argument <code>italicise</code>).</p>
<p>You can also use functions from specific 'table reporting' packages to transform the output of <code>antibiogram()</code> to your needs, e.g. with <code>flextable::as_flextable()</code> or <code>gt::gt()</code>.</p>
</div>
</div>
<div class="section level2">
<h2 id="why-use-wisca-">Why Use WISCA?<a class="anchor" aria-label="anchor" href="#why-use-wisca-"></a></h2>
<p>WISCA is a powerful tool for guiding empirical antibiotic therapy because it provides precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility. This is particularly important in empirical treatment, where the causative pathogen is often unknown at the outset. Traditional antibiograms do not reflect the weighted likelihood of specific pathogens based on clinical syndromes, which can lead to suboptimal treatment choices.</p>
<p>The Bayesian WISCA, as described by Bielicki <em>et al.</em> (2016), improves on earlier methods by handling uncertainties common in smaller datasets, such as low-incidence infections. This method offers a significant advantage by:</p><ol><li><p>Pooling Data from Multiple Sources:<br> WISCA uses pooled data from multiple hospitals or surveillance sources to overcome limitations of small sample sizes at individual institutions, allowing for more confident selection of narrow-spectrum antibiotics or combinations.</p></li>
<li><p>Bayesian Framework:<br> The Bayesian decision tree model accounts for both local data and prior knowledge (such as inherent resistance patterns) to estimate regimen coverage. It allows for a more precise estimation of coverage, even in cases where susceptibility data is missing or incomplete.</p></li>
<li><p>Incorporating Pathogen and Regimen Uncertainty:<br> WISCA allows clinicians to see the likelihood that an empirical regimen will be effective against all relevant pathogens, taking into account uncertainties related to both pathogen prevalence and antimicrobial resistance. This leads to better-informed, data-driven clinical decisions.</p></li>
<li><p>Scenarios for Optimising Treatment:<br> For hospitals or settings with low-incidence infections, WISCA helps determine whether local data is sufficient or if pooling with external data is necessary. It also identifies statistically significant differences or similarities between antibiotic regimens, enabling clinicians to choose optimal therapies with greater confidence.</p></li>
</ol><p>WISCA is essential in optimising empirical treatment by shifting away from broad-spectrum antibiotics, which are often overused in empirical settings. By offering precise estimates based on syndromic patterns and pooled data, WISCA supports antimicrobial stewardship by guiding more targeted therapy, reducing unnecessary broad-spectrum use, and combating the rise of antimicrobial resistance.</p>
</div>
<div class="section level2">
<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># example_isolates is a data set available in the AMR package.</span></span></span>
<span class="r-in"><span><span class="co"># run ?example_isolates for more info.</span></span></span>
<span class="r-in"><span><span class="va">example_isolates</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2,000 × 46</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> date patient age gender ward mo PEN OXA FLC AMX </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&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;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1,990 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="co"># Traditional antibiogram ----------------------------------------------</span></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="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="antibiotic_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><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 10 × 7</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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 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> 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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</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="antibiotic_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-msg co"><span class="r-pr">#&gt;</span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 5</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> Gram-negative 96% (658/686) 96% (659/684) 0% (0/35) 98% (251/256)</span>
<span class="r-out co"><span class="r-pr">#&gt;</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">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</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="antibiotic_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-msg co"><span class="r-pr">#&gt;</span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 5 × 3</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Imipenem Meropenem </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> Coagulase-negative Staphylococcus (CoNS) 52% (25/48) 52% (25/48) </span>
<span class="r-out co"><span class="r-pr">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Combined antibiogram -------------------------------------------------</span></span></span>
<span class="r-in"><span></span></span>
<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">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 4</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> Gram-neg… 88% (565/641) 99% (681/691) 98% (679/693) </span>
<span class="r-out co"><span class="r-pr">#&gt;</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">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated names: ¹​`Piperacillin/tazobactam`,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Piperacillin/tazobactam + Gentamicin`,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ³​`Piperacillin/tazobactam + Tobramycin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># names of antibiotics do not need to resemble columns exactly:</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">"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">" &amp; "</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 3</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Ciprofloxacin `Ciprofloxacin &amp; Gentamicin`</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> Gram-negative 91% (621/684) 99% (684/694) </span>
<span class="r-out co"><span class="r-pr">#&gt;</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">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Syndromic antibiogram ------------------------------------------------</span></span></span>
<span class="r-in"><span></span></span>
<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="antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="antibiotic_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-msg co"><span class="r-pr">#&gt;</span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 14 × 8</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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 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> 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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</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">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: Tobramycin &lt;chr&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># now define a data set with only E. coli</span></span></span>
<span class="r-in"><span><span class="va">ex1</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Escherichia"</span><span class="op">)</span>, <span class="op">]</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Using column 'mo' as input for mo_genus()</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># with a custom language, though this will be determined automatically</span></span></span>
<span class="r-in"><span><span class="co"># (i.e., this table will be in Spanish on Spanish systems)</span></span></span>
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">ex1</span>,</span></span>
<span class="r-in"><span> antibiotics <span class="op">=</span> <span class="fu"><a href="antibiotic_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">"name"</span>,</span></span>
<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-msg co"><span class="r-pr">#&gt;</span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 5</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> No UCI E. coli 100% (119/119) 98% (316/323) 98% (318/325)</span>
<span class="r-out co"><span class="r-pr">#&gt;</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">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Weighted-incidence syndromic combination antibiogram (WISCA) ---------</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># the data set could contain a filter for e.g. respiratory specimens/ICU</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">"AMC"</span>, <span class="st">"AMC+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> minimum <span class="op">=</span> <span class="fl">10</span>, <span class="co"># this should be &gt;=30, but now just as example</span></span></span>
<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">example_isolates</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span></span>
<span class="r-in"><span> <span class="va">example_isolates</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"M"</span>,</span></span>
<span class="r-in"><span> <span class="st">"WISCA Group 1"</span>, <span class="st">"WISCA Group 2"</span></span></span>
<span class="r-in"><span> <span class="op">)</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 4 × 6</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` Pathogen Amoxicillin/clavulani…¹ Amoxicillin/clavulan…²</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</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> WISCA Group 1 Gram-negative 76% (216/285) 95% (270/284) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> WISCA Group 2 Gram-negative 76% (336/441) 98% (432/442) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> WISCA Group 1 Gram-positive 76% (310/406) 89% (347/392) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> WISCA Group 2 Gram-positive 76% (556/732) 89% (617/695) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated names: ¹​`Amoxicillin/clavulanic acid`,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Amoxicillin/clavulanic acid + Ciprofloxacin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 2 more variables: `Piperacillin/tazobactam` &lt;chr&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Print the output for R Markdown / Quarto -----------------------------</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">ureido</span> <span class="op">&lt;-</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="antibiotic_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><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)</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>
<span class="r-in"><span><span class="co"># but to be explicit here:</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">"knitr"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu">knitr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/knit_print.html" class="external-link">knit_print</a></span><span class="op">(</span><span class="va">ureido</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Pathogen |Piperacillin/tazobactam |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |:---------------|:-----------------------|</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |CoNS |30% (10/33) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |*E. coli* |94% (393/416) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |*K. pneumoniae* |89% (47/53) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |*S. pneumoniae* |100% (112/112) |</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">&lt;-</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><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">ab2</span> <span class="op">&lt;-</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></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>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-plt img"><img src="antibiogram-1.png" alt="" width="700" height="433"></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">ab2</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-plt img"><img src="antibiogram-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab1</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="antibiogram-3.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab2</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="antibiogram-4.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
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