<!-- Generated by pkgdown: do not edit by hand --><htmllang="en"><head><metahttp-equiv="Content-Type"content="text/html; charset=UTF-8"><metacharset="utf-8"><metahttp-equiv="X-UA-Compatible"content="IE=edge"><metaname="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 --><linkrel="icon"type="image/png"sizes="16x16"href="../favicon-16x16.png"><linkrel="icon"type="image/png"sizes="32x32"href="../favicon-32x32.png"><linkrel="apple-touch-icon"type="image/png"sizes="180x180"href="../apple-touch-icon.png"><linkrel="apple-touch-icon"type="image/png"sizes="120x120"href="../apple-touch-icon-120x120.png"><linkrel="apple-touch-icon"type="image/png"sizes="76x76"href="../apple-touch-icon-76x76.png"><linkrel="apple-touch-icon"type="image/png"sizes="60x60"href="../apple-touch-icon-60x60.png"><scriptsrc="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><metaname="viewport"content="width=device-width, initial-scale=1, shrink-to-fit=no"><linkhref="../deps/bootstrap-5.3.1/bootstrap.min.css"rel="stylesheet"><scriptsrc="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><linkhref="../deps/Lato-0.4.9/font.css"rel="stylesheet"><linkhref="../deps/Fira_Code-0.4.9/font.css"rel="stylesheet"><linkhref="../deps/font-awesome-6.5.2/css/all.min.css"rel="stylesheet"><linkhref="../deps/font-awesome-6.5.2/css/v4-shims.min.css"rel="stylesheet"><scriptsrc="../deps/headroom-0.11.0/headroom.min.js"></script><scriptsrc="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><scriptsrc="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><scriptsrc="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><scriptsrc="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><scriptsrc="../deps/search-1.0.0/fuse.min.js"></script><scriptsrc="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><scriptsrc="../pkgdown.js"></script><linkhref="../extra.css"rel="stylesheet"><scriptsrc="../extra.js"></script><metaproperty="og:title"content="Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram"><metaname="description"content="Createdetailedantibiogramswithoptionsfortraditional,combination,syndromic,andBayesianWISCAmethods.
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."><metaproperty="og:description"content="Createdetailedantibiogramswithoptionsfortraditional,combination,syndromic,andBayesianWISCAmethods.
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."><metaproperty="og:image"content="https://msberends.github.io/AMR/logo.svg"><linkrel="stylesheet"href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css"integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+"crossorigin="anonymous"><scriptdefersrc="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js"integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg"crossorigin="anonymous"></script><scriptdefersrc="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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<ulclass="dropdown-menu"aria-labelledby="dropdown-how-to"><li><aclass="dropdown-item"href="../articles/AMR.html"><spanclass="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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<p>Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.</p>
<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>
<span> info <spanclass="op">=</span><spanclass="fu"><ahref="https://rdrr.io/r/base/interactive.html"class="external-link">interactive</a></span><spanclass="op">(</span><spanclass="op">)</span><spanclass="op">)</span></span>
<span> info <spanclass="op">=</span><spanclass="fu"><ahref="https://rdrr.io/r/base/interactive.html"class="external-link">interactive</a></span><spanclass="op">(</span><spanclass="op">)</span><spanclass="op">)</span></span>
<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); <ahref="https://doi.org/10.1093/jac/dkv397"class="external-link">doi:10.1093/jac/dkv397</a></p></li>
<li><p>Bielicki JA <em>et al.</em> (2020). <strong>Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries</strong><em>JAMA Netw Open.</em> 3(2):e1921124; <ahref="https://doi.org/10.1001.jamanetworkopen.2019.21124"class="external-link">doi:10.1001.jamanetworkopen.2019.21124</a></p></li>
<li><p>Klinker KP <em>et al.</em> (2021). <strong>Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms</strong>. <em>Therapeutic Advances in Infectious Disease</em>, May 5;8:20499361211011373; <ahref="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 & Infection Control</em> May 1;10(1):74; <ahref="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>. <ahref="https://clsi.org/standards/products/microbiology/documents/m39/"class="external-link">https://clsi.org/standards/products/microbiology/documents/m39/</a>.</p></li>
<dd><p>a <ahref="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><ahref="as.sir.html">as.sir()</a></code>)</p></dd>
<dd><p>vector of any antimicrobial name or code (will be evaluated with <code><ahref="as.ab.html">as.ab()</a></code>, column name of <code>x</code>, or (any combinations of) <ahref="antimicrobial_class_selectors.html">antimicrobial selectors</a> such as <code><ahref="antimicrobial_class_selectors.html">aminoglycosides()</a></code> or <code><ahref="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>
<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 <ahref="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>
<dd><p>a character to transform antimicrobial input - must be one of the column names of the <ahref="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>
<dd><p>a column name of <code>x</code>, or values calculated to split rows of <code>x</code>, e.g. by using <code><ahref="https://rdrr.io/r/base/ifelse.html"class="external-link">ifelse()</a></code> or <code><ahref="https://dplyr.tidyverse.org/reference/case_when.html"class="external-link">case_when()</a></code>. See <em>Examples</em>.</p></dd>
<dd><p>a <ahref="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>
<dd><p>(for combination antibiograms): a <ahref="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>
<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>
<dd><p>column name of the names or codes of the microorganisms (see <code><ahref="as.mo.html">as.mo()</a></code>) - the default is the first column of class <code><ahref="as.mo.html">mo</a></code>. Values will be coerced using <code><ahref="as.mo.html">as.mo()</a></code>.</p></dd>
<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>
<dd><p>a <ahref="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>
<dd><p>a <ahref="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>
<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>
<dd><p>a <ahref="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>
<dd><p>when used in <ahref="https://rdrr.io/pkg/knitr/man/kable.html"class="external-link">R Markdown or Quarto</a>: arguments passed on to <code><ahref="https://rdrr.io/pkg/knitr/man/kable.html"class="external-link">knitr::kable()</a></code> (otherwise, has no use)</p></dd>
<dd><p>a <ahref="https://rdrr.io/r/base/logical.html"class="external-link">logical</a> to indicate whether the microorganism names in the <ahref="https://rdrr.io/pkg/knitr/man/kable.html"class="external-link">knitr</a> table should be made italic, using <code><ahref="italicise_taxonomy.html">italicise_taxonomy()</a></code>.</p></dd>
<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><ahref="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><ahref="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 <ahref="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><divclass="section">
<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>
<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><ahref="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>
<p>There are various antibiogram types, as summarised by Klinker <em>et al.</em> (2021, <ahref="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, <ahref="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>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 <ahref="https://tibble.tidyverse.org/reference/tibble.html"class="external-link">tibbles</a> can also be used to calculate susceptibilities over various groups.</p>
<h3id="inclusion-in-combination-antibiogram-and-syndromic-antibiogram">Inclusion in Combination Antibiogram and Syndromic Antibiogram<aclass="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>
<spanid="cb1-7"><ahref="#cb1-7"tabindex="-1"></a> S or I S or I X X X X</span>
<spanid="cb1-8"><ahref="#cb1-8"tabindex="-1"></a> R S or I X X X X</span>
<spanid="cb1-9"><ahref="#cb1-9"tabindex="-1"></a><spanclass="sc"><</span><spanclass="cn">NA</span><spanclass="sc">></span> S or I X X <spanclass="sc">-</span><spanclass="sc">-</span></span>
<spanid="cb1-10"><ahref="#cb1-10"tabindex="-1"></a> S or I R X X X X</span>
<spanid="cb1-11"><ahref="#cb1-11"tabindex="-1"></a> R R <spanclass="sc">-</span> X <spanclass="sc">-</span> X</span>
<spanid="cb1-12"><ahref="#cb1-12"tabindex="-1"></a><spanclass="sc"><</span><spanclass="cn">NA</span><spanclass="sc">></span> R <spanclass="sc">-</span><spanclass="sc">-</span><spanclass="sc">-</span><spanclass="sc">-</span></span>
<spanid="cb1-13"><ahref="#cb1-13"tabindex="-1"></a> S or I <spanclass="sc"><</span><spanclass="cn">NA</span><spanclass="sc">></span> X X <spanclass="sc">-</span><spanclass="sc">-</span></span>
<spanid="cb1-14"><ahref="#cb1-14"tabindex="-1"></a> R <spanclass="sc"><</span><spanclass="cn">NA</span><spanclass="sc">></span><spanclass="sc">-</span><spanclass="sc">-</span><spanclass="sc">-</span><spanclass="sc">-</span></span>
<p>All types of antibiograms as listed above can be plotted (using <code><ahref="https://ggplot2.tidyverse.org/reference/autoplot.html"class="external-link">ggplot2::autoplot()</a></code> or base <spanstyle="R">R</span>'s <code><ahref="plot.html">plot()</a></code> and <code><ahref="https://rdrr.io/r/graphics/barplot.html"class="external-link">barplot()</a></code>). As mentioned above, the numeric values of an antibiogram are stored in a long format as the <ahref="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>.</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><ahref="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>
<divclass="section level2">
<h2id="why-use-wisca-">Why Use WISCA?<aclass="anchor"aria-label="anchor"href="#why-use-wisca-"></a></h2>
), 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>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>Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \(\alpha\), where each \(\alpha_i\) corresponds to a specific pathogen. The prior is typically chosen to be uniform (\(\alpha_i = 1\)), reflecting an assumption of equal prior probability across pathogens.</p>
<p>The posterior distribution of pathogen incidence is then given by:</p>
<p>where \(n_i\) is the number of infections caused by pathogen \(i\) observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:</p>
<p>$$x_i \sim \text{Gamma}(\alpha_i + n_i, 1)$$
$$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
<p>where \(x_i\) represents unnormalised pathogen counts, and \(p_i\) is the normalised proportion for pathogen \(i\).</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><ahref="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>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>
<divclass="sourceCode"><preclass="sourceCode r"><code><spanclass="r-in"><span><spanclass="co"># example_isolates is a data set available in the AMR package.</span></span></span>
<spanclass="r-in"><span><spanclass="co"># run ?example_isolates for more info.</span></span></span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 1</span> 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 2</span> 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 3</span> 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 4</span> 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 5</span> 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 6</span> 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 7</span> 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 8</span> 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;"> 9</span> 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA </span>
<spanclass="r-out co"><spanclass="r-pr">#></span><spanstyle="color: #BCBCBC;">10</span> 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA </span>
<p><code>AMR</code> (for R). Free and open-source, licenced under the <atarget="_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 <atarget="_blank"href="https://www.rug.nl"class="external-link">University of Groningen</a> and <atarget="_blank"href="https://www.umcg.nl"class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
