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@@ -264,7 +264,7 @@ reading</em>, is basically a form of imputation, and is part of the
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@@ -812,14 +812,14 @@ inhibitors</td>
<code>clinical_breakpoints</code>: Interpretation from MIC values
&amp; disk diameters to SIR<a class="anchor" aria-label="anchor" href="#clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir"></a>
</h2>
<p>A data set with 34 382 rows and 14 columns, containing the following
<p>A data set with 34 376 rows and 14 columns, containing the following
column names:<br><em>guideline</em>, <em>type</em>, <em>host</em>, <em>method</em>,
<em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>,
<em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>,
<em>breakpoint_R</em>, <em>uti</em>, and <em>is_SDD</em>.</p>
<p>This data set is in R available as <code>clinical_breakpoints</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 13 March 2025 13:30:14 UTC. Find more info
<p>It was last updated on 13 March 2025 14:38:39 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/clinical_breakpoints.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
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index.html
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<a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9195</small>
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@@ -48,19 +48,19 @@
</div>
<div class="section level2">
<h2 class="pkg-version" data-toc-text="2.1.1.9195" id="amr-2119195">AMR 2.1.1.9195<a class="anchor" aria-label="anchor" href="#amr-2119195"></a></h2>
<h2 class="pkg-version" data-toc-text="2.1.1.9196" id="amr-2119196">AMR 2.1.1.9196<a class="anchor" aria-label="anchor" href="#amr-2119196"></a></h2>
<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
<div class="section level5">
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9195">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9195"></a></h5>
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9196">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9196"></a></h5>
<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
</div>
<div class="section level3">
<h3 id="breaking-2-1-1-9195">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9195"></a></h3>
<h3 id="breaking-2-1-1-9196">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9196"></a></h3>
<ul><li>Dataset <code>antibiotics</code> has been renamed to <code>antimicrobials</code> as the data set contains more than just antibiotics. Using <code>antibiotics</code> will still work, but now returns a warning.</li>
<li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over two years ago.</li>
</ul></div>
<div class="section level3">
<h3 id="new-2-1-1-9195">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9195"></a></h3>
<h3 id="new-2-1-1-9196">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9196"></a></h3>
<ul><li>
<strong>One Health implementation</strong>
<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
@@ -113,7 +113,7 @@
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="changed-2-1-1-9195">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9195"></a></h3>
<h3 id="changed-2-1-1-9196">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9196"></a></h3>
<ul><li>SIR interpretation
<ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
<li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
@@ -187,7 +187,7 @@
<li>Added console colours support of <code>sir</code> class for Positron</li>
</ul></div>
<div class="section level3">
<h3 id="other-2-1-1-9195">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9195"></a></h3>
<h3 id="other-2-1-1-9196">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9196"></a></h3>
<ul><li>Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way</li>
<li>Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations</li>
<li>Greatly improved <code>vctrs</code> integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html" class="external-link">rowwise()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/c_across.html" class="external-link">c_across()</a></code> are now supported for e.g. columns of class <code>mic</code>. Despite this, this <code>AMR</code> package is still zero-dependent on any other package, including <code>dplyr</code> and <code>vctrs</code>.</li>
@@ -195,7 +195,7 @@
<li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
</ul></div>
<div class="section level3">
<h3 id="older-versions-2-1-1-9195">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9195"></a></h3>
<h3 id="older-versions-2-1-1-9196">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9196"></a></h3>
<p>This changelog only contains changes from AMR v3.0 (March 2025) and later.</p>
<ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
<li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>
pkgdown.yml
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resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2025-03-13T13:35Z
last_built: 2025-03-13T14:42Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
reference/AMR-deprecated.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/AMR-options.html
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reference/AMR.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/WHOCC.html
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reference/ab_from_text.html
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reference/add_custom_antimicrobials.html
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reference/age_groups.html
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@@ -7,7 +7,7 @@
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reference/antibiogram.html
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@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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reference/antimicrobial_selectors.html
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@@ -17,7 +17,7 @@ my_data_with_all_these_columns %&amp;gt;%
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reference/antimicrobials.html
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@@ -7,7 +7,7 @@
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reference/as.ab.html
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@@ -7,7 +7,7 @@
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reference/as.av.html
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@@ -7,7 +7,7 @@
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reference/as.disk.html
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@@ -7,7 +7,7 @@
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reference/as.mic.html
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@@ -7,7 +7,7 @@
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@@ -7,7 +7,7 @@
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reference/as.sir.html
+24 -42
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@@ -3,14 +3,14 @@
These breakpoints are currently implemented:
For clinical microbiology: EUCAST 2011-2024 and CLSI 2011-2024;
For veterinary microbiology: EUCAST 2021-2024 and CLSI 2019-2024;
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.
All breakpoints used for interpretation are available in our clinical_breakpoints data set."><meta property="og:description" content="Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. as.sir() transforms the input to a new class sir, which is an ordered factor containing the levels S, SDD, I, R, NI.
These breakpoints are currently implemented:
For clinical microbiology: EUCAST 2011-2024 and CLSI 2011-2024;
For veterinary microbiology: EUCAST 2021-2024 and CLSI 2019-2024;
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.
All breakpoints used for interpretation are available in our clinical_breakpoints data set."><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>
@@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@@ -66,7 +66,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
<p>Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. <code>as.sir()</code> transforms the input to a new class <code>sir</code>, which is an ordered <a href="https://rdrr.io/r/base/factor.html" class="external-link">factor</a> containing the levels <code>S</code>, <code>SDD</code>, <code>I</code>, <code>R</code>, <code>NI</code>.</p>
<p>These breakpoints are currently implemented:</p><ul><li><p>For <strong>clinical microbiology</strong>: EUCAST 2011-2024 and CLSI 2011-2024;</p></li>
<li><p>For <strong>veterinary microbiology</strong>: EUCAST 2021-2024 and CLSI 2019-2024;</p></li>
<li><p>For <strong>ECOFFs</strong> (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.</p></li>
<li><p>For <strong>ECOFFs</strong> (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.</p></li>
</ul><p>All breakpoints used for interpretation are available in our <a href="clinical_breakpoints.html">clinical_breakpoints</a> data set.</p>
</div>
@@ -125,7 +125,6 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
<p>For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:</p><ul><li><p><strong>CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data</strong>, 2011-2024, <em>Clinical and Laboratory Standards Institute</em> (CLSI). <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">https://clsi.org/standards/products/microbiology/documents/m39/</a>.</p></li>
<li><p><strong>CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing</strong>, 2011-2024, <em>Clinical and Laboratory Standards Institute</em> (CLSI). <a href="https://clsi.org/standards/products/microbiology/documents/m100/" class="external-link">https://clsi.org/standards/products/microbiology/documents/m100/</a>.</p></li>
<li><p><strong>CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals</strong>, 2019-2024, <em>Clinical and Laboratory Standards Institute</em> (CLSI). <a href="https://clsi.org/standards/products/veterinary-medicine/documents/vet01/" class="external-link">https://clsi.org/standards/products/veterinary-medicine/documents/vet01/</a>.</p></li>
<li><p><strong>CLSI VET09: Understanding Susceptibility Test Data as a Component of Antimicrobial Stewardship in Veterinary Settings</strong>, 2019-2024, <em>Clinical and Laboratory Standards Institute</em> (CLSI). <a href="https://clsi.org/standards/products/veterinary-medicine/documents/vet09/" class="external-link">https://clsi.org/standards/products/veterinary-medicine/documents/vet09/</a>.</p></li>
<li><p><strong>EUCAST Breakpoint tables for interpretation of MICs and zone diameters</strong>, 2011-2024, <em>European Committee on Antimicrobial Susceptibility Testing</em> (EUCAST). <a href="https://www.eucast.org/clinical_breakpoints" class="external-link">https://www.eucast.org/clinical_breakpoints</a>.</p></li>
<li><p><strong>WHONET</strong> as a source for machine-reading the clinical breakpoints (<a href="https://msberends.github.io/AMR/reference/clinical_breakpoints.html#imported-from-whonet">read more here</a>), 1989-2024, <em>WHO Collaborating Centre for Surveillance of Antimicrobial Resistance</em>. <a href="https://whonet.org/" class="external-link">https://whonet.org/</a>.</p></li>
</ul></div>
@@ -258,7 +257,6 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
<p>For veterinary guidelines, these might be the best options:</p>
<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a> <span class="fu">options</span>(<span class="at">AMR_guideline =</span> <span class="st">"CLSI"</span>)</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a> <span class="fu">options</span>(<span class="at">AMR_breakpoint_type =</span> <span class="st">"animal"</span>)</span></code></pre><p></p></div>
<p>When applying veterinary breakpoints (by setting <code>host</code> or by setting <code>breakpoint_type = "animal"</code>), the <a href="https://clsi.org/standards/products/veterinary-medicine/documents/vet09/" class="external-link">CLSI VET09 guideline</a> will be applied to cope with missing animal species-specific breakpoints.</p>
</div>
<div class="section">
@@ -273,7 +271,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
<h3 id="machine-readable-clinical-breakpoints">Machine-Readable Clinical Breakpoints<a class="anchor" aria-label="anchor" href="#machine-readable-clinical-breakpoints"></a></h3>
<p>The repository of this package <a href="https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt" class="external-link">contains a machine-readable version</a> of all guidelines. This is a CSV file consisting of 34 382 rows and 14 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. <strong>This allows for easy implementation of these rules in laboratory information systems (LIS)</strong>. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.</p>
<p>The repository of this package <a href="https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt" class="external-link">contains a machine-readable version</a> of all guidelines. This is a CSV file consisting of 34 376 rows and 14 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. <strong>This allows for easy implementation of these rules in laboratory information systems (LIS)</strong>. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.</p>
</div>
<div class="section">
@@ -682,9 +680,15 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Assuming </span><span style="color: #0000BB; background-color: #EEEEEE;">breakpoint_type = "animal"</span><span style="color: #0000BB;">, since </span><span style="color: #0000BB; background-color: #EEEEEE;">host</span><span style="color: #0000BB;"> is set.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Please note that in the absence of specific veterinary breakpoints for</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> certain animal hosts, the CLSI guideline VET09 will be applied where</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> possible.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">amoxicillin</span><span style="color: #0000BB;">' (AMX), '</span><span style="color: #0000BB; font-weight: bold;">cipro</span><span style="color: #0000BB;">' (CIP, ciprofloxacin),</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">'</span><span style="color: #0000BB; font-weight: bold;">tobra</span><span style="color: #0000BB;">' (TOB, tobramycin), and '</span><span style="color: #0000BB; font-weight: bold;">genta</span><span style="color: #0000BB;">' (GEN, gentamicin) based on column</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">'</span><span style="color: #0000BB; font-weight: bold;">bacteria</span><span style="color: #0000BB;">', </span><span style="color: #0000BB; font-weight: bold;">CLSI 2024</span><span style="color: #0000BB;">...</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #080808; background-color: #5FD7AF;"> OK </span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">ℹ Run </span><span style="color: #00BB00; background-color: #EEEEEE;">sir_interpretation_history()</span><span style="color: #00BB00;"> afterwards to retrieve a logbook with</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;"> all the details of the breakpoint interpretations.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Assuming </span><span style="color: #0000BB; background-color: #EEEEEE;">breakpoint_type = "animal"</span><span style="color: #0000BB;">, since </span><span style="color: #0000BB; background-color: #EEEEEE;">host</span><span style="color: #0000BB;"> is set.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">amoxicillin</span><span style="color: #0000BB;">' (AMX), '</span><span style="color: #0000BB; font-weight: bold;">cipro</span><span style="color: #0000BB;">' (CIP, ciprofloxacin),</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">'</span><span style="color: #0000BB; font-weight: bold;">tobra</span><span style="color: #0000BB;">' (TOB, tobramycin), and '</span><span style="color: #0000BB; font-weight: bold;">genta</span><span style="color: #0000BB;">' (GEN, gentamicin) based on column</span></span>
@@ -696,24 +700,6 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Assuming </span><span style="color: #0000BB; background-color: #EEEEEE;">breakpoint_type = "animal"</span><span style="color: #0000BB;">, since </span><span style="color: #0000BB; background-color: #EEEEEE;">host</span><span style="color: #0000BB;"> is set.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Please note that in the absence of specific veterinary breakpoints for</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> certain animal hosts, the CLSI guideline VET09 will be applied where</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> possible.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">amoxicillin</span><span style="color: #0000BB;">' (AMX), '</span><span style="color: #0000BB; font-weight: bold;">cipro</span><span style="color: #0000BB;">' (CIP, ciprofloxacin),</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">'</span><span style="color: #0000BB; font-weight: bold;">tobra</span><span style="color: #0000BB;">' (TOB, tobramycin), and '</span><span style="color: #0000BB; font-weight: bold;">genta</span><span style="color: #0000BB;">' (GEN, gentamicin) based on column</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">'</span><span style="color: #0000BB; font-weight: bold;">bacteria</span><span style="color: #0000BB;">', </span><span style="color: #0000BB; font-weight: bold;">CLSI 2024</span><span style="color: #0000BB;">...</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #080808; background-color: #5FD7AF;"> OK </span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">ℹ Run </span><span style="color: #00BB00; background-color: #EEEEEE;">sir_interpretation_history()</span><span style="color: #00BB00;"> afterwards to retrieve a logbook with</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;"> all the details of the breakpoint interpretations.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Assuming </span><span style="color: #0000BB; background-color: #EEEEEE;">breakpoint_type = "animal"</span><span style="color: #0000BB;">, since </span><span style="color: #0000BB; background-color: #EEEEEE;">host</span><span style="color: #0000BB;"> is set.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Please note that in the absence of specific veterinary breakpoints for</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> certain animal hosts, the CLSI guideline VET09 will be applied where</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> possible.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">antibiotic</span><span style="color: #0000BB;">' (TESTAB, test Antibiotic), </span><span style="color: #0000BB; font-weight: bold;">CLSI</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB; font-weight: bold;">2024</span><span style="color: #0000BB;">...</span></span>
@@ -738,10 +724,6 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Assuming </span><span style="color: #0000BB; background-color: #EEEEEE;">breakpoint_type = "animal"</span><span style="color: #0000BB;">, since </span><span style="color: #0000BB; background-color: #EEEEEE;">host</span><span style="color: #0000BB;"> is set.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Please note that in the absence of specific veterinary breakpoints for</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> certain animal hosts, the CLSI guideline VET09 will be applied where</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;"> possible.</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">cipro</span><span style="color: #0000BB;">' (CIP, ciprofloxacin), </span><span style="color: #0000BB; font-weight: bold;">CLSI 2024</span><span style="color: #0000BB;">...</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #080808; background-color: #5FD7AF;"> OK </span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">Interpreting disk diffusion zones: '</span><span style="color: #0000BB; font-weight: bold;">tobra</span><span style="color: #0000BB;">' (TOB, tobramycin), </span><span style="color: #0000BB; font-weight: bold;">CLSI 2024</span><span style="color: #0000BB;">...</span></span>
@@ -817,16 +799,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 55 × 16</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index ab_given mo_given host_given ab mo </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-03-13 <span style="color: #949494;">13:36:01</span> 4 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-03-13 <span style="color: #949494;">13:36:09</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-03-13 <span style="color: #949494;">13:36:10</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-03-13 <span style="color: #949494;">13:36:11</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-03-13 <span style="color: #949494;">13:36:12</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-03-13 <span style="color: #949494;">13:36:01</span> 3 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-03-13 <span style="color: #949494;">13:36:09</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-03-13 <span style="color: #949494;">13:36:10</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-03-13 <span style="color: #949494;">13:36:11</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-03-13 <span style="color: #949494;">13:36:12</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-03-13 <span style="color: #949494;">14:43:20</span> 4 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-03-13 <span style="color: #949494;">14:43:29</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-03-13 <span style="color: #949494;">14:43:29</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-03-13 <span style="color: #949494;">14:43:31</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-03-13 <span style="color: #949494;">14:43:31</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-03-13 <span style="color: #949494;">14:43:20</span> 3 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-03-13 <span style="color: #949494;">14:43:29</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-03-13 <span style="color: #949494;">14:43:29</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-03-13 <span style="color: #949494;">14:43:31</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-03-13 <span style="color: #949494;">14:43:31</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 45 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;chr&gt;, outcome &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>
reference/atc_online.html
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reference/clinical_breakpoints.html
+7 -7
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@@ -3,14 +3,14 @@
These breakpoints are currently implemented:
For clinical microbiology: EUCAST 2011-2024 and CLSI 2011-2024;
For veterinary microbiology: EUCAST 2021-2024 and CLSI 2019-2024;
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.
Use as.sir() to transform MICs or disks measurements to SIR values."><meta property="og:description" content="Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This dataset contain breakpoints for humans, 7 different animal groups, and ECOFFs.
These breakpoints are currently implemented:
For clinical microbiology: EUCAST 2011-2024 and CLSI 2011-2024;
For veterinary microbiology: EUCAST 2021-2024 and CLSI 2019-2024;
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.
For ECOFFs (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.
Use as.sir() to transform MICs or disks measurements to SIR values."><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>
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@@ -66,7 +66,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
<p>Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This dataset contain breakpoints for humans, 7 different animal groups, and ECOFFs.</p>
<p>These breakpoints are currently implemented:</p><ul><li><p>For <strong>clinical microbiology</strong>: EUCAST 2011-2024 and CLSI 2011-2024;</p></li>
<li><p>For <strong>veterinary microbiology</strong>: EUCAST 2021-2024 and CLSI 2019-2024;</p></li>
<li><p>For <strong>ECOFFs</strong> (Epidemiological Cut-off Values): EUCAST 2020-2024 and CLSI 2022-2024.</p></li>
<li><p>For <strong>ECOFFs</strong> (Epidemiological Cut-off Values): EUCAST 2012-2024 and CLSI 2022-2024.</p></li>
</ul><p>Use <code><a href="as.sir.html">as.sir()</a></code> to transform MICs or disks measurements to SIR values.</p>
</div>
@@ -77,7 +77,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
<div class="section level2">
<h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
<p>A <a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a> with 34 382 observations and 14 variables:</p><ul><li><p><code>guideline</code><br> Name of the guideline</p></li>
<p>A <a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a> with 34 376 observations and 14 variables:</p><ul><li><p><code>guideline</code><br> Name of the guideline</p></li>
<li><p><code>type</code><br> Breakpoint type, either "ECOFF", "animal", or "human"</p></li>
<li><p><code>host</code><br> Host of infectious agent. This is mostly useful for veterinary breakpoints and is either "ECOFF", "aquatic", "cats", "cattle", "dogs", "horse", "human", "poultry", or "swine"</p></li>
<li><p><code>method</code><br> Testing method, either "DISK" or "MIC"</p></li>
@@ -134,7 +134,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
<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="va">clinical_breakpoints</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 34,382 × 14</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 34,376 × 14</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> guideline type host method site mo rank_index ab ref_tbl </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> EUCAST 2024 human human DISK <span style="color: #BB0000;">NA</span> <span style="color: #949494;">B_</span>ACHRMB<span style="color: #949494;">_</span>XYLS 2 MEM A. xylo…</span>
@@ -147,7 +147,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> EUCAST 2024 human human DISK Uncomp… <span style="color: #949494;">B_</span>ACNTB 3 AMK Acineto…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> EUCAST 2024 human human MIC <span style="color: #BB0000;">NA</span> <span style="color: #949494;">B_</span>ACNTB 3 AMK Acineto…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> EUCAST 2024 human human MIC Uncomp… <span style="color: #949494;">B_</span>ACNTB 3 AMK Acineto…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 34,372 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 34,366 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 5 more variables: disk_dose &lt;chr&gt;, breakpoint_S &lt;dbl&gt;, breakpoint_R &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># uti &lt;lgl&gt;, is_SDD &lt;lgl&gt;</span></span>
</code></pre></div>
reference/count.html
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@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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@@ -7,7 +7,7 @@
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@@ -7,7 +7,7 @@
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@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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@@ -217,13 +217,13 @@ Leclercq et al. <strong>EUCAST expert rules in antimicrobial susceptibility test
<span class="r-in"><span><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu">eucast_rules</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>in <span style="background-color: #EEEEEE;">eucast_rules()</span>: not all columns with antimicrobial results are of</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> class 'sir'. Transform them on beforehand, with e.g.:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% as.sir(VAN:AMX)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% as.sir(CXM:AMX)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% mutate_if(is_sir_eligible, as.sir)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% mutate(across(where(is_sir_eligible), as.sir))</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> mo VAN AMX COL CAZ CXM PEN FOX</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 Staphylococcus aureus - S R R - S S</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 Staphylococcus aureus - S R R S S S</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 Enterococcus faecalis - - R R R S R</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 Escherichia coli R - - - - R S</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 Klebsiella pneumoniae R R - - - R S</span>
@@ -235,27 +235,27 @@ Leclercq et al. <strong>EUCAST expert rules in antimicrobial susceptibility test
<span class="r-in"><span><span class="va">c</span> <span class="op">&lt;-</span> <span class="fu">eucast_rules</span><span class="op">(</span><span class="va">a</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>in <span style="background-color: #EEEEEE;">eucast_rules()</span>: not all columns with antimicrobial results are of</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> class 'sir'. Transform them on beforehand, with e.g.:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% as.sir(VAN:AMX)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% as.sir(CXM:AMX)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% mutate_if(is_sir_eligible, as.sir)</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> - a %&gt;% mutate(across(where(is_sir_eligible), as.sir))</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">c</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> row col mo_fullname old new rule rule_group</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1 AMX Staphylococcus aureus - S Breakpoints</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1 CAZ Staphylococcus aureus - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1 COL Staphylococcus aureus - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 2 CAZ Enterococcus faecalis - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 2 COL Enterococcus faecalis - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 2 CXM Enterococcus faecalis - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1 CXM Staphylococcus aureus - S Breakpoints</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1 CAZ Staphylococcus aureus - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1 COL Staphylococcus aureus - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 2 CAZ Enterococcus faecalis - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 2 COL Enterococcus faecalis - R Expert Rules</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> rule_name</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 Staphylococcus</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 Table 4: Intrinsic resistance in gram-positive bacteria</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 Staphylococcus</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 Table 4: Intrinsic resistance in gram-positive bacteria</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 Table 4: Intrinsic resistance in gram-positive bacteria</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 Table 4: Intrinsic resistance in gram-positive bacteria</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 Table 4: Intrinsic resistance in gram-positive bacteria</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> rule_source</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021</span>
reference/example_isolates.html
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+1 -1
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reference/key_antimicrobials.html
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reference/like.html
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reference/microorganisms.html
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@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
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@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
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[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> ClLine truncated
[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> ClLine truncated