<scriptsrc="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js"integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8="crossorigin="anonymous"></script><!-- Font Awesome icons --><linkrel="stylesheet"href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css"integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78="crossorigin="anonymous">
<p>With the function <code><ahref="../reference/mdro.html">mdro()</a></code>, you can determine which micro-organisms are multi-drug resistant organisms (MDRO).</p>
<p>The <code><ahref="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a regular <code>data.frame</code>. It tries to automatically determine the right columns for info about your isolates, like the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command <code><ahref="../reference/mdro.html">?mdro</a></code>.</p>
<p>The function support multiple guidelines. You can select a guideline with the <code>guideline</code> parameter. Currently supported guidelines are (case-insensitive):</p>
Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (<ahref="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext">link</a>)</li>
<li>
<p><code>guideline = "EUCAST"</code></p>
The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<ahref="http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf">link</a>)</li>
<li>
<p><code>guideline = "TB"</code></p>
The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (<ahref="https://www.who.int/tb/publications/pmdt_companionhandbook/en/">link</a>)</li>
<li>
<p><code>guideline = "MRGN"</code></p>
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. (<ahref="https://doi.org/10.1186/s13756-015-0047-6">link</a>)</li>
<li>
<p><code>guideline = "BRMO"</code></p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]” (<ahref="https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH">link</a>)</p>
<p>The <code><ahref="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<divclass="sourceCode"id="cb1"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb1-1"data-line-number="1"><spanclass="kw"><ahref="https://rdrr.io/r/base/library.html">library</a></span>(dplyr) <spanclass="co"># to support pipes: %>%</span></a></code></pre></div>
<aclass="sourceLine"id="cb2-3"data-line-number="3"><spanclass="st"></span><spanclass="kw"><ahref="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>() <spanclass="co"># show frequency table of the result</span></a>
<aclass="sourceLine"id="cb2-4"data-line-number="4"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `mo` as input for `col_mo`.</span></a>
<aclass="sourceLine"id="cb2-5"data-line-number="5"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Auto-guessing columns suitable for analysis...OK.</span></a>
<aclass="sourceLine"id="cb2-6"data-line-number="6"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Reliability will be improved if these antimicrobial results would be available too: SAM (ampicillin/sulbactam), ATM (aztreonam), CTT (cefotetan), CPT (ceftaroline), DAP (daptomycin), DOR (doripenem), ETP (ertapenem), FUS (fusidic acid), GEH (gentamicin-high), LVX (levofloxacin), MNO (minocycline), NET (netilmicin), PLB (polymyxin B), QDA (quinupristin/dalfopristin), STH (streptomycin-high), TLV (telavancin), TCC (ticarcillin/clavulanic acid)</span></a>
<aclass="sourceLine"id="cb2-12"data-line-number="12"><spanclass="co"># Warning in mdro(.): NA introduced for isolates where the available percentage of</span></a>
<aclass="sourceLine"id="cb2-13"data-line-number="13"><spanclass="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></a></code></pre></div>
<p>For another example, I will create a data set to determine multi-drug resistant TB:</p>
<divclass="sourceCode"id="cb3"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb3-1"data-line-number="1"><spanclass="co"># a helper function to get a random vector with values S, I and R</span></a>
<aclass="sourceLine"id="cb3-2"data-line-number="2"><spanclass="co"># with the probabilities 50% - 10% - 40%</span></a>
<aclass="sourceLine"id="cb7-2"data-line-number="2"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.</span></a>
<aclass="sourceLine"id="cb7-3"data-line-number="3"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Auto-guessing columns suitable for analysis...OK.</span></a>
<aclass="sourceLine"id="cb7-4"data-line-number="4"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Reliability will be improved if these antimicrobial results would be available too: CAP (capreomycin), RIB (rifabutin), RFP (rifapentine)</span></a>
<aclass="sourceLine"id="cb7-6"data-line-number="6"><spanclass="co"># Only results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.</span></a>
<aclass="sourceLine"id="cb7-8"data-line-number="8"><spanclass="co"># Determining multidrug-resistant organisms (MDRO), according to:</span></a>
<aclass="sourceLine"id="cb7-9"data-line-number="9"><spanclass="co"># Guideline: Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis</span></a>
<aclass="sourceLine"id="cb7-14"data-line-number="14"><spanclass="co"># => Found 4338 MDROs out of 5000 tested isolates (86.8%)</span></a></code></pre></div>
<p>Developed by <ahref="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <ahref="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <ahref="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <ahref="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <ahref="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <ahref="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
