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<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.9/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.9/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram"><meta name="description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
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Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
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Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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<ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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<li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
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<li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
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<img src="../logo.svg" class="logo" alt=""><h1>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms</h1>
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<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>
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<div class="d-none name"><code>antibiogram.Rd</code></div>
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</div>
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<div class="ref-description section level2">
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<p>Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.</p>
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<p>Adhering to previously described approaches (see <em>Source</em>) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki <em>et al.</em>, these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
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</div>
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<div class="section level2">
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<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
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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="fl">0</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">18</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> 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">0</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">18</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> 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="co"># S3 method for class 'antibiogram'</span></span>
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<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>
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<span></span>
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<span><span class="co"># S3 method for class 'antibiogram'</span></span>
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<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>
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<span></span>
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<span><span class="co"># S3 method for class 'antibiogram'</span></span>
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<span><span class="fu">knit_print</span><span class="op">(</span><span class="va">x</span>, italicise <span class="op">=</span> <span class="cn">TRUE</span>,</span>
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<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 class="va">...</span><span class="op">)</span></span></code></pre></div>
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</div>
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<div class="section level2">
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<h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
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<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>
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<li><p>Bielicki JA <em>et al.</em> (2020). <strong>Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries</strong> <em>JAMA Netw Open.</em> 3(2):e1921124; <a href="https://doi.org/10.1001.jamanetworkopen.2019.21124" class="external-link">doi:10.1001.jamanetworkopen.2019.21124</a></p></li>
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<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>
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<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 & 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>
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<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>
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</ul></div>
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<div class="section level2">
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<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
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<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
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<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 antimicrobial results (class 'sir', see <code><a href="as.sir.html">as.sir()</a></code>)</p></dd>
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<dt id="arg-antibiotics">antibiotics<a class="anchor" aria-label="anchor" href="#arg-antibiotics"></a></dt>
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<dd><p>vector of any antimicrobial 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="antimicrobial_class_selectors.html">antimicrobial selectors</a> such as <code><a href="antimicrobial_class_selectors.html">aminoglycosides()</a></code> or <code><a href="antimicrobial_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 antimicrobials exist in <code>x</code>. See <em>Examples</em>.</p></dd>
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<dt id="arg-mo-transform">mo_transform<a class="anchor" aria-label="anchor" href="#arg-mo-transform"></a></dt>
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<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>
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<dt id="arg-ab-transform">ab_transform<a class="anchor" aria-label="anchor" href="#arg-ab-transform"></a></dt>
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<dd><p>a character to transform antimicrobial 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>
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<dt id="arg-syndromic-group">syndromic_group<a class="anchor" aria-label="anchor" href="#arg-syndromic-group"></a></dt>
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<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>
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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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<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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<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 antimicrobials, see <em>Details</em></p></dd>
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<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
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<dd><p>number of digits to use for rounding the susceptibility percentage</p></dd>
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<dt id="arg-formatting-type">formatting_type<a class="anchor" aria-label="anchor" href="#arg-formatting-type"></a></dt>
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<dd><p>numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See <em>Details</em> > <em>Formatting Type</em> for a list of options.</p></dd>
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<dt id="arg-col-mo">col_mo<a class="anchor" aria-label="anchor" href="#arg-col-mo"></a></dt>
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<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>
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<dt id="arg-language">language<a class="anchor" aria-label="anchor" href="#arg-language"></a></dt>
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<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>
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<dt id="arg-minimum">minimum<a class="anchor" aria-label="anchor" href="#arg-minimum"></a></dt>
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<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>
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|
||
<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 antimicrobial columns in combination antibiograms</p></dd>
|
||
|
||
|
||
<dt id="arg-wisca">wisca<a class="anchor" aria-label="anchor" href="#arg-wisca"></a></dt>
|
||
<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 hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set <code>simulations</code> to adjust.</p></dd>
|
||
|
||
|
||
<dt id="arg-simulations">simulations<a class="anchor" aria-label="anchor" href="#arg-simulations"></a></dt>
|
||
<dd><p>(for WISCA) a numerical value to set the number of Montecarlo simulations</p></dd>
|
||
|
||
|
||
<dt id="arg-conf-interval">conf_interval<a class="anchor" aria-label="anchor" href="#arg-conf-interval"></a></dt>
|
||
<dd><p>(for WISCA) a numerical value to set confidence interval (default is <code>0.95</code>)</p></dd>
|
||
|
||
|
||
<dt id="arg-interval-side">interval_side<a class="anchor" aria-label="anchor" href="#arg-interval-side"></a></dt>
|
||
<dd><p>(for WISCA) the side of the confidence interval, either <code>"two-tailed"</code> (default), <code>"left"</code> or <code>"right"</code></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>
|
||
<p>For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top <em>n</em> species encountered in the data. You can filter on this top <em>n</em> using <code><a href="top_n_microorganisms.html">top_n_microorganisms()</a></code>. For example, use <code>top_n_microorganisms(your_data, n = 10)</code> as a pre-processing step to only include the top 10 species in the data.</p>
|
||
<p>The numeric values of an antibiogram are stored in a long format as the <a href="https://rdrr.io/r/base/attributes.html" class="external-link">attribute</a> <code>long_numeric</code>. You can retrieve them using <code>attributes(x)$long_numeric</code>, where <code>x</code> is the outcome of <code>antibiogram()</code> or <code>wisca()</code>. This is ideal for e.g. advanced plotting.</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 (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>
|
||
<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) - <strong>default for non-WISCA</strong></p></li>
|
||
<li><p>5 (N=15/300)</p></li>
|
||
<li><p>5% (N=15/300)</p>
|
||
<p>Additional options for WISCA (using <code>antibiogram(..., wisca = TRUE)</code> or <code>wisca()</code>):</p></li>
|
||
<li><p>5 (4-6)</p></li>
|
||
<li><p>5% (4-6%)</p></li>
|
||
<li><p>5 (4-6,300)</p></li>
|
||
<li><p>5% (4-6%,300)</p></li>
|
||
<li><p>5 (4-6,N=300)</p></li>
|
||
<li><p>5% (4-6%,N=300) - <strong>default for WISCA</strong></p></li>
|
||
<li><p>5 (4-6,15/300)</p></li>
|
||
<li><p>5% (4-6%,15/300)</p></li>
|
||
<li><p>5 (4-6,N=15/300)</p></li>
|
||
<li><p>5% (4-6%,N=15/300)</p></li>
|
||
</ol><p>The default is <code>18</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>.</p>
|
||
<p>Set <code>digits</code> (defaults to <code>0</code>) to alter the rounding of the susceptibility percentages.</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 various antibiogram types, as summarised by Klinker <em>et al.</em> (2021, <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a>
|
||
), and they are all supported by <code>antibiogram()</code>.</p>
|
||
<p><strong>Use WISCA whenever possible</strong>, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki <em>et al.</em> (2020, <a href="https://doi.org/10.1001.jamanetworkopen.2019.21124" class="external-link">doi:10.1001.jamanetworkopen.2019.21124</a>
|
||
). See the section <em>Why Use WISCA?</em> on this page.</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/antimicrobial_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 can be applied to any antibiogram, see the section <em>Why Use WISCA?</em> on this page for more information.</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>
|
||
<span> wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
|
||
<span></span>
|
||
<span><span class="co"># this is equal to:</span></span>
|
||
<span><span class="fu"><a href="../reference/antibiogram.html">wisca</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>
|
||
<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><p>Grouped <a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibbles</a> can also be used to calculate susceptibilities over various groups.</p>
|
||
<p>Code example:</p>
|
||
<p></p><div class="sourceCode r"><pre><code><span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">has_sepsis</span>, <span class="va">is_neonate</span>, <span class="va">sex</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||
<span> <span class="fu"><a href="../reference/antibiogram.html">wisca</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 class="op">)</span></span></code></pre><p></p></div>
|
||
</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 antimicrobials, 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"><</span><span class="cn">NA</span><span class="sc">></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"><</span><span class="cn">NA</span><span class="sc">></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"><</span><span class="cn">NA</span><span class="sc">></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"><</span><span class="cn">NA</span><span class="sc">></span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span></span>
|
||
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a> <span class="er"><</span><span class="cn">NA</span><span class="sc">></span> <span class="er"><</span><span class="cn">NA</span><span class="sc">></span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span> <span class="sc">-</span></span>
|
||
<span id="cb1-16"><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, as outlined by Barbieri <em>et al.</em> (<a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">doi:10.1186/s13756-021-00939-2</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>
|
||
<p>The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \(\theta\) for a given antimicrobial regimen is modelled using a Beta distribution as a prior:</p>
|
||
<p>$$\theta \sim \text{Beta}(\alpha_0, \beta_0)$$</p>
|
||
<p>where \(\alpha_0\) and \(\beta_0\) represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \(\alpha_0 = 1, \beta_0 = 1\)). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:</p>
|
||
<p>$$y \sim \text{Binomial}(n, \theta)$$</p>
|
||
<p>Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \(\theta\) is also a Beta distribution:</p>
|
||
<p>$$\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)$$</p>
|
||
<p>For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.</p>
|
||
<p>Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using <code>dplyr</code>'s <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> as a pre-processing step before running <code>wisca()</code>. In this case, 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>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
|
||
<p>Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends</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">#></span> <span style="color: #949494;"># A tibble: 2,000 × 46</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> date patient age gender ward mo PEN OXA FLC AMX </span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><date></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></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;"><mo></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span></span>
|
||
<span class="r-out co"><span class="r-pr">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></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">#></span> <span style="color: #949494;"># ℹ 1,990 more rows</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,</span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …</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="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><span class="op">)</span></span></span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</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> 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> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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="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-msg co"><span class="r-pr">#></span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</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> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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="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-msg co"><span class="r-pr">#></span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</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> 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> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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">#></span> <span style="color: #949494;"># An Antibiogram (non-WISCA): 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: #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-out co"><span class="r-pr">#></span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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">" & "</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-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> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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="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-msg co"><span class="r-pr">#></span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</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> `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: #949494;"># ℹ 1 more variable: Tobramycin <chr></span></span>
|
||
<span class="r-out co"><span class="r-pr">#></span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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"><-</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">#></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="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">"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">#></span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
|
||
<span class="r-msg co"><span class="r-pr">#></span> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</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> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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"># 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="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% (85-90%,N=641) 98% (97-99%,N=691) 98% (97-99%,N=693) </span>
|
||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> Gram-pos… 86% (82-89%,N=345) 97% (96-98%,N=1044) 95% (93-97%,N=550) </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-out co"><span class="r-pr">#></span> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,</span>
|
||
<span class="r-out co"><span class="r-pr">#></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"><-</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-msg co"><span class="r-pr">#></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">#></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> |:---------------|:-----------------------|</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*B. fragilis* |5% (0-17%,N=20) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |CoNS |32% (17-47%,N=33) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*E. cloacae* |73% (51-88%,N=20) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*E. coli* |94% (92-96%,N=416) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*E. faecalis* |95% (82-100%,N=18) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*E. faecium* |10% (1-26%,N=18) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |GBS |95% (84-100%,N=18) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*K. pneumoniae* |87% (78-95%,N=53) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*P. aeruginosa* |97% (88-100%,N=27) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*P. mirabilis* |97% (88-100%,N=27) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*S. anginosus* |94% (80-100%,N=16) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*S. marcescens* |50% (32-69%,N=22) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*S. pneumoniae* |99% (97-100%,N=112) |</span>
|
||
<span class="r-out co"><span class="r-pr">#></span> |*S. pyogenes* |95% (81-100%,N=16) |</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><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></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>
|
||
</div>
|
||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||
</nav></aside></div>
|
||
|
||
|
||
<footer><div class="pkgdown-footer-left">
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<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
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