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<h1>The AMR package - How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
</div>
<p><strong>Note:</strong> values on this page will be regenerated with every website update since it is written in <a href="https://rmarkdown.rstudio.com/">RMarkdown</a>, so actual results will change over time. However, the methodology remains unchanged. This page was generated on 31 December 2018.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
<p>(work in progress)</p>
</div>
<div id="tutorial" class="section level1">
<h1 class="hasAnchor">
<a href="#tutorial" class="anchor"></a>Tutorial</h1>
<p>For this tutorial, we will create fake demonstration data to work with.</p>
<p>You can skip to <a href="#cleaning-the-data">Cleaning the data</a> if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:</p>
<table class="table">
<thead><tr class="header">
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">mo</th>
<th align="center">amox</th>
<th align="center">cipr</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2018-12-31</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2018-12-31</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2018-12-31</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
</tr>
</tbody>
</table>
<div id="needed-r-packages" class="section level2">
<h2 class="hasAnchor">
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR analysis. The most important one is <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a>, which tremendously improves the way we work with data - it allows for a very natural way of writing syntaxes in R. Another important dependency is <a href="https://ggplot2.tidyverse.org/"><code>ggplot2</code></a>. This package can be used to create beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr) <span class="co"># the data science package</span>
<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR) <span class="co"># this package, to simplify and automate AMR analysis</span>
<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2) <span class="co"># for appealing plots</span></code></pre></div>
</div>
<div id="creation-of-data" class="section level2">
<h2 class="hasAnchor">
<a href="#creation-of-data" class="anchor"></a>Creation of data</h2>
<p>We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).</p>
<p>With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.</p>
<div id="patients" class="section level4">
<h4 class="hasAnchor">
<a href="#patients" class="anchor"></a>Patients</h4>
<p>To start with patients, we need a unique list of patients.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">patients &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/unlist">unlist</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/lapply">lapply</a></span>(LETTERS, paste0, <span class="dv">1</span>:<span class="dv">10</span>))</code></pre></div>
<p>The <code>LETTERS</code> object is available in R - it’s a vector with 26 characters: <code>A</code> to <code>Z</code>. The <code>patients</code> object we just created is now a vector of length 260, with values (patient IDs) varying from <code>A1</code> to <code>Z10</code>. Now we we also set the gender of our patients, by putting the ID and the gender in a table:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">patients_table &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">patient_id =</span> patients,
<span class="dt">gender =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/strrep">strrep</a></span>(<span class="st">"M"</span>, <span class="dv">135</span>),
<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/strrep">strrep</a></span>(<span class="st">"F"</span>, <span class="dv">125</span>)))</code></pre></div>
<p>The first 135 patient IDs are now male, the other 125 are female.</p>
</div>
<div id="dates" class="section level4">
<h4 class="hasAnchor">
<a href="#dates" class="anchor"></a>Dates</h4>
<p>Let’s pretend that our data consists of blood cultures isolates from 1 January 2010 until 1 January 2018.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dates &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/seq">seq</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/as.Date">as.Date</a></span>(<span class="st">"2010-01-01"</span>), <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/as.Date">as.Date</a></span>(<span class="st">"2018-01-01"</span>), <span class="dt">by =</span> <span class="st">"day"</span>)</code></pre></div>
<p>This <code>dates</code> object now contains all days in our date range.</p>
</div>
<div id="microorganisms" class="section level4">
<h4 class="hasAnchor">
<a href="#microorganisms" class="anchor"></a>Microorganisms</h4>
<p>For this tutorial, we will uses four different microorganisms: <em>Escherichia coli</em>, <em>Staphylococcus aureus</em>, <em>Streptococcus pneumoniae</em>, and <em>Klebsiella pneumoniae</em>:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bacteria &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"Escherichia coli"</span>, <span class="st">"Staphylococcus aureus"</span>,
<span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"Klebsiella pneumoniae"</span>)</code></pre></div>
</div>
<div id="other-variables" class="section level4">
<h4 class="hasAnchor">
<a href="#other-variables" class="anchor"></a>Other variables</h4>
<p>For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">hospitals &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"Hospital A"</span>, <span class="st">"Hospital B"</span>, <span class="st">"Hospital C"</span>, <span class="st">"Hospital D"</span>)
ab_interpretations &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span>)</code></pre></div>
</div>
<div id="put-everything-together" class="section level4">
<h4 class="hasAnchor">
<a href="#put-everything-together" class="anchor"></a>Put everything together</h4>
<p>Using the <code><a href="http://dplyr.tidyverse.org/reference/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">date =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(dates, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>),
<span class="dt">patient_id =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(patients, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>),
<span class="dt">hospital =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(hospitals, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>),
<span class="dt">bacteria =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(bacteria, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>, <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.50</span>, <span class="fl">0.25</span>, <span class="fl">0.15</span>, <span class="fl">0.10</span>)),
<span class="dt">amox =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>, <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.6</span>, <span class="fl">0.05</span>, <span class="fl">0.35</span>)),
<span class="dt">amcl =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>, <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.75</span>, <span class="fl">0.1</span>, <span class="fl">0.15</span>)),
<span class="dt">cipr =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>, <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.8</span>, <span class="dv">0</span>, <span class="fl">0.2</span>)),
<span class="dt">gent =</span> <span class="kw"><a href="http://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dv">5000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>, <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.92</span>, <span class="dv">0</span>, <span class="fl">0.07</span>))
)</code></pre></div>
<p>Using the <code><a href="http://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/join.html">left_join</a></span>(patients_table)</code></pre></div>
<p>The resulting data set contains 5,000 blood culture isolates. With the <code><a href="https://www.rdocumentation.org/packages/utils/topics/head">head()</a></code> function we can preview the first 6 values of this data set:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/head">head</a></span>(data)</code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">hospital</th>
<th align="center">bacteria</th>
<th align="center">amox</th>
<th align="center">amcl</th>
<th align="center">cipr</th>
<th align="center">gent</th>
<th align="center">gender</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2011-11-05</td>
<td align="center">F8</td>
<td align="center">Hospital A</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
</tr>
<tr class="even">
<td align="center">2013-06-01</td>
<td align="center">L1</td>
<td align="center">Hospital C</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
</tr>
<tr class="odd">
<td align="center">2014-05-29</td>
<td align="center">X5</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
</tr>
<tr class="even">
<td align="center">2013-06-29</td>
<td align="center">K6</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
</tr>
<tr class="odd">
<td align="center">2015-02-02</td>
<td align="center">W2</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
</tr>
<tr class="even">
<td align="center">2011-10-28</td>
<td align="center">T5</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
</tr>
</tbody>
</table>
<p>Now, let’s start the cleaning and the analysis!</p>
</div>
</div>
<div id="cleaning-the-data" class="section level2">
<h2 class="hasAnchor">
<a href="#cleaning-the-data" class="anchor"></a>Cleaning the data</h2>
<p>Use the frequency table function <code><a href="../reference/freq.html">freq()</a></code> to look specifically for unique values in any variable. For example, for the <code>gender</code> variable:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data %&gt;%<span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(gender) <span class="co"># this would be the same: freq(data$gender)</span></code></pre></div>
<pre><code># Frequency table of `gender`
# Class: factor (numeric)
# Levels: FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF, MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM
# Length: 5,000 (of which NA: 0 = 0.00%)
# Unique: 2
#
# Item Count Percent Cum. Count Cum. Percent
# --- ---------------------------------------------------------------------------------------------------------------------------------------- ------ -------- ----------- -------------
# 1 FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF 2,536 50.7% 2,536 50.7%
# 2 MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM 2,464 49.3% 5,000 100.0%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code>mutate</code> function of the dplyr package makes this really easy:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">bacteria =</span> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(bacteria))</code></pre></div>
<p>We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function ensures reliability and reproducibility in these kind of variables. The <code><a href="http://dplyr.tidyverse.org/reference/summarise_all.html">mutate_at()</a></code> will run the <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function on defined variables:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/summarise_all.html">mutate_at</a></span>(<span class="kw"><a href="http://dplyr.tidyverse.org/reference/vars.html">vars</a></span>(amox:cipr), as.rsi)</code></pre></div>
<p>Finally, we will apply <a href="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">EUCAST rules</a> on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
<p>Because the amoxicillin (column <code>amox</code>) and amoxicillin/clavulanic acid (column <code>amcl</code>) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> fixes this:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span><span class="kw"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(data, <span class="dt">col_mo =</span> <span class="st">"bacteria"</span>)
<span class="co"># </span>
<span class="co"># Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST)</span>
<span class="co"># </span>
<span class="co"># EUCAST Clinical Breakpoints (v8.1, 2018)</span>
<span class="co"># Enterobacteriales (Order) (no changes)</span>
<span class="co"># Staphylococcus (no changes)</span>
<span class="co"># Enterococcus (no changes)</span>
<span class="co"># Streptococcus groups A, B, C, G (no changes)</span>
<span class="co"># Streptococcus pneumoniae (366 changes)</span>
<span class="co"># Viridans group streptococci (no changes)</span>
<span class="co"># Haemophilus influenzae (no changes)</span>
<span class="co"># Moraxella catarrhalis (no changes)</span>
<span class="co"># Anaerobic Gram positives (no changes)</span>
<span class="co"># Anaerobic Gram negatives (no changes)</span>
<span class="co"># Pasteurella multocida (no changes)</span>
<span class="co"># Campylobacter jejuni and C. coli (no changes)</span>
<span class="co"># Aerococcus sanguinicola and A. urinae (no changes)</span>
<span class="co"># Kingella kingae (no changes)</span>
<span class="co"># </span>
<span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span>
<span class="co"># Table 1: Intrinsic resistance in Enterobacteriaceae (332 changes)</span>
<span class="co"># Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)</span>
<span class="co"># Table 3: Intrinsic resistance in other Gram-negative bacteria (no changes)</span>
<span class="co"># Table 4: Intrinsic resistance in Gram-positive bacteria (699 changes)</span>
<span class="co"># Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)</span>
<span class="co"># Table 9: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)</span>
<span class="co"># Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)</span>
<span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)</span>
<span class="co"># Table 12: Interpretive rules for aminoglycosides (no changes)</span>
<span class="co"># Table 13: Interpretive rules for quinolones (no changes)</span>
<span class="co"># </span>
<span class="co"># Other rules</span>
<span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (351 changes)</span>
<span class="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes)</span>
<span class="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes)</span>
<span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (242 changes)</span>
<span class="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)</span>
<span class="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)</span>
<span class="co"># </span>
<span class="co"># =&gt; EUCAST rules affected 4,575 out of 5,000 rows -&gt; changed 1,990 test results.</span></code></pre></div>
</div>
<div id="adding-new-variables" class="section level2">
<h2 class="hasAnchor">
<a href="#adding-new-variables" class="anchor"></a>Adding new variables</h2>
<p>Now we have the microbial ID, we can add some taxonomic properties:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">gramstain =</span> <span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(bacteria),
<span class="dt">family =</span> <span class="kw"><a href="../reference/mo_property.html">mo_family</a></span>(bacteria))</code></pre></div>
<div id="first-isolates" class="section level3">
<h3 class="hasAnchor">
<a href="#first-isolates" class="anchor"></a>First isolates</h3>
<p>We also need to know which isolates we can <em>actually</em> use for analysis.</p>
<p>To conduct an analysis of antimicrobial resistance, you must <a href="https://www.ncbi.nlm.nih.gov/pubmed/17304462">only include the first isolate of every patient per episode</a> (Hindler <em>et al.</em>, Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be <a href="https://en.wikipedia.org/wiki/Selection_bias">selection bias</a>.</p>
<p>The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:</p>
<blockquote>
<p><em>(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, <strong>only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype)</strong>. The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.</em> <br>Chapter 6.4, M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="uri">https://clsi.org/standards/products/microbiology/documents/m39/</a></p>
</blockquote>
<p>This <code>AMR</code> package includes this methodology with the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">first =</span> <span class="kw"><a href="../reference/first_isolate.html">first_isolate</a></span>(.))
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<span class="co"># =&gt; Found 2,928 first isolates (58.6% of total)</span></code></pre></div>
<p>So only 58.6% is suitable for resistance analysis! We can now filter on is with the <code><a href="http://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_1st &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(first ==<span class="st"> </span><span class="ot">TRUE</span>)</code></pre></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_1st &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_isolate</a></span>()</code></pre></div>
</div>
<div id="first-weighted-isolates" class="section level3">
<h3 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h3>
<p>We made a slight twist to the CLSI algorithm, to take into account antimicrobial results. Imagine this data, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">bacteria</th>
<th align="center">amox</th>
<th align="center">amcl</th>
<th align="center">cipr</th>
<th align="center">gent</th>
<th align="center">first</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-08</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-07-04</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-07-25</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2011-08-12</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2012-03-16</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2012-08-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2013-01-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2013-06-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2013-09-18</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2014-01-13</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
</tbody>
</table>
<p>Only 4 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and show be included too. This is why we weigh isolates, based on their antibiogram. The <code><a href="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>If a column exists with a name like ‘key(…)ab’ the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">keyab =</span> <span class="kw"><a href="../reference/key_antibiotics.html">key_antibiotics</a></span>(.)) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">first_weighted =</span> <span class="kw"><a href="../reference/first_isolate.html">first_isolate</a></span>(.))
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># Warning: These columns do not exist and will be ignored: cfur, pita, trsu, vanc, teic, tetr, eryt, oxac, rifa, tobr, coli, cfot, cfta, mero.</span>
<span class="co"># THIS MAY STRONGLY INFLUENCE THE OUTCOME.</span>
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<span class="co"># NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span>
<span class="co"># [Criterion] Inclusion based on key antibiotics, ignoring I.</span>
<span class="co"># =&gt; Found 4,412 first weighted isolates (88.2% of total)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">bacteria</th>
<th align="center">amox</th>
<th align="center">amcl</th>
<th align="center">cipr</th>
<th align="center">gent</th>
<th align="center">first</th>
<th align="center">first_weighted</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-08</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-07-04</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-07-25</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2011-08-12</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2012-03-16</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2012-08-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2013-01-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2013-06-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2013-09-18</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2014-01-13</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
</tbody>
</table>
<p>Instead of 4, now 9 isolates are flagged. In total, 88.2% of all isolates are marked ‘first weighted’ - 29.7% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_1st &lt;-<span class="st"> </span>data %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</code></pre></div>
<p>So we end up with 4,412 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_1st &lt;-<span class="st"> </span>data_1st %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw"><a href="http://dplyr.tidyverse.org/reference/select.html">select</a></span>(-first, -keyab)</code></pre></div>
<p>Now our data looks like:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/head">head</a></span>(data_1st)</code></pre></div>
<table class="table">
<thead><tr class="header">
<th></th>
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">hospital</th>
<th align="center">bacteria</th>
<th align="center">amox</th>
<th align="center">amcl</th>
<th align="center">cipr</th>
<th align="center">gent</th>
<th align="center">gender</th>
<th align="center">gramstain</th>
<th align="center">family</th>
<th align="center">first_weighted</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>1</td>
<td align="center">2011-11-05</td>
<td align="center">F8</td>
<td align="center">Hospital A</td>
<td align="center">B_STRPTC_PNE</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
<td align="center">Gram positive</td>
<td align="center">Streptococcaceae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>2</td>
<td align="center">2013-06-01</td>
<td align="center">L1</td>
<td align="center">Hospital C</td>
<td align="center">B_STRPTC_PNE</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
<td align="center">Gram positive</td>
<td align="center">Streptococcaceae</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>4</td>
<td align="center">2013-06-29</td>
<td align="center">K6</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
<td align="center">Gram negative</td>
<td align="center">Enterobacteriaceae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>6</td>
<td align="center">2011-10-28</td>
<td align="center">T5</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
<td align="center">Gram negative</td>
<td align="center">Enterobacteriaceae</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>7</td>
<td align="center">2017-03-11</td>
<td align="center">D5</td>
<td align="center">Hospital D</td>
<td align="center">B_KLBSL_PNE</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM</td>
<td align="center">Gram negative</td>
<td align="center">Enterobacteriaceae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>8</td>
<td align="center">2013-04-06</td>
<td align="center">F6</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF</td>
<td align="center">Gram negative</td>
<td align="center">Enterobacteriaceae</td>
<td align="center">TRUE</td>
</tr>
</tbody>
</table>
<p>Time for the analysis!</p>
</div>
</div>
<div id="analysing-the-data" class="section level2">
<h2 class="hasAnchor">
<a href="#analysing-the-data" class="anchor"></a>Analysing the data</h2>
<p>(work in progress)</p>
</div>
</div>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<div id="tocnav">
<h2 class="hasAnchor">
<a href="#tocnav" class="anchor"></a>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#introduction">Introduction</a></li>
<li>
<a href="#tutorial">Tutorial</a><ul class="nav nav-pills nav-stacked">
<li><a href="#needed-r-packages">Needed R packages</a></li>
<li><a href="#creation-of-data">Creation of data</a></li>
<li><a href="#cleaning-the-data">Cleaning the data</a></li>
<li><a href="#adding-new-variables">Adding new variables</a></li>
<li><a href="#analysing-the-data">Analysing the data</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
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