mirror of
https://github.com/msberends/AMR.git
synced 2026-06-23 22:16:22 +02:00
Built site for AMR@3.0.1.9061: 0c1709c
This commit is contained in:
github-actions
6
404.html
6
404.html
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="https://amr-for-r.org/favicon.ico">
|
||||
<link rel="manifest" href="https://amr-for-r.org/site.webmanifest">
|
||||
<script src="https://amr-for-r.org/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="https://amr-for-r.org/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="https://amr-for-r.org/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://amr-for-r.org/deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="https://amr-for-r.org/deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="https://amr-for-r.org/deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -31,7 +31,7 @@
|
||||
|
||||
<a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>
|
||||
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>CLAUDE.md — AMR R Package • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="CLAUDE.md — AMR R Package"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>CLAUDE.md — AMR R Package • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="CLAUDE.md — AMR R Package"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
@@ -91,7 +91,7 @@
|
||||
website update since they are based on randomly created values and the
|
||||
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
|
||||
Markdown</a>. However, the methodology remains unchanged. This page was
|
||||
generated on 02 May 2026.</p>
|
||||
generated on 23 June 2026.</p>
|
||||
<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
</h2>
|
||||
@@ -147,21 +147,21 @@ make the structure of your data generally look like this:</p>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="center">2026-05-02</td>
|
||||
<td align="center">2026-06-23</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">2026-05-02</td>
|
||||
<td align="center">2026-06-23</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">2026-05-02</td>
|
||||
<td align="center">2026-06-23</td>
|
||||
<td align="center">efgh</td>
|
||||
<td align="center">Escherichia coli</td>
|
||||
<td align="center">R</td>
|
||||
@@ -218,7 +218,7 @@ cleaned as SIR values as well.</p>
|
||||
<p>With <code><a href="../reference/as.mo.html">as.mo()</a></code>, users can transform arbitrary
|
||||
microorganism names or codes to current taxonomy. The <code>AMR</code>
|
||||
package contains up-to-date taxonomic data. To be specific, currently
|
||||
included data were retrieved on 24 Jun 2024.</p>
|
||||
included data were retrieved on 07 May 2026.</p>
|
||||
<p>The codes of the AMR packages that come from <code><a href="../reference/as.mo.html">as.mo()</a></code> are
|
||||
short, but still human readable. More importantly, <code><a href="../reference/as.mo.html">as.mo()</a></code>
|
||||
supports all kinds of input:</p>
|
||||
@@ -287,24 +287,23 @@ taxonomic codes. Let’s check this:</p>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Klebsiella pneumoniae</span> complex<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella pneumoniae</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">ozaenae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella pneumoniae pneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.688</span>), <span style="font-style: italic;">Klebsiella</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pneumoniae rhinoscleromatis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.658</span>), <span style="font-style: italic;">Klebsiella pasteurii</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.500</span>), <span style="font-style: italic;">Klebsiella</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">planticola</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.500</span>), <span style="font-style: italic;">Kingella potus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.400</span>), <span style="font-style: italic;">Kluyveromyces pseudotropicale</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.386</span>), <span style="font-style: italic;">Kluyveromyces pseudotropicalis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.363</span>), and <span style="font-style: italic;">Kosakonia pseudosacchari</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.361</span>)</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">planticola</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.500</span>), <span style="font-style: italic;">Kosakonia pseudosacchari</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.471</span>), <span style="font-style: italic;">Kaistella palustris</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.435</span>), <span style="font-style: italic;">Kingella potus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.435</span>), and <span style="font-style: italic;">Kocuria palustris</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.435</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"S. aureus"</span> -> <span style="font-weight: bold; font-style: italic;">Staphylococcus aureus</span> (B_STPHY_AURS, <span style="color: #080808; background-color: #FFFF87;">0.690</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Staphylococcus aureus aureus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.643</span>), <span style="font-style: italic;">Staphylococcus argenteus</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus aureus anaerobius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus auricularis</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.615</span>), <span style="font-style: italic;">Salmonella</span> Aurelianis<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>), <span style="font-style: italic;">Salmonella</span> Aarhus<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.588</span>), <span style="font-style: italic;">Salmonella</span></span></span>
|
||||
<span><span class="co">#> Amounderness<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), <span style="font-style: italic;">Staphylococcus argensis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), <span style="font-style: italic;">Streptococcus australis</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), and <span style="font-style: italic;">Salmonella choleraesuis arizonae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.562</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus aureus anaerobius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Streptomyces aureus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.618</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Staphylococcus auricularis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.615</span>), <span style="font-style: italic;">Streptomyces azureus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.609</span>), <span style="font-style: italic;">Salmonella</span></span></span>
|
||||
<span><span class="co">#> Aurelianis<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>), <span style="font-style: italic;">Salmonella</span> Aarhus<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.588</span>), <span style="font-style: italic;">Salmonella</span> Amounderness<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>),</span></span>
|
||||
<span><span class="co">#> and <span style="font-style: italic;">Staphylococcus argensis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"S. pneumoniae"</span> -> <span style="font-weight: bold; font-style: italic;">Streptococcus pneumoniae</span> (B_STRPT_PNMN, <span style="color: #080808; background-color: #5FD7AF;">0.750</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Streptococcus pseudopneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.700</span>), <span style="font-style: italic;">Streptococcus phocae</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">salmonis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.552</span>), <span style="font-style: italic;">Serratia proteamaculans quinovora</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.545</span>), <span style="font-style: italic;">Streptococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pseudoporcinus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.536</span>), <span style="font-style: italic;">Staphylococcus piscifermentans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.533</span>), <span style="font-style: italic;">Staphylococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pseudintermedius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.532</span>), <span style="font-style: italic;">Serratia proteamaculans proteamaculans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.526</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Streptococcus gallolyticus pasteurianus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.526</span>), <span style="font-style: italic;">Salmonella</span> Portanigra<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.524</span>),</span></span>
|
||||
<span><span class="co">#> and <span style="font-style: italic;">Streptococcus periodonticum</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.519</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Streptococcus parapneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.714</span>), <span style="font-style: italic;">Streptococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pseudopneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.700</span>), <span style="font-style: italic;">Serratia proteamaculans quinivorans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.557</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Streptococcus phocae salmonis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.552</span>), <span style="font-style: italic;">Serratia proteamaculans quinovora</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.545</span>), <span style="font-style: italic;">Sphingomonas piscinae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.538</span>), <span style="font-style: italic;">Streptococcus pseudoporcinus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.536</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Staphylococcus piscifermentans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.533</span>), <span style="font-style: italic;">Staphylococcus pseudintermedius</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.532</span>), and <span style="font-style: italic;">Serratia proteamaculans proteamaculans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.526</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Only the first 10 other matches of each record are shown. Run ``</span></span>
|
||||
<span><span class="co">#> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save</span></span>
|
||||
<span><span class="co">#> `mo_uncertainties()` to an object.</span></span></code></pre></div>
|
||||
@@ -633,36 +632,46 @@ in:</p>
|
||||
<div class="section level3">
|
||||
<h3 id="generate-antibiograms">Generate antibiograms<a class="anchor" aria-label="anchor" href="#generate-antibiograms"></a>
|
||||
</h3>
|
||||
<p>Since AMR v2.0 (March 2023), it is very easy to create different
|
||||
types of antibiograms, with support for 20 different languages.</p>
|
||||
<p>There are four antibiogram types, as proposed by Klinker <em>et
|
||||
<p>The <code>AMR</code> package supports 28 different languages for
|
||||
antibiograms and provides four types, as proposed by Klinker <em>et
|
||||
al.</em> (2021, <a href="https://doi.org/10.1177/20499361211011373" class="external-link">DOI
|
||||
10.1177/20499361211011373</a>), and they are all supported by the new
|
||||
<code><a href="../reference/antibiogram.html">antibiogram()</a></code> function:</p>
|
||||
10.1177/20499361211011373</a>):</p>
|
||||
<ol style="list-style-type: decimal">
|
||||
<li>
|
||||
<strong>Traditional Antibiogram (TA)</strong> e.g, for the
|
||||
susceptibility of <em>Pseudomonas aeruginosa</em> to
|
||||
piperacillin/tazobactam (TZP)</li>
|
||||
<strong>Traditional Antibiogram (TA)</strong> – susceptibility of a
|
||||
species to individual antibiotics</li>
|
||||
<li>
|
||||
<strong>Combination Antibiogram (CA)</strong> e.g, for the
|
||||
sdditional susceptibility of <em>Pseudomonas aeruginosa</em> to TZP +
|
||||
tobramycin versus TZP alone</li>
|
||||
<strong>Combination Antibiogram (CA)</strong> – susceptibility of a
|
||||
species to combination regimens</li>
|
||||
<li>
|
||||
<strong>Syndromic Antibiogram (SA)</strong> e.g, for the
|
||||
susceptibility of <em>Pseudomonas aeruginosa</em> to TZP among
|
||||
respiratory specimens (obtained among ICU patients only)</li>
|
||||
<strong>Syndromic Antibiogram (SA)</strong> – susceptibility of a
|
||||
species, stratified by clinical syndrome or setting</li>
|
||||
<li>
|
||||
<strong>Weighted-Incidence Syndromic Combination Antibiogram
|
||||
(WISCA)</strong> e.g, for the susceptibility of <em>Pseudomonas
|
||||
aeruginosa</em> to TZP among respiratory specimens (obtained among ICU
|
||||
patients only) for male patients age >=65 years with heart
|
||||
failure</li>
|
||||
(WISCA)</strong> – estimated empirical coverage of a <em>regimen</em>
|
||||
for a <em>syndrome</em>, weighted by pathogen incidence and with
|
||||
quantified uncertainty</li>
|
||||
</ol>
|
||||
<p>In this section, we show how to use the <code><a href="../reference/antibiogram.html">antibiogram()</a></code>
|
||||
function to create any of the above antibiogram types. For starters,
|
||||
this is what the included <code>example_isolates</code> data set looks
|
||||
like:</p>
|
||||
<p><strong>If your goal is to guide empirical therapy, WISCA should be
|
||||
your default.</strong> The reason is simple: when you start empirical
|
||||
treatment, you do not know which pathogen is causing the infection. Your
|
||||
next patient will not present with a species label attached to them.
|
||||
What matters is the probability that the <em>regimen</em> you choose
|
||||
will cover <em>whatever pathogen turns out to be the cause</em>, given
|
||||
the local epidemiology of the syndrome. Traditional antibiograms do not
|
||||
answer that question. They fragment information by species, ignore how
|
||||
frequently each species causes the syndrome, do not evaluate combination
|
||||
regimens, and provide no measure of uncertainty. WISCA addresses all of
|
||||
these limitations using a Bayesian framework (Hebert <em>et al.</em>,
|
||||
2012; Bielicki <em>et al.</em>, 2016). See the <a href="https://amr-for-r.org/articles/WISCA.html">WISCA vignette</a> for
|
||||
the full explanation.</p>
|
||||
<p>Traditional, combination, and syndromic antibiograms remain useful
|
||||
for <strong>surveillance</strong> purposes, i.e., tracking resistance
|
||||
trends per species over time. But if you care about clinical impact,
|
||||
about choosing the right empirical regimen for your patient, use
|
||||
WISCA.</p>
|
||||
<p>For starters, this is what the included <code>example_isolates</code>
|
||||
data set looks like:</p>
|
||||
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">example_isolates</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
|
||||
@@ -686,13 +695,162 @@ like:</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …</span></span></span></code></pre></div>
|
||||
<div class="section level4">
|
||||
<h4 id="wisca-recommended-for-empirical-therapy-guidance">WISCA (recommended for empirical therapy guidance)<a class="anchor" aria-label="anchor" href="#wisca-recommended-for-empirical-therapy-guidance"></a>
|
||||
</h4>
|
||||
<p>Use the <code><a href="../reference/antibiogram.html">wisca()</a></code> function, or equivalently
|
||||
<code>antibiogram(..., wisca = TRUE)</code>. WISCA produces a single
|
||||
coverage estimate per regimen for the entire syndrome, weighted by
|
||||
pathogen incidence, with a 95% credible interval from Bayesian Monte
|
||||
Carlo simulation:</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">wisca_result</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span></span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
|
||||
<span> minimum <span class="op">=</span> <span class="fl">10</span></span>
|
||||
<span> <span class="op">)</span> <span class="co"># Recommended threshold: ≥30</span></span>
|
||||
<span><span class="va">wisca_result</span></span></code></pre></div>
|
||||
<table style="width:100%;" class="table">
|
||||
<colgroup>
|
||||
<col width="24%">
|
||||
<col width="37%">
|
||||
<col width="37%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">Piperacillin/tazobactam</th>
|
||||
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
|
||||
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">70.2% (64.8-75.2%)</td>
|
||||
<td align="left">93.6% (92.2-95%)</td>
|
||||
<td align="left">89.9% (87-92.3%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
<p>The output tells you: <em>“given the species distribution in your
|
||||
data, there is an estimated X% probability that this regimen covers the
|
||||
infection, with 95% credible interval [lower, upper]”</em>. That is the
|
||||
clinically relevant question.</p>
|
||||
<p>For <strong>syndrome-specific</strong> or <strong>patient-specific
|
||||
WISCA</strong>, use the <code>syndromic_group</code> argument or group
|
||||
your data first. You can stratify by anything: ward, age group, risk
|
||||
profile, acquisition type. The <code>syndromic_group</code> argument
|
||||
accepts any column or expression:</p>
|
||||
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">wisca_out</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
|
||||
<span> age_group <span class="op">=</span> <span class="fu"><a href="../reference/age_groups.html">age_groups</a></span><span class="op">(</span><span class="va">age</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">25</span>, <span class="fl">50</span>, <span class="fl">75</span><span class="op">)</span><span class="op">)</span>,</span>
|
||||
<span> <span class="va">gender</span></span>
|
||||
<span> <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span>antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="va">wisca_out</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="8%">
|
||||
<col width="6%">
|
||||
<col width="20%">
|
||||
<col width="32%">
|
||||
<col width="32%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">age_group</th>
|
||||
<th align="left">gender</th>
|
||||
<th align="left">Piperacillin/tazobactam</th>
|
||||
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
|
||||
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">0-24</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">56.8% (29.9-81.3%)</td>
|
||||
<td align="left">70.7% (45.2-89%)</td>
|
||||
<td align="left">65.9% (42.3-86.6%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">0-24</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">59.5% (31.2-85.5%)</td>
|
||||
<td align="left">76.1% (56.5-92%)</td>
|
||||
<td align="left">59.6% (31.7-85.1%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">25-49</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">67.7% (43.9-89.7%)</td>
|
||||
<td align="left">93.8% (87.4-98.1%)</td>
|
||||
<td align="left">87% (70.1-97%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">25-49</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">56.9% (26.6-86.2%)</td>
|
||||
<td align="left">91% (82-97.2%)</td>
|
||||
<td align="left">76.6% (51.4-93.5%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">50-74</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">68% (54.1-81.8%)</td>
|
||||
<td align="left">96.9% (94.6-98.5%)</td>
|
||||
<td align="left">90.2% (82-96.2%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">50-74</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">67% (56-78.5%)</td>
|
||||
<td align="left">96.7% (94.1-98.5%)</td>
|
||||
<td align="left">86.7% (77.3-94.4%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">75+</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">73.1% (61.8-84.1%)</td>
|
||||
<td align="left">97.7% (95.9-99%)</td>
|
||||
<td align="left">92.8% (85.7-97.2%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">75+</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">74% (63.6-82.6%)</td>
|
||||
<td align="left">97.9% (96-99%)</td>
|
||||
<td align="left">94.7% (89.3-97.9%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>Keep in mind that more granular stratification produces more relevant
|
||||
estimates for each subgroup, but with wider credible intervals due to
|
||||
smaller sample sizes. There is always a trade-off between granularity
|
||||
and precision. If local numbers are small, consider pooling data from
|
||||
multiple sites (Bielicki <em>et al.</em>, 2016).</p>
|
||||
<p>For reliable WISCA results, ensure your data includes <strong>only
|
||||
first isolates</strong> (use <code><a href="../reference/first_isolate.html">first_isolate()</a></code>) and consider
|
||||
filtering for <strong>the top <em>n</em> species</strong> (use
|
||||
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>), since rare contaminants can
|
||||
distort coverage estimates.</p>
|
||||
<p>After creating the WISCA model, assessments can be done on the
|
||||
distributions of the Monte Carlo simulations that WISCA carried out:</p>
|
||||
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/wisca_plots-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span>, wisca_plot_type <span class="op">=</span> <span class="st">"posterior_coverage"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/wisca_plots-2.png" class="r-plt" alt="" width="720"></p>
|
||||
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span></span>
|
||||
<span><span class="co"># a ggplot2 extension for WISCAs and other antibiograms:</span></span>
|
||||
<span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/wisca_plots-3.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="traditional-antibiogram">Traditional Antibiogram<a class="anchor" aria-label="anchor" href="#traditional-antibiogram"></a>
|
||||
</h4>
|
||||
<p>To create a traditional antibiogram, simply state which antibiotics
|
||||
should be used. The <code>antibiotics</code> argument in the
|
||||
<code><a href="../reference/antibiogram.html">antibiogram()</a></code> function supports any (combination) of the
|
||||
previously mentioned antibiotic class selectors:</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
|
||||
<p>If you need per-species susceptibility rates, e.g., for AMR
|
||||
surveillance reports, the traditional antibiogram remains the right
|
||||
tool. It reports the proportion of susceptible isolates per species per
|
||||
antibiotic:</p>
|
||||
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span>
|
||||
@@ -821,7 +979,7 @@ Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish,
|
||||
Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish,
|
||||
Ukrainian, Urdu, or Vietnamese. In this next example, we force the
|
||||
language to be Spanish using the <code>language</code> argument:</p>
|
||||
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span>
|
||||
@@ -864,11 +1022,13 @@ language to be Spanish using the <code>language</code> argument:</p>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="combined-antibiogram">Combined Antibiogram<a class="anchor" aria-label="anchor" href="#combined-antibiogram"></a>
|
||||
<h4 id="combination-antibiogram">Combination Antibiogram<a class="anchor" aria-label="anchor" href="#combination-antibiogram"></a>
|
||||
</h4>
|
||||
<p>To create a combined antibiogram, use antibiotic codes or names with
|
||||
a plus <code>+</code> character like this:</p>
|
||||
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
|
||||
<p>A combination antibiogram shows how much additional susceptibility a
|
||||
second agent adds for a given species. This is useful for surveillance
|
||||
of combination regimens, but note that it is still species-stratified
|
||||
and does not account for pathogen incidence in the syndrome:</p>
|
||||
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">combined_ab</span> <span class="op"><-</span> <span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
|
||||
<span> ab_transform <span class="op">=</span> <span class="cn">NULL</span></span>
|
||||
@@ -948,10 +1108,13 @@ a plus <code>+</code> character like this:</p>
|
||||
<div class="section level4">
|
||||
<h4 id="syndromic-antibiogram">Syndromic Antibiogram<a class="anchor" aria-label="anchor" href="#syndromic-antibiogram"></a>
|
||||
</h4>
|
||||
<p>To create a syndromic antibiogram, the <code>syndromic_group</code>
|
||||
argument must be used. This can be any column in the data, or e.g. an
|
||||
<code><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse()</a></code> with calculations based on certain columns:</p>
|
||||
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
|
||||
<p>A syndromic antibiogram stratifies per-species susceptibility by
|
||||
clinical context (ward, specimen type, etc.). It adds clinical context
|
||||
to the traditional antibiogram but is still species-level, without
|
||||
incidence weighting or uncertainty quantification. For surveillance by
|
||||
setting this is fine; for empirical therapy guidance, WISCA is
|
||||
preferred:</p>
|
||||
<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span>
|
||||
@@ -1125,511 +1288,14 @@ argument must be used. This can be any column in the data, or e.g. an
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="weighted-incidence-syndromic-combination-antibiogram-wisca">Weighted-Incidence Syndromic Combination Antibiogram (WISCA)<a class="anchor" aria-label="anchor" href="#weighted-incidence-syndromic-combination-antibiogram-wisca"></a>
|
||||
</h4>
|
||||
<p>To create a <strong>Weighted-Incidence Syndromic Combination
|
||||
Antibiogram (WISCA)</strong>, simply set <code>wisca = TRUE</code> in
|
||||
the <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function, or use the dedicated
|
||||
<code><a href="../reference/antibiogram.html">wisca()</a></code> function. Unlike traditional antibiograms, WISCA
|
||||
provides syndrome-based susceptibility estimates, weighted by pathogen
|
||||
incidence and antimicrobial susceptibility patterns.</p>
|
||||
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span></span>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
|
||||
<span> minimum <span class="op">=</span> <span class="fl">10</span></span>
|
||||
<span> <span class="op">)</span> <span class="co"># Recommended threshold: ≥30</span></span></code></pre></div>
|
||||
<table style="width:100%;" class="table">
|
||||
<colgroup>
|
||||
<col width="24%">
|
||||
<col width="37%">
|
||||
<col width="37%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">Piperacillin/tazobactam</th>
|
||||
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
|
||||
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">69.4% (64.3-74.3%)</td>
|
||||
<td align="left">92.6% (91.1-93.9%)</td>
|
||||
<td align="left">88.7% (85.8-91.2%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
<p>WISCA uses a <strong>Bayesian decision model</strong> to integrate
|
||||
data from multiple pathogens, improving empirical therapy guidance,
|
||||
especially for low-incidence infections. It is
|
||||
<strong>pathogen-agnostic</strong>, meaning results are syndrome-based
|
||||
rather than stratified by microorganism.</p>
|
||||
<p>For reliable results, ensure your data includes <strong>only first
|
||||
isolates</strong> (use <code><a href="../reference/first_isolate.html">first_isolate()</a></code>) and consider
|
||||
filtering for <strong>the top <em>n</em> species</strong> (use
|
||||
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>), as WISCA outcomes are most
|
||||
meaningful when based on robust incidence estimates.</p>
|
||||
<p>For <strong>patient- or syndrome-specific WISCA</strong>, run the
|
||||
function on a grouped <code>tibble</code>, i.e., using
|
||||
<code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> first:</p>
|
||||
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
|
||||
<span> age_group <span class="op">=</span> <span class="fu"><a href="../reference/age_groups.html">age_groups</a></span><span class="op">(</span><span class="va">age</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">25</span>, <span class="fl">50</span>, <span class="fl">75</span><span class="op">)</span><span class="op">)</span>,</span>
|
||||
<span> <span class="va">gender</span></span>
|
||||
<span> <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span>antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
|
||||
<table style="width:100%;" class="table">
|
||||
<colgroup>
|
||||
<col width="1%">
|
||||
<col width="0%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
<col width="2%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">age_group</th>
|
||||
<th align="left">gender</th>
|
||||
<th align="left">Amikacin</th>
|
||||
<th align="left">Amoxicillin</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||||
<th align="left">Ampicillin</th>
|
||||
<th align="left">Azithromycin</th>
|
||||
<th align="left">Benzylpenicillin</th>
|
||||
<th align="left">Cefazolin</th>
|
||||
<th align="left">Cefepime</th>
|
||||
<th align="left">Cefotaxime</th>
|
||||
<th align="left">Cefoxitin</th>
|
||||
<th align="left">Ceftazidime</th>
|
||||
<th align="left">Ceftriaxone</th>
|
||||
<th align="left">Cefuroxime</th>
|
||||
<th align="left">Chloramphenicol</th>
|
||||
<th align="left">Ciprofloxacin</th>
|
||||
<th align="left">Clindamycin</th>
|
||||
<th align="left">Colistin</th>
|
||||
<th align="left">Doxycycline</th>
|
||||
<th align="left">Erythromycin</th>
|
||||
<th align="left">Flucloxacillin</th>
|
||||
<th align="left">Fosfomycin</th>
|
||||
<th align="left">Gentamicin</th>
|
||||
<th align="left">Imipenem</th>
|
||||
<th align="left">Kanamycin</th>
|
||||
<th align="left">Linezolid</th>
|
||||
<th align="left">Meropenem</th>
|
||||
<th align="left">Metronidazole</th>
|
||||
<th align="left">Moxifloxacin</th>
|
||||
<th align="left">Mupirocin</th>
|
||||
<th align="left">Nitrofurantoin</th>
|
||||
<th align="left">Oxacillin</th>
|
||||
<th align="left">Piperacillin/tazobactam</th>
|
||||
<th align="left">Rifampicin</th>
|
||||
<th align="left">Teicoplanin</th>
|
||||
<th align="left">Tetracycline</th>
|
||||
<th align="left">Tigecycline</th>
|
||||
<th align="left">Tobramycin</th>
|
||||
<th align="left">Trimethoprim</th>
|
||||
<th align="left">Trimethoprim/sulfamethoxazole</th>
|
||||
<th align="left">Vancomycin</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">0-24</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">45.4% (15.4-79%)</td>
|
||||
<td align="left">50.1% (20.5-77.6%)</td>
|
||||
<td align="left">69% (44.5-88.5%)</td>
|
||||
<td align="left">50.4% (20.6-77.3%)</td>
|
||||
<td align="left">41.9% (18.1-65.6%)</td>
|
||||
<td align="left">36.1% (12.3-64.3%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">63.9% (34.6-87.6%)</td>
|
||||
<td align="left">56.7% (25.9-85.8%)</td>
|
||||
<td align="left">51.5% (25.6-74.4%)</td>
|
||||
<td align="left">63.4% (32-88.1%)</td>
|
||||
<td align="left">70.4% (45.4-89.1%)</td>
|
||||
<td align="left">54% (22.3-85.3%)</td>
|
||||
<td align="left">69.8% (45.9-88.9%)</td>
|
||||
<td align="left">39.3% (17.7-64.6%)</td>
|
||||
<td align="left">45.3% (18.1-75.9%)</td>
|
||||
<td align="left">50.1% (21.5-80.5%)</td>
|
||||
<td align="left">41.7% (19.1-67.6%)</td>
|
||||
<td align="left">55.8% (23.7-83.3%)</td>
|
||||
<td align="left">63.5% (32.6-89.4%)</td>
|
||||
<td align="left">69.3% (44.6-88.3%)</td>
|
||||
<td align="left">63.6% (36.1-88.2%)</td>
|
||||
<td align="left">45.5% (15.7-77.7%)</td>
|
||||
<td align="left">43.3% (17.8-71.2%)</td>
|
||||
<td align="left">55.9% (24.3-82.2%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">56.5% (24.3-85%)</td>
|
||||
<td align="left">56.8% (30.9-82.3%)</td>
|
||||
<td align="left">50.5% (19.4-80.8%)</td>
|
||||
<td align="left">56.9% (26.7-85%)</td>
|
||||
<td align="left">42.3% (18.3-68.8%)</td>
|
||||
<td align="left">40.2% (17.6-67.7%)</td>
|
||||
<td align="left">49.8% (20-79.3%)</td>
|
||||
<td align="left">56.1% (22-85.4%)</td>
|
||||
<td align="left">64.5% (39.6-85.5%)</td>
|
||||
<td align="left">69.7% (42.3-90.4%)</td>
|
||||
<td align="left">75.4% (52.1-91.7%)</td>
|
||||
<td align="left">48.5% (24.3-72.6%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">0-24</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">41.9% (15.2-72.5%)</td>
|
||||
<td align="left">49.4% (23.3-75.5%)</td>
|
||||
<td align="left">73.8% (51.8-90.1%)</td>
|
||||
<td align="left">49.3% (22.7-76%)</td>
|
||||
<td align="left">63.4% (40.7-83.5%)</td>
|
||||
<td align="left">41.8% (20.4-64.8%)</td>
|
||||
<td align="left">56.8% (25.2-83.5%)</td>
|
||||
<td align="left">58.2% (29.1-85.8%)</td>
|
||||
<td align="left">59.7% (29.1-87.4%)</td>
|
||||
<td align="left">59.3% (29.1-86.6%)</td>
|
||||
<td align="left">24.9% (8.9-47.3%)</td>
|
||||
<td align="left">58.5% (28-86.5%)</td>
|
||||
<td align="left">72.1% (47.9-90.5%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">77.2% (53-93.2%)</td>
|
||||
<td align="left">61.6% (36.2-83.6%)</td>
|
||||
<td align="left">25.5% (8.7-46.1%)</td>
|
||||
<td align="left">69.4% (44.6-89.4%)</td>
|
||||
<td align="left">63.4% (41.8-82.7%)</td>
|
||||
<td align="left">64% (37.6-85.6%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">63.5% (40.9-83.1%)</td>
|
||||
<td align="left">58.7% (27.6-86.5%)</td>
|
||||
<td align="left">41.8% (13.6-71.2%)</td>
|
||||
<td align="left">48.3% (17.9-78%)</td>
|
||||
<td align="left">59.2% (27.4-86.4%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">53% (21.3-83.7%)</td>
|
||||
<td align="left">57.2% (24.6-84.6%)</td>
|
||||
<td align="left">59.9% (29.7-85.6%)</td>
|
||||
<td align="left">48.2% (16.1-80.4%)</td>
|
||||
<td align="left">48.4% (17.4-79.8%)</td>
|
||||
<td align="left">68% (43.5-87.3%)</td>
|
||||
<td align="left">65.7% (36-89.2%)</td>
|
||||
<td align="left">44.3% (17.2-73.4%)</td>
|
||||
<td align="left">69.4% (46.9-87.8%)</td>
|
||||
<td align="left">74% (50.8-90.9%)</td>
|
||||
<td align="left">75.3% (52.4-92.2%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">25-49</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">46.8% (26.7-65.6%)</td>
|
||||
<td align="left">39% (26.3-52.9%)</td>
|
||||
<td align="left">73.8% (63.5-82.6%)</td>
|
||||
<td align="left">39.3% (27.2-54.4%)</td>
|
||||
<td align="left">54.8% (44.9-64.8%)</td>
|
||||
<td align="left">36.5% (26.3-47.1%)</td>
|
||||
<td align="left">66.4% (46.1-85%)</td>
|
||||
<td align="left">69.2% (49.1-86.2%)</td>
|
||||
<td align="left">70.2% (50.5-86.2%)</td>
|
||||
<td align="left">68.1% (48.6-85.4%)</td>
|
||||
<td align="left">27.9% (19.2-37.9%)</td>
|
||||
<td align="left">70.1% (50.5-87.1%)</td>
|
||||
<td align="left">71.4% (61.7-80.4%)</td>
|
||||
<td align="left">58.2% (35.3-79.9%)</td>
|
||||
<td align="left">85.5% (74.1-94.2%)</td>
|
||||
<td align="left">67.1% (55.8-77.4%)</td>
|
||||
<td align="left">25.8% (17-36%)</td>
|
||||
<td align="left">75.5% (61.2-88.2%)</td>
|
||||
<td align="left">54.9% (44.8-65.6%)</td>
|
||||
<td align="left">55.2% (37.8-72.5%)</td>
|
||||
<td align="left">60.9% (38.3-81.8%)</td>
|
||||
<td align="left">75.2% (65.7-83.5%)</td>
|
||||
<td align="left">69.9% (50.1-86.6%)</td>
|
||||
<td align="left">37.5% (17.7-57.9%)</td>
|
||||
<td align="left">50.7% (30.8-68.7%)</td>
|
||||
<td align="left">69.4% (48.6-86.7%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">56.9% (36.2-77.7%)</td>
|
||||
<td align="left">53.3% (30.9-75.6%)</td>
|
||||
<td align="left">60.1% (38.1-81.8%)</td>
|
||||
<td align="left">64.3% (43.2-83.6%)</td>
|
||||
<td align="left">66% (45.7-85.2%)</td>
|
||||
<td align="left">50.1% (30-69.6%)</td>
|
||||
<td align="left">38.8% (19.6-58.9%)</td>
|
||||
<td align="left">75.8% (61.6-88.1%)</td>
|
||||
<td align="left">73.3% (56.6-89.5%)</td>
|
||||
<td align="left">62.7% (47.6-77.1%)</td>
|
||||
<td align="left">70.4% (58.7-80.2%)</td>
|
||||
<td align="left">90% (82.9-95.4%)</td>
|
||||
<td align="left">71.6% (61.7-80.4%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">25-49</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">49.8% (24.2-75.8%)</td>
|
||||
<td align="left">16.5% (8.1-27.4%)</td>
|
||||
<td align="left">72.4% (60.5-83.5%)</td>
|
||||
<td align="left">16.6% (7.7-28.2%)</td>
|
||||
<td align="left">55.9% (43.6-67.9%)</td>
|
||||
<td align="left">24.9% (14.7-37.6%)</td>
|
||||
<td align="left">60.3% (33.2-82.4%)</td>
|
||||
<td align="left">55.3% (27.6-81.8%)</td>
|
||||
<td align="left">55.9% (29.7-81.1%)</td>
|
||||
<td align="left">56.2% (27.7-82.1%)</td>
|
||||
<td align="left">22.2% (12.7-33.9%)</td>
|
||||
<td align="left">55.6% (29.1-81.8%)</td>
|
||||
<td align="left">73.7% (62.6-83.8%)</td>
|
||||
<td align="left">52.9% (25.2-79.6%)</td>
|
||||
<td align="left">67.1% (53-79.8%)</td>
|
||||
<td align="left">57.8% (43.5-71.8%)</td>
|
||||
<td align="left">22.3% (12.6-33.6%)</td>
|
||||
<td align="left">73% (57.8-85.5%)</td>
|
||||
<td align="left">55.8% (43-68.2%)</td>
|
||||
<td align="left">66.5% (51.6-79.4%)</td>
|
||||
<td align="left">63.1% (40.3-84.5%)</td>
|
||||
<td align="left">83.9% (74.5-91.7%)</td>
|
||||
<td align="left">56.4% (28.4-84%)</td>
|
||||
<td align="left">45.4% (18.9-73.8%)</td>
|
||||
<td align="left">59.4% (37.6-77.9%)</td>
|
||||
<td align="left">56.3% (28.8-81.1%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">52.8% (24.7-78.7%)</td>
|
||||
<td align="left">64.2% (40.2-84.5%)</td>
|
||||
<td align="left">62.9% (37.7-85.1%)</td>
|
||||
<td align="left">60.5% (37.1-80.7%)</td>
|
||||
<td align="left">55.8% (29.4-82.9%)</td>
|
||||
<td align="left">65.4% (48.7-80.8%)</td>
|
||||
<td align="left">54.5% (31.7-73.7%)</td>
|
||||
<td align="left">72.8% (58.7-84.8%)</td>
|
||||
<td align="left">84.8% (72.4-93.6%)</td>
|
||||
<td align="left">66.7% (44.5-84.1%)</td>
|
||||
<td align="left">71.4% (58.9-82.6%)</td>
|
||||
<td align="left">86.6% (77.9-93.7%)</td>
|
||||
<td align="left">77.1% (65.5-87.1%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">50-74</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">44.8% (35.8-54.1%)</td>
|
||||
<td align="left">30.1% (24.9-35.3%)</td>
|
||||
<td align="left">74.1% (69.2-78.7%)</td>
|
||||
<td align="left">30% (24.6-35.4%)</td>
|
||||
<td align="left">41.9% (36.5-47.3%)</td>
|
||||
<td align="left">23.5% (18.6-29%)</td>
|
||||
<td align="left">73.1% (62-82.9%)</td>
|
||||
<td align="left">76.6% (66.1-86%)</td>
|
||||
<td align="left">74.8% (64.9-84.5%)</td>
|
||||
<td align="left">74.6% (64.2-83.3%)</td>
|
||||
<td align="left">37.5% (32.3-43.4%)</td>
|
||||
<td align="left">74.8% (64.4-83.8%)</td>
|
||||
<td align="left">74.5% (69.7-78.9%)</td>
|
||||
<td align="left">61.2% (40.3-82.4%)</td>
|
||||
<td align="left">79.4% (73-85%)</td>
|
||||
<td align="left">44.9% (38.7-51%)</td>
|
||||
<td align="left">37.8% (32.7-43.3%)</td>
|
||||
<td align="left">63.8% (47.6-80.1%)</td>
|
||||
<td align="left">41.7% (36.6-46.9%)</td>
|
||||
<td align="left">58.1% (40-75.1%)</td>
|
||||
<td align="left">65.2% (53.5-76.6%)</td>
|
||||
<td align="left">78.7% (73.8-83.2%)</td>
|
||||
<td align="left">80.6% (70.3-90%)</td>
|
||||
<td align="left">28.1% (10.1-46.6%)</td>
|
||||
<td align="left">53.2% (42.9-62.4%)</td>
|
||||
<td align="left">79.3% (68.7-88.6%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">49.5% (37.5-61.8%)</td>
|
||||
<td align="left">67.8% (48.5-86%)</td>
|
||||
<td align="left">75.1% (63.3-86.3%)</td>
|
||||
<td align="left">56.6% (37.8-74.2%)</td>
|
||||
<td align="left">67.7% (56.4-79.6%)</td>
|
||||
<td align="left">50.6% (40.9-59.1%)</td>
|
||||
<td align="left">41.3% (31.5-50.4%)</td>
|
||||
<td align="left">59% (48.3-74.5%)</td>
|
||||
<td align="left">87.7% (80.4-94.1%)</td>
|
||||
<td align="left">62.2% (55.4-68.4%)</td>
|
||||
<td align="left">55.5% (49.8-61.1%)</td>
|
||||
<td align="left">68% (62.7-73.3%)</td>
|
||||
<td align="left">60.9% (55.8-66.1%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">50-74</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">38.8% (30.6-48.6%)</td>
|
||||
<td align="left">34.6% (29.1-40.3%)</td>
|
||||
<td align="left">75% (70-79.5%)</td>
|
||||
<td align="left">34.7% (29.2-40.5%)</td>
|
||||
<td align="left">43.4% (37.8-48.5%)</td>
|
||||
<td align="left">21% (16.5-26.4%)</td>
|
||||
<td align="left">64.3% (54.1-74.1%)</td>
|
||||
<td align="left">65.9% (56.5-75.4%)</td>
|
||||
<td align="left">67.3% (58.3-77%)</td>
|
||||
<td align="left">65.9% (56.1-75.9%)</td>
|
||||
<td align="left">32.9% (27.6-38%)</td>
|
||||
<td align="left">67.3% (57.4-76.8%)</td>
|
||||
<td align="left">74.1% (69.2-78.8%)</td>
|
||||
<td align="left">63.5% (42.4-83%)</td>
|
||||
<td align="left">76.9% (71.6-81.9%)</td>
|
||||
<td align="left">47.3% (40.9-53.8%)</td>
|
||||
<td align="left">30.8% (26.1-36.1%)</td>
|
||||
<td align="left">68.5% (53.5-81.9%)</td>
|
||||
<td align="left">43.4% (37.7-48.8%)</td>
|
||||
<td align="left">58.1% (42.4-73.2%)</td>
|
||||
<td align="left">68.1% (53.5-82.2%)</td>
|
||||
<td align="left">79.1% (74.4-83.1%)</td>
|
||||
<td align="left">69% (59.7-78.3%)</td>
|
||||
<td align="left">24.8% (9.5-40.5%)</td>
|
||||
<td align="left">49.7% (35-63.2%)</td>
|
||||
<td align="left">68.1% (58.1-77.6%)</td>
|
||||
<td align="left">53.8% (32-75%)</td>
|
||||
<td align="left">51.7% (36.1-67.3%)</td>
|
||||
<td align="left">68.8% (51.1-85.7%)</td>
|
||||
<td align="left">70.2% (54.7-85.3%)</td>
|
||||
<td align="left">53.2% (37.5-68.7%)</td>
|
||||
<td align="left">66.5% (55-76.8%)</td>
|
||||
<td align="left">56.2% (45.8-65.4%)</td>
|
||||
<td align="left">44% (30.3-57.5%)</td>
|
||||
<td align="left">71.9% (58.2-82.2%)</td>
|
||||
<td align="left">86.8% (77.3-93.6%)</td>
|
||||
<td align="left">54.1% (46.9-61.4%)</td>
|
||||
<td align="left">67.1% (61.5-72.5%)</td>
|
||||
<td align="left">81% (76.4-85.2%)</td>
|
||||
<td align="left">66.3% (61-71.2%)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">75+</td>
|
||||
<td align="left">F</td>
|
||||
<td align="left">51.4% (41.7-62%)</td>
|
||||
<td align="left">30.9% (26.2-36.5%)</td>
|
||||
<td align="left">74.4% (70.3-78.6%)</td>
|
||||
<td align="left">30.9% (25.7-36.1%)</td>
|
||||
<td align="left">36.6% (32-41.6%)</td>
|
||||
<td align="left">20.7% (16.2-25.4%)</td>
|
||||
<td align="left">73.6% (63.6-82.5%)</td>
|
||||
<td align="left">79.1% (70.6-86.8%)</td>
|
||||
<td align="left">78.6% (69.9-86.3%)</td>
|
||||
<td align="left">76% (67.5-83.7%)</td>
|
||||
<td align="left">43.1% (38.6-48%)</td>
|
||||
<td align="left">78.9% (70.5-86.4%)</td>
|
||||
<td align="left">77% (72.6-81.3%)</td>
|
||||
<td align="left">63.2% (43.2-84.1%)</td>
|
||||
<td align="left">77.7% (72.1-83.2%)</td>
|
||||
<td align="left">41.2% (36-46.4%)</td>
|
||||
<td align="left">39.1% (34.2-44.4%)</td>
|
||||
<td align="left">63.7% (46.3-80.6%)</td>
|
||||
<td align="left">36.5% (31.9-41.2%)</td>
|
||||
<td align="left">57.1% (39.8-76%)</td>
|
||||
<td align="left">65.8% (57.2-73.5%)</td>
|
||||
<td align="left">84.6% (80.6-88%)</td>
|
||||
<td align="left">81.9% (73.7-89.5%)</td>
|
||||
<td align="left">33.3% (13.7-53%)</td>
|
||||
<td align="left">49.6% (42.3-56.1%)</td>
|
||||
<td align="left">81.3% (73.2-88.9%)</td>
|
||||
<td align="left">55.9% (33.5-76.5%)</td>
|
||||
<td align="left">41% (31.4-51.8%)</td>
|
||||
<td align="left">63.7% (43.8-82.3%)</td>
|
||||
<td align="left">77.8% (66-87.4%)</td>
|
||||
<td align="left">56.3% (37.3-75.1%)</td>
|
||||
<td align="left">71.8% (62-82.2%)</td>
|
||||
<td align="left">48.3% (41.5-54.9%)</td>
|
||||
<td align="left">43.3% (36.2-50.7%)</td>
|
||||
<td align="left">63% (45.3-80.3%)</td>
|
||||
<td align="left">85.9% (79.9-90.9%)</td>
|
||||
<td align="left">70.4% (64.1-76.8%)</td>
|
||||
<td align="left">60.4% (55.1-65.8%)</td>
|
||||
<td align="left">77.6% (73.4-82.1%)</td>
|
||||
<td align="left">55.3% (50.4-60.1%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">75+</td>
|
||||
<td align="left">M</td>
|
||||
<td align="left">52.6% (43.3-62.6%)</td>
|
||||
<td align="left">33% (28.1-38%)</td>
|
||||
<td align="left">77.4% (73.3-81.5%)</td>
|
||||
<td align="left">33% (28.2-38.2%)</td>
|
||||
<td align="left">36.8% (32.3-41.8%)</td>
|
||||
<td align="left">17.9% (12.6-23.2%)</td>
|
||||
<td align="left">64.4% (55.4-73.3%)</td>
|
||||
<td align="left">71.2% (63.1-79.1%)</td>
|
||||
<td align="left">67.9% (59.5-75.8%)</td>
|
||||
<td align="left">65.3% (56.3-73.6%)</td>
|
||||
<td align="left">42.6% (37.8-47.4%)</td>
|
||||
<td align="left">68.2% (59.7-76.4%)</td>
|
||||
<td align="left">75.1% (70.9-79.2%)</td>
|
||||
<td align="left">64.1% (45.8-81.8%)</td>
|
||||
<td align="left">77.6% (72-82.6%)</td>
|
||||
<td align="left">41% (36-46.4%)</td>
|
||||
<td align="left">39.9% (35.1-44.5%)</td>
|
||||
<td align="left">62.1% (46-78.8%)</td>
|
||||
<td align="left">36.9% (32.4-41.4%)</td>
|
||||
<td align="left">59.7% (43.4-76.6%)</td>
|
||||
<td align="left">64.7% (56.6-73.6%)</td>
|
||||
<td align="left">83% (79.4-86.7%)</td>
|
||||
<td align="left">75.7% (66.6-83%)</td>
|
||||
<td align="left">31.6% (12.1-51.7%)</td>
|
||||
<td align="left">51.8% (44.9-58%)</td>
|
||||
<td align="left">74.2% (65.8-82.7%)</td>
|
||||
<td align="left">NA</td>
|
||||
<td align="left">52.2% (41.5-60.8%)</td>
|
||||
<td align="left">69.3% (50.5-86.4%)</td>
|
||||
<td align="left">72.2% (58.7-83.4%)</td>
|
||||
<td align="left">59.3% (41.6-76.7%)</td>
|
||||
<td align="left">73.1% (64.3-81.4%)</td>
|
||||
<td align="left">49.9% (42.4-56.9%)</td>
|
||||
<td align="left">46.3% (38.2-53.1%)</td>
|
||||
<td align="left">59.7% (44.2-75.7%)</td>
|
||||
<td align="left">86.8% (81.4-91.3%)</td>
|
||||
<td align="left">72% (66.3-77.6%)</td>
|
||||
<td align="left">55.8% (50.3-61.1%)</td>
|
||||
<td align="left">73.3% (68.9-77.6%)</td>
|
||||
<td align="left">57% (52.2-61.6%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="plotting-antibiograms">Plotting antibiograms<a class="anchor" aria-label="anchor" href="#plotting-antibiograms"></a>
|
||||
</h4>
|
||||
<p>Antibiograms can be plotted using <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> from the
|
||||
<code>ggplot2</code> packages, since this <code>AMR</code> package
|
||||
provides an extension to that function:</p>
|
||||
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">combined_ab</span><span class="op">)</span></span></code></pre></div>
|
||||
<p>All antibiogram types, including WISCA, can be plotted using
|
||||
<code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> from the <code>ggplot2</code> package, since
|
||||
this <code>AMR</code> package provides an extension to that
|
||||
function:</p>
|
||||
<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_result</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<p>To calculate antimicrobial resistance in a more sensible way, also by
|
||||
correcting for too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
|
||||
@@ -1657,7 +1323,7 @@ proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a>
|
||||
I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to
|
||||
<code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their
|
||||
own:</p>
|
||||
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
|
||||
<span><span class="co">#> category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
|
||||
@@ -1666,7 +1332,7 @@ own:</p>
|
||||
<span><span class="co">#> [1] 0.4203377</span></span></code></pre></div>
|
||||
<p>Or can be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> and
|
||||
<code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
|
||||
<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">hospital</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>amoxicillin <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span><span class="op">)</span></span>
|
||||
@@ -1684,7 +1350,7 @@ own:</p>
|
||||
diameters can be interpreted into clinical breakpoints (SIR) using
|
||||
<code><a href="../reference/as.sir.html">as.sir()</a></code>. Here’s an example with randomly generated MIC
|
||||
values for <em>Klebsiella pneumoniae</em> and ciprofloxacin:</p>
|
||||
<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
|
||||
<span><span class="va">mic_values</span> <span class="op"><-</span> <span class="fu"><a href="../reference/random.html">random_mic</a></span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
|
||||
<span><span class="va">sir_values</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span>
|
||||
@@ -1715,7 +1381,7 @@ breakpoints, facilitating automated AMR data processing.</p>
|
||||
using <code>ggplot2</code>, using the new <code><a href="../reference/plot.html">scale_y_mic()</a></code> for
|
||||
the y-axis and <code><a href="../reference/plot.html">scale_colour_sir()</a></code> to colour-code SIR
|
||||
categories.</p>
|
||||
<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># add a group</span></span>
|
||||
<span><span class="va">my_data</span><span class="op">$</span><span class="va">group</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>, <span class="st">"D"</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">25</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
@@ -1739,16 +1405,16 @@ across different groups while incorporating clinical breakpoints.</p>
|
||||
<code>ggplot2</code>’s function <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> has been
|
||||
extended by this package to directly plot MIC and disk diffusion
|
||||
values:</p>
|
||||
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/autoplot-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span></span>
|
||||
<span><span class="co"># by providing `mo` and `ab`, colours will indicate the SIR interpretation:</span></span>
|
||||
<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/autoplot-2.png" class="r-plt" alt="" width="720"></p>
|
||||
<hr>
|
||||
<p><em>Author: Dr. Matthijs Berends, 23rd Feb 2025</em></p>
|
||||
<p><em>Author: Dr. Matthijs Berends, 23rd June 2026</em></p>
|
||||
</div>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
|
||||
285
articles/AMR.md
285
articles/AMR.md
@@ -3,7 +3,7 @@
|
||||
**Note:** values on this page will change with every website update
|
||||
since they are based on randomly created values and the page was written
|
||||
in [R Markdown](https://rmarkdown.rstudio.com/). However, the
|
||||
methodology remains unchanged. This page was generated on 02 May 2026.
|
||||
methodology remains unchanged. This page was generated on 23 June 2026.
|
||||
|
||||
## Introduction
|
||||
|
||||
@@ -51,9 +51,9 @@ structure of your data generally look like this:
|
||||
|
||||
| date | patient_id | mo | AMX | CIP |
|
||||
|:----------:|:----------:|:----------------:|:---:|:---:|
|
||||
| 2026-05-02 | abcd | Escherichia coli | S | S |
|
||||
| 2026-05-02 | abcd | Escherichia coli | S | R |
|
||||
| 2026-05-02 | efgh | Escherichia coli | R | S |
|
||||
| 2026-06-23 | abcd | Escherichia coli | S | S |
|
||||
| 2026-06-23 | abcd | Escherichia coli | S | R |
|
||||
| 2026-06-23 | efgh | Escherichia coli | R | S |
|
||||
|
||||
### Needed R packages
|
||||
|
||||
@@ -112,7 +112,7 @@ SIR values as well.
|
||||
With [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), users can
|
||||
transform arbitrary microorganism names or codes to current taxonomy.
|
||||
The `AMR` package contains up-to-date taxonomic data. To be specific,
|
||||
currently included data were retrieved on 24 Jun 2024.
|
||||
currently included data were retrieved on 07 May 2026.
|
||||
|
||||
The codes of the AMR packages that come from
|
||||
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) are short, but
|
||||
@@ -199,24 +199,23 @@ mo_uncertainties()
|
||||
#> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella pneumoniae
|
||||
#> ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), Klebsiella
|
||||
#> pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii (0.500), Klebsiella
|
||||
#> planticola (0.500), Kingella potus (0.400), Kluyveromyces pseudotropicale
|
||||
#> (0.386), Kluyveromyces pseudotropicalis (0.363), and Kosakonia pseudosacchari
|
||||
#> (0.361)
|
||||
#> planticola (0.500), Kosakonia pseudosacchari (0.471), Kaistella palustris
|
||||
#> (0.435), Kingella potus (0.435), and Kocuria palustris (0.435)
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
|
||||
#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus argenteus
|
||||
#> (0.625), Staphylococcus aureus anaerobius (0.625), Staphylococcus auricularis
|
||||
#> (0.615), Salmonella Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella
|
||||
#> Amounderness (0.587), Staphylococcus argensis (0.587), Streptococcus australis
|
||||
#> (0.587), and Salmonella choleraesuis arizonae (0.562)
|
||||
#> (0.625), Staphylococcus aureus anaerobius (0.625), Streptomyces aureus (0.618),
|
||||
#> Staphylococcus auricularis (0.615), Streptomyces azureus (0.609), Salmonella
|
||||
#> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness (0.587),
|
||||
#> and Staphylococcus argensis (0.587)
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
|
||||
#> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus phocae
|
||||
#> salmonis (0.552), Serratia proteamaculans quinovora (0.545), Streptococcus
|
||||
#> pseudoporcinus (0.536), Staphylococcus piscifermentans (0.533), Staphylococcus
|
||||
#> pseudintermedius (0.532), Serratia proteamaculans proteamaculans (0.526),
|
||||
#> Streptococcus gallolyticus pasteurianus (0.526), Salmonella Portanigra (0.524),
|
||||
#> and Streptococcus periodonticum (0.519)
|
||||
#> Also matched: Streptococcus parapneumoniae (0.714), Streptococcus
|
||||
#> pseudopneumoniae (0.700), Serratia proteamaculans quinivorans (0.557),
|
||||
#> Streptococcus phocae salmonis (0.552), Serratia proteamaculans quinovora
|
||||
#> (0.545), Sphingomonas piscinae (0.538), Streptococcus pseudoporcinus (0.536),
|
||||
#> Staphylococcus piscifermentans (0.533), Staphylococcus pseudintermedius
|
||||
#> (0.532), and Serratia proteamaculans proteamaculans (0.526)
|
||||
#> ℹ Only the first 10 other matches of each record are shown. Run ``
|
||||
#> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save
|
||||
#> `mo_uncertainties()` to an object.
|
||||
@@ -575,33 +574,42 @@ our_data_1st[all(betalactams() == "R"), ]
|
||||
|
||||
### Generate antibiograms
|
||||
|
||||
Since AMR v2.0 (March 2023), it is very easy to create different types
|
||||
of antibiograms, with support for 20 different languages.
|
||||
The `AMR` package supports 28 different languages for antibiograms and
|
||||
provides four types, as proposed by Klinker *et al.* (2021, [DOI
|
||||
10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)):
|
||||
|
||||
There are four antibiogram types, as proposed by Klinker *et al.* (2021,
|
||||
[DOI
|
||||
10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)),
|
||||
and they are all supported by the new
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function:
|
||||
1. **Traditional Antibiogram (TA)** – susceptibility of a species to
|
||||
individual antibiotics
|
||||
2. **Combination Antibiogram (CA)** – susceptibility of a species to
|
||||
combination regimens
|
||||
3. **Syndromic Antibiogram (SA)** – susceptibility of a species,
|
||||
stratified by clinical syndrome or setting
|
||||
4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)** –
|
||||
estimated empirical coverage of a *regimen* for a *syndrome*,
|
||||
weighted by pathogen incidence and with quantified uncertainty
|
||||
|
||||
1. **Traditional Antibiogram (TA)** e.g, for the susceptibility of
|
||||
*Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
|
||||
2. **Combination Antibiogram (CA)** e.g, for the sdditional
|
||||
susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin
|
||||
versus TZP alone
|
||||
3. **Syndromic Antibiogram (SA)** e.g, for the susceptibility of
|
||||
*Pseudomonas aeruginosa* to TZP among respiratory specimens
|
||||
(obtained among ICU patients only)
|
||||
4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
|
||||
e.g, for the susceptibility of *Pseudomonas aeruginosa* to TZP among
|
||||
respiratory specimens (obtained among ICU patients only) for male
|
||||
patients age \>=65 years with heart failure
|
||||
**If your goal is to guide empirical therapy, WISCA should be your
|
||||
default.** The reason is simple: when you start empirical treatment, you
|
||||
do not know which pathogen is causing the infection. Your next patient
|
||||
will not present with a species label attached to them. What matters is
|
||||
the probability that the *regimen* you choose will cover *whatever
|
||||
pathogen turns out to be the cause*, given the local epidemiology of the
|
||||
syndrome. Traditional antibiograms do not answer that question. They
|
||||
fragment information by species, ignore how frequently each species
|
||||
causes the syndrome, do not evaluate combination regimens, and provide
|
||||
no measure of uncertainty. WISCA addresses all of these limitations
|
||||
using a Bayesian framework (Hebert *et al.*, 2012; Bielicki *et al.*,
|
||||
2016). See the [WISCA
|
||||
vignette](https://amr-for-r.org/articles/WISCA.html) for the full
|
||||
explanation.
|
||||
|
||||
In this section, we show how to use the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function to create any of the above antibiogram types. For starters,
|
||||
this is what the included `example_isolates` data set looks like:
|
||||
Traditional, combination, and syndromic antibiograms remain useful for
|
||||
**surveillance** purposes, i.e., tracking resistance trends per species
|
||||
over time. But if you care about clinical impact, about choosing the
|
||||
right empirical regimen for your patient, use WISCA.
|
||||
|
||||
For starters, this is what the included `example_isolates` data set
|
||||
looks like:
|
||||
|
||||
``` r
|
||||
|
||||
@@ -628,13 +636,106 @@ example_isolates
|
||||
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
|
||||
```
|
||||
|
||||
#### WISCA (recommended for empirical therapy guidance)
|
||||
|
||||
Use the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function, or equivalently `antibiogram(..., wisca = TRUE)`. WISCA
|
||||
produces a single coverage estimate per regimen for the entire syndrome,
|
||||
weighted by pathogen incidence, with a 95% credible interval from
|
||||
Bayesian Monte Carlo simulation:
|
||||
|
||||
``` r
|
||||
|
||||
wisca_result <- example_isolates %>%
|
||||
wisca(
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10
|
||||
) # Recommended threshold: ≥30
|
||||
wisca_result
|
||||
```
|
||||
|
||||
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:---|:---|:---|
|
||||
| 70.2% (64.8-75.2%) | 93.6% (92.2-95%) | 89.9% (87-92.3%) |
|
||||
|
||||
The output tells you: *“given the species distribution in your data,
|
||||
there is an estimated X% probability that this regimen covers the
|
||||
infection, with 95% credible interval \[lower, upper\]”*. That is the
|
||||
clinically relevant question.
|
||||
|
||||
For **syndrome-specific** or **patient-specific WISCA**, use the
|
||||
`syndromic_group` argument or group your data first. You can stratify by
|
||||
anything: ward, age group, risk profile, acquisition type. The
|
||||
`syndromic_group` argument accepts any column or expression:
|
||||
|
||||
``` r
|
||||
|
||||
wisca_out <- example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
group_by(
|
||||
age_group = age_groups(age, c(25, 50, 75)),
|
||||
gender
|
||||
) %>%
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
|
||||
wisca_out
|
||||
```
|
||||
|
||||
| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:---|:---|:---|:---|:---|
|
||||
| 0-24 | F | 56.8% (29.9-81.3%) | 70.7% (45.2-89%) | 65.9% (42.3-86.6%) |
|
||||
| 0-24 | M | 59.5% (31.2-85.5%) | 76.1% (56.5-92%) | 59.6% (31.7-85.1%) |
|
||||
| 25-49 | F | 67.7% (43.9-89.7%) | 93.8% (87.4-98.1%) | 87% (70.1-97%) |
|
||||
| 25-49 | M | 56.9% (26.6-86.2%) | 91% (82-97.2%) | 76.6% (51.4-93.5%) |
|
||||
| 50-74 | F | 68% (54.1-81.8%) | 96.9% (94.6-98.5%) | 90.2% (82-96.2%) |
|
||||
| 50-74 | M | 67% (56-78.5%) | 96.7% (94.1-98.5%) | 86.7% (77.3-94.4%) |
|
||||
| 75+ | F | 73.1% (61.8-84.1%) | 97.7% (95.9-99%) | 92.8% (85.7-97.2%) |
|
||||
| 75+ | M | 74% (63.6-82.6%) | 97.9% (96-99%) | 94.7% (89.3-97.9%) |
|
||||
|
||||
Keep in mind that more granular stratification produces more relevant
|
||||
estimates for each subgroup, but with wider credible intervals due to
|
||||
smaller sample sizes. There is always a trade-off between granularity
|
||||
and precision. If local numbers are small, consider pooling data from
|
||||
multiple sites (Bielicki *et al.*, 2016).
|
||||
|
||||
For reliable WISCA results, ensure your data includes **only first
|
||||
isolates** (use
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md))
|
||||
and consider filtering for **the top *n* species** (use
|
||||
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md)),
|
||||
since rare contaminants can distort coverage estimates.
|
||||
|
||||
After creating the WISCA model, assessments can be done on the
|
||||
distributions of the Monte Carlo simulations that WISCA carried out:
|
||||
|
||||
``` r
|
||||
|
||||
wisca_plot(wisca_out)
|
||||
```
|
||||
|
||||
|
||||
|
||||
``` r
|
||||
|
||||
wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
|
||||
```
|
||||
|
||||
|
||||
|
||||
``` r
|
||||
|
||||
|
||||
# a ggplot2 extension for WISCAs and other antibiograms:
|
||||
ggplot2::autoplot(wisca_out)
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### Traditional Antibiogram
|
||||
|
||||
To create a traditional antibiogram, simply state which antibiotics
|
||||
should be used. The `antibiotics` argument in the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function supports any (combination) of the previously mentioned
|
||||
antibiotic class selectors:
|
||||
If you need per-species susceptibility rates, e.g., for AMR surveillance
|
||||
reports, the traditional antibiogram remains the right tool. It reports
|
||||
the proportion of susceptible isolates per species per antibiotic:
|
||||
|
||||
``` r
|
||||
|
||||
@@ -691,10 +792,12 @@ antibiogram(example_isolates,
|
||||
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
|
||||
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
|
||||
|
||||
#### Combined Antibiogram
|
||||
#### Combination Antibiogram
|
||||
|
||||
To create a combined antibiogram, use antibiotic codes or names with a
|
||||
plus `+` character like this:
|
||||
A combination antibiogram shows how much additional susceptibility a
|
||||
second agent adds for a given species. This is useful for surveillance
|
||||
of combination regimens, but note that it is still species-stratified
|
||||
and does not account for pathogen incidence in the syndrome:
|
||||
|
||||
``` r
|
||||
|
||||
@@ -719,10 +822,12 @@ combined_ab
|
||||
|
||||
#### Syndromic Antibiogram
|
||||
|
||||
To create a syndromic antibiogram, the `syndromic_group` argument must
|
||||
be used. This can be any column in the data, or e.g. an
|
||||
[`ifelse()`](https://rdrr.io/r/base/ifelse.html) with calculations based
|
||||
on certain columns:
|
||||
A syndromic antibiogram stratifies per-species susceptibility by
|
||||
clinical context (ward, specimen type, etc.). It adds clinical context
|
||||
to the traditional antibiogram but is still species-level, without
|
||||
incidence weighting or uncertainty quantification. For surveillance by
|
||||
setting this is fine; for empirical therapy guidance, WISCA is
|
||||
preferred:
|
||||
|
||||
``` r
|
||||
|
||||
@@ -752,80 +857,16 @@ antibiogram(example_isolates,
|
||||
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
|
||||
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
|
||||
|
||||
#### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
|
||||
|
||||
To create a **Weighted-Incidence Syndromic Combination Antibiogram
|
||||
(WISCA)**, simply set `wisca = TRUE` in the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function, or use the dedicated
|
||||
[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) function.
|
||||
Unlike traditional antibiograms, WISCA provides syndrome-based
|
||||
susceptibility estimates, weighted by pathogen incidence and
|
||||
antimicrobial susceptibility patterns.
|
||||
|
||||
``` r
|
||||
|
||||
example_isolates %>%
|
||||
wisca(
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10
|
||||
) # Recommended threshold: ≥30
|
||||
```
|
||||
|
||||
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:---|:---|:---|
|
||||
| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
|
||||
|
||||
WISCA uses a **Bayesian decision model** to integrate data from multiple
|
||||
pathogens, improving empirical therapy guidance, especially for
|
||||
low-incidence infections. It is **pathogen-agnostic**, meaning results
|
||||
are syndrome-based rather than stratified by microorganism.
|
||||
|
||||
For reliable results, ensure your data includes **only first isolates**
|
||||
(use
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md))
|
||||
and consider filtering for **the top *n* species** (use
|
||||
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md)),
|
||||
as WISCA outcomes are most meaningful when based on robust incidence
|
||||
estimates.
|
||||
|
||||
For **patient- or syndrome-specific WISCA**, run the function on a
|
||||
grouped `tibble`, i.e., using
|
||||
[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html)
|
||||
first:
|
||||
|
||||
``` r
|
||||
|
||||
example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
group_by(
|
||||
age_group = age_groups(age, c(25, 50, 75)),
|
||||
gender
|
||||
) %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
| age_group | gender | Amikacin | Amoxicillin | Amoxicillin/clavulanic acid | Ampicillin | Azithromycin | Benzylpenicillin | Cefazolin | Cefepime | Cefotaxime | Cefoxitin | Ceftazidime | Ceftriaxone | Cefuroxime | Chloramphenicol | Ciprofloxacin | Clindamycin | Colistin | Doxycycline | Erythromycin | Flucloxacillin | Fosfomycin | Gentamicin | Imipenem | Kanamycin | Linezolid | Meropenem | Metronidazole | Moxifloxacin | Mupirocin | Nitrofurantoin | Oxacillin | Piperacillin/tazobactam | Rifampicin | Teicoplanin | Tetracycline | Tigecycline | Tobramycin | Trimethoprim | Trimethoprim/sulfamethoxazole | Vancomycin |
|
||||
|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|
|
||||
| 0-24 | F | 45.4% (15.4-79%) | 50.1% (20.5-77.6%) | 69% (44.5-88.5%) | 50.4% (20.6-77.3%) | 41.9% (18.1-65.6%) | 36.1% (12.3-64.3%) | NA | NA | 63.9% (34.6-87.6%) | 56.7% (25.9-85.8%) | 51.5% (25.6-74.4%) | 63.4% (32-88.1%) | 70.4% (45.4-89.1%) | 54% (22.3-85.3%) | 69.8% (45.9-88.9%) | 39.3% (17.7-64.6%) | 45.3% (18.1-75.9%) | 50.1% (21.5-80.5%) | 41.7% (19.1-67.6%) | 55.8% (23.7-83.3%) | 63.5% (32.6-89.4%) | 69.3% (44.6-88.3%) | 63.6% (36.1-88.2%) | 45.5% (15.7-77.7%) | 43.3% (17.8-71.2%) | 55.9% (24.3-82.2%) | NA | NA | 56.5% (24.3-85%) | 56.8% (30.9-82.3%) | 50.5% (19.4-80.8%) | 56.9% (26.7-85%) | 42.3% (18.3-68.8%) | 40.2% (17.6-67.7%) | 49.8% (20-79.3%) | 56.1% (22-85.4%) | 64.5% (39.6-85.5%) | 69.7% (42.3-90.4%) | 75.4% (52.1-91.7%) | 48.5% (24.3-72.6%) |
|
||||
| 0-24 | M | 41.9% (15.2-72.5%) | 49.4% (23.3-75.5%) | 73.8% (51.8-90.1%) | 49.3% (22.7-76%) | 63.4% (40.7-83.5%) | 41.8% (20.4-64.8%) | 56.8% (25.2-83.5%) | 58.2% (29.1-85.8%) | 59.7% (29.1-87.4%) | 59.3% (29.1-86.6%) | 24.9% (8.9-47.3%) | 58.5% (28-86.5%) | 72.1% (47.9-90.5%) | NA | 77.2% (53-93.2%) | 61.6% (36.2-83.6%) | 25.5% (8.7-46.1%) | 69.4% (44.6-89.4%) | 63.4% (41.8-82.7%) | 64% (37.6-85.6%) | NA | 63.5% (40.9-83.1%) | 58.7% (27.6-86.5%) | 41.8% (13.6-71.2%) | 48.3% (17.9-78%) | 59.2% (27.4-86.4%) | NA | NA | NA | 53% (21.3-83.7%) | 57.2% (24.6-84.6%) | 59.9% (29.7-85.6%) | 48.2% (16.1-80.4%) | 48.4% (17.4-79.8%) | 68% (43.5-87.3%) | 65.7% (36-89.2%) | 44.3% (17.2-73.4%) | 69.4% (46.9-87.8%) | 74% (50.8-90.9%) | 75.3% (52.4-92.2%) |
|
||||
| 25-49 | F | 46.8% (26.7-65.6%) | 39% (26.3-52.9%) | 73.8% (63.5-82.6%) | 39.3% (27.2-54.4%) | 54.8% (44.9-64.8%) | 36.5% (26.3-47.1%) | 66.4% (46.1-85%) | 69.2% (49.1-86.2%) | 70.2% (50.5-86.2%) | 68.1% (48.6-85.4%) | 27.9% (19.2-37.9%) | 70.1% (50.5-87.1%) | 71.4% (61.7-80.4%) | 58.2% (35.3-79.9%) | 85.5% (74.1-94.2%) | 67.1% (55.8-77.4%) | 25.8% (17-36%) | 75.5% (61.2-88.2%) | 54.9% (44.8-65.6%) | 55.2% (37.8-72.5%) | 60.9% (38.3-81.8%) | 75.2% (65.7-83.5%) | 69.9% (50.1-86.6%) | 37.5% (17.7-57.9%) | 50.7% (30.8-68.7%) | 69.4% (48.6-86.7%) | NA | 56.9% (36.2-77.7%) | 53.3% (30.9-75.6%) | 60.1% (38.1-81.8%) | 64.3% (43.2-83.6%) | 66% (45.7-85.2%) | 50.1% (30-69.6%) | 38.8% (19.6-58.9%) | 75.8% (61.6-88.1%) | 73.3% (56.6-89.5%) | 62.7% (47.6-77.1%) | 70.4% (58.7-80.2%) | 90% (82.9-95.4%) | 71.6% (61.7-80.4%) |
|
||||
| 25-49 | M | 49.8% (24.2-75.8%) | 16.5% (8.1-27.4%) | 72.4% (60.5-83.5%) | 16.6% (7.7-28.2%) | 55.9% (43.6-67.9%) | 24.9% (14.7-37.6%) | 60.3% (33.2-82.4%) | 55.3% (27.6-81.8%) | 55.9% (29.7-81.1%) | 56.2% (27.7-82.1%) | 22.2% (12.7-33.9%) | 55.6% (29.1-81.8%) | 73.7% (62.6-83.8%) | 52.9% (25.2-79.6%) | 67.1% (53-79.8%) | 57.8% (43.5-71.8%) | 22.3% (12.6-33.6%) | 73% (57.8-85.5%) | 55.8% (43-68.2%) | 66.5% (51.6-79.4%) | 63.1% (40.3-84.5%) | 83.9% (74.5-91.7%) | 56.4% (28.4-84%) | 45.4% (18.9-73.8%) | 59.4% (37.6-77.9%) | 56.3% (28.8-81.1%) | NA | 52.8% (24.7-78.7%) | 64.2% (40.2-84.5%) | 62.9% (37.7-85.1%) | 60.5% (37.1-80.7%) | 55.8% (29.4-82.9%) | 65.4% (48.7-80.8%) | 54.5% (31.7-73.7%) | 72.8% (58.7-84.8%) | 84.8% (72.4-93.6%) | 66.7% (44.5-84.1%) | 71.4% (58.9-82.6%) | 86.6% (77.9-93.7%) | 77.1% (65.5-87.1%) |
|
||||
| 50-74 | F | 44.8% (35.8-54.1%) | 30.1% (24.9-35.3%) | 74.1% (69.2-78.7%) | 30% (24.6-35.4%) | 41.9% (36.5-47.3%) | 23.5% (18.6-29%) | 73.1% (62-82.9%) | 76.6% (66.1-86%) | 74.8% (64.9-84.5%) | 74.6% (64.2-83.3%) | 37.5% (32.3-43.4%) | 74.8% (64.4-83.8%) | 74.5% (69.7-78.9%) | 61.2% (40.3-82.4%) | 79.4% (73-85%) | 44.9% (38.7-51%) | 37.8% (32.7-43.3%) | 63.8% (47.6-80.1%) | 41.7% (36.6-46.9%) | 58.1% (40-75.1%) | 65.2% (53.5-76.6%) | 78.7% (73.8-83.2%) | 80.6% (70.3-90%) | 28.1% (10.1-46.6%) | 53.2% (42.9-62.4%) | 79.3% (68.7-88.6%) | NA | 49.5% (37.5-61.8%) | 67.8% (48.5-86%) | 75.1% (63.3-86.3%) | 56.6% (37.8-74.2%) | 67.7% (56.4-79.6%) | 50.6% (40.9-59.1%) | 41.3% (31.5-50.4%) | 59% (48.3-74.5%) | 87.7% (80.4-94.1%) | 62.2% (55.4-68.4%) | 55.5% (49.8-61.1%) | 68% (62.7-73.3%) | 60.9% (55.8-66.1%) |
|
||||
| 50-74 | M | 38.8% (30.6-48.6%) | 34.6% (29.1-40.3%) | 75% (70-79.5%) | 34.7% (29.2-40.5%) | 43.4% (37.8-48.5%) | 21% (16.5-26.4%) | 64.3% (54.1-74.1%) | 65.9% (56.5-75.4%) | 67.3% (58.3-77%) | 65.9% (56.1-75.9%) | 32.9% (27.6-38%) | 67.3% (57.4-76.8%) | 74.1% (69.2-78.8%) | 63.5% (42.4-83%) | 76.9% (71.6-81.9%) | 47.3% (40.9-53.8%) | 30.8% (26.1-36.1%) | 68.5% (53.5-81.9%) | 43.4% (37.7-48.8%) | 58.1% (42.4-73.2%) | 68.1% (53.5-82.2%) | 79.1% (74.4-83.1%) | 69% (59.7-78.3%) | 24.8% (9.5-40.5%) | 49.7% (35-63.2%) | 68.1% (58.1-77.6%) | 53.8% (32-75%) | 51.7% (36.1-67.3%) | 68.8% (51.1-85.7%) | 70.2% (54.7-85.3%) | 53.2% (37.5-68.7%) | 66.5% (55-76.8%) | 56.2% (45.8-65.4%) | 44% (30.3-57.5%) | 71.9% (58.2-82.2%) | 86.8% (77.3-93.6%) | 54.1% (46.9-61.4%) | 67.1% (61.5-72.5%) | 81% (76.4-85.2%) | 66.3% (61-71.2%) |
|
||||
| 75+ | F | 51.4% (41.7-62%) | 30.9% (26.2-36.5%) | 74.4% (70.3-78.6%) | 30.9% (25.7-36.1%) | 36.6% (32-41.6%) | 20.7% (16.2-25.4%) | 73.6% (63.6-82.5%) | 79.1% (70.6-86.8%) | 78.6% (69.9-86.3%) | 76% (67.5-83.7%) | 43.1% (38.6-48%) | 78.9% (70.5-86.4%) | 77% (72.6-81.3%) | 63.2% (43.2-84.1%) | 77.7% (72.1-83.2%) | 41.2% (36-46.4%) | 39.1% (34.2-44.4%) | 63.7% (46.3-80.6%) | 36.5% (31.9-41.2%) | 57.1% (39.8-76%) | 65.8% (57.2-73.5%) | 84.6% (80.6-88%) | 81.9% (73.7-89.5%) | 33.3% (13.7-53%) | 49.6% (42.3-56.1%) | 81.3% (73.2-88.9%) | 55.9% (33.5-76.5%) | 41% (31.4-51.8%) | 63.7% (43.8-82.3%) | 77.8% (66-87.4%) | 56.3% (37.3-75.1%) | 71.8% (62-82.2%) | 48.3% (41.5-54.9%) | 43.3% (36.2-50.7%) | 63% (45.3-80.3%) | 85.9% (79.9-90.9%) | 70.4% (64.1-76.8%) | 60.4% (55.1-65.8%) | 77.6% (73.4-82.1%) | 55.3% (50.4-60.1%) |
|
||||
| 75+ | M | 52.6% (43.3-62.6%) | 33% (28.1-38%) | 77.4% (73.3-81.5%) | 33% (28.2-38.2%) | 36.8% (32.3-41.8%) | 17.9% (12.6-23.2%) | 64.4% (55.4-73.3%) | 71.2% (63.1-79.1%) | 67.9% (59.5-75.8%) | 65.3% (56.3-73.6%) | 42.6% (37.8-47.4%) | 68.2% (59.7-76.4%) | 75.1% (70.9-79.2%) | 64.1% (45.8-81.8%) | 77.6% (72-82.6%) | 41% (36-46.4%) | 39.9% (35.1-44.5%) | 62.1% (46-78.8%) | 36.9% (32.4-41.4%) | 59.7% (43.4-76.6%) | 64.7% (56.6-73.6%) | 83% (79.4-86.7%) | 75.7% (66.6-83%) | 31.6% (12.1-51.7%) | 51.8% (44.9-58%) | 74.2% (65.8-82.7%) | NA | 52.2% (41.5-60.8%) | 69.3% (50.5-86.4%) | 72.2% (58.7-83.4%) | 59.3% (41.6-76.7%) | 73.1% (64.3-81.4%) | 49.9% (42.4-56.9%) | 46.3% (38.2-53.1%) | 59.7% (44.2-75.7%) | 86.8% (81.4-91.3%) | 72% (66.3-77.6%) | 55.8% (50.3-61.1%) | 73.3% (68.9-77.6%) | 57% (52.2-61.6%) |
|
||||
|
||||
#### Plotting antibiograms
|
||||
|
||||
Antibiograms can be plotted using
|
||||
All antibiogram types, including WISCA, can be plotted using
|
||||
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
|
||||
from the `ggplot2` packages, since this `AMR` package provides an
|
||||
from the `ggplot2` package, since this `AMR` package provides an
|
||||
extension to that function:
|
||||
|
||||
``` r
|
||||
|
||||
autoplot(combined_ab)
|
||||
autoplot(wisca_result)
|
||||
```
|
||||
|
||||
|
||||
@@ -989,4 +1030,4 @@ autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 202
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
*Author: Dr. Matthijs Berends, 23rd Feb 2025*
|
||||
*Author: Dr. Matthijs Berends, 23rd June 2026*
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 51 KiB After Width: | Height: | Size: 36 KiB |
BIN
articles/AMR_files/figure-html/wisca_plots-1.png
Normal file
BIN
articles/AMR_files/figure-html/wisca_plots-1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 750 KiB |
BIN
articles/AMR_files/figure-html/wisca_plots-2.png
Normal file
BIN
articles/AMR_files/figure-html/wisca_plots-2.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 121 KiB |
BIN
articles/AMR_files/figure-html/wisca_plots-3.png
Normal file
BIN
articles/AMR_files/figure-html/wisca_plots-3.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 72 KiB |
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
@@ -200,7 +200,7 @@ function:</p>
|
||||
<span><span class="co">#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></span>
|
||||
<span><span class="co">#> Importance of components:</span></span>
|
||||
<span><span class="co">#> PC1 PC2 PC3 PC4 PC5 PC6 PC7</span></span>
|
||||
<span><span class="co">#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17</span></span>
|
||||
<span><span class="co">#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
|
||||
<span><span class="co">#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
|
||||
<span><span class="co">#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
|
||||
<pre><code><span><span class="co">#> Groups (n=4, named as 'order'):</span></span>
|
||||
|
||||
@@ -123,7 +123,7 @@ summary(pca_result)
|
||||
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
|
||||
#> Importance of components:
|
||||
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7
|
||||
#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17
|
||||
#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
|
||||
#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
|
||||
#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
|
||||
```
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
@@ -87,89 +87,98 @@
|
||||
|
||||
|
||||
|
||||
<blockquote>
|
||||
<p>This explainer was largely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
|
||||
manually-trained model able to answer any question about the
|
||||
<code>AMR</code> package.</p>
|
||||
</blockquote>
|
||||
<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
|
||||
</h2>
|
||||
<p>Clinical guidelines for empirical antimicrobial therapy require
|
||||
<em>probabilistic reasoning</em>: what is the chance that a regimen will
|
||||
cover the likely infecting organisms, before culture results are
|
||||
available?</p>
|
||||
<p>This is the purpose of <strong>WISCA</strong>, or
|
||||
<strong>Weighted-Incidence Syndromic Combination
|
||||
Antibiogram</strong>.</p>
|
||||
<p>WISCA is a Bayesian approach that integrates:</p>
|
||||
<p>When a clinician starts empirical antimicrobial therapy, the
|
||||
causative pathogen is unknown. The question they need answered is not
|
||||
<em>“what proportion of</em> E. coli <em>is susceptible to
|
||||
ciprofloxacin?“</em> but rather <em>“what is the probability that this
|
||||
regimen will adequately cover whatever pathogen turns out to be causing
|
||||
my patient’s infection?”</em></p>
|
||||
<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
|
||||
cannot answer that question. It presents susceptibility percentages per
|
||||
species per antibiotic, but:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Pathogen prevalence</strong> (how often each species causes
|
||||
the syndrome),</li>
|
||||
<strong>It fragments information by organism.</strong> The clinician
|
||||
must mentally combine susceptibility rates across multiple species,
|
||||
weighting by how often each species causes the syndrome, a calculation
|
||||
nobody does at the bedside.</li>
|
||||
<li>
|
||||
<strong>Regimen susceptibility</strong> (how often a regimen works
|
||||
<em>if</em> the pathogen is known),</li>
|
||||
<strong>It ignores pathogen incidence.</strong> A species that
|
||||
causes 2% of infections is given the same visual weight as one that
|
||||
causes 60%.</li>
|
||||
<li>
|
||||
<strong>It does not evaluate combination regimens.</strong> Much
|
||||
empirical therapy consists of two or more agents, but the traditional
|
||||
antibiogram only shows monotherapy per organism.</li>
|
||||
<li>
|
||||
<strong>It provides no measure of uncertainty.</strong> A reported
|
||||
“90% susceptible” based on 50 isolates has a 95% confidence interval of
|
||||
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
|
||||
point estimate without context.</li>
|
||||
</ul>
|
||||
<p>to estimate the <strong>overall empirical coverage</strong> of
|
||||
antimicrobial regimens, with quantified uncertainty.</p>
|
||||
<p>This vignette explains how WISCA works, why it is useful, and how to
|
||||
apply it using the <code>AMR</code> package.</p>
|
||||
<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
|
||||
Antibiogram) resolves all four limitations. It estimates the probability
|
||||
that a regimen will provide adequate empirical coverage for a given
|
||||
infection syndrome, weighted by local pathogen incidence, with full
|
||||
uncertainty quantification via Bayesian inference.</p>
|
||||
<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
|
||||
demonstrated that traditional antibiogram susceptibility rates could be
|
||||
misleading: ciprofloxacin appeared 84% effective against <em>E.
|
||||
coli</em> in the traditional antibiogram, but WISCA revealed only 62%
|
||||
coverage for UTI and 37% for abdominal infections, because enterococci
|
||||
(intrinsically resistant) and other species contribute substantially to
|
||||
these syndromes. Randhawa <em>et al.</em> (2014) showed that
|
||||
WISCA-guided regimen selection could improve time-to-adequate-coverage
|
||||
on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
|
||||
Bayesian framework now used in this package, enabling credible intervals
|
||||
and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
|
||||
globally across 52 hospitals in 23 countries.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="why-traditional-antibiograms-fall-short">Why traditional antibiograms fall short<a class="anchor" aria-label="anchor" href="#why-traditional-antibiograms-fall-short"></a>
|
||||
</h2>
|
||||
<p>A standard antibiogram gives you:</p>
|
||||
<pre><code>Species → Antibiotic → Susceptibility %</code></pre>
|
||||
<p>But clinicians don’t know the species <em>a priori</em>. They need to
|
||||
choose a regimen that covers the <strong>likely pathogens</strong>,
|
||||
without knowing which one is present.</p>
|
||||
<p>Traditional antibiograms calculate the susceptibility % as just the
|
||||
number of resistant isolates divided by the total number of tested
|
||||
isolates. Therefore, traditional antibiograms:</p>
|
||||
<ul>
|
||||
<li>Fragment information by organism,</li>
|
||||
<li>Do not weight by real-world prevalence,</li>
|
||||
<li>Do not account for combination therapy or sample size,</li>
|
||||
<li>Do not provide uncertainty.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-idea-of-wisca">The idea of WISCA<a class="anchor" aria-label="anchor" href="#the-idea-of-wisca"></a>
|
||||
<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
|
||||
</h2>
|
||||
<p>WISCA asks:</p>
|
||||
<blockquote>
|
||||
<p>“What is the <strong>probability</strong> that this regimen
|
||||
<strong>will cover</strong> the pathogen, given the syndrome?”</p>
|
||||
</blockquote>
|
||||
<p>This means combining two things:</p>
|
||||
<p>This means combining two quantities:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Incidence</strong> of each pathogen in the syndrome,</li>
|
||||
<strong>Pathogen incidence</strong> in the syndrome (how often each
|
||||
species causes it),</li>
|
||||
<li>
|
||||
<strong>Susceptibility</strong> of each pathogen to the
|
||||
regimen.</li>
|
||||
</ul>
|
||||
<p>We can write this as:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
|
||||
<p>For example, suppose:</p>
|
||||
<p>For example, suppose in your hospital:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<em>E. coli</em> causes 60% of cases, and 90% of <em>E. coli</em>
|
||||
are susceptible to a drug.</li>
|
||||
<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
|
||||
susceptible to a drug.</li>
|
||||
<li>
|
||||
<em>Klebsiella</em> causes 40% of cases, and 70% of
|
||||
<em>Klebsiella</em> causes 40% of UTIs, and 70% of
|
||||
<em>Klebsiella</em> are susceptible.</li>
|
||||
</ul>
|
||||
<p>Then:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
|
||||
<p>But in real data, incidence and susceptibility are <strong>estimated
|
||||
from samples</strong>, so they carry uncertainty. WISCA models this
|
||||
<strong>probabilistically</strong>, using conjugate Bayesian
|
||||
<p>That 82% is a far more clinically meaningful number than the
|
||||
species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
|
||||
reported separately in a traditional antibiogram, because it directly
|
||||
answers the question the clinician actually faces.</p>
|
||||
<p>But in real data, both incidence and susceptibility are
|
||||
<strong>estimated from finite samples</strong>, so they carry
|
||||
uncertainty. A sample of 50 isolates is not a census. WISCA models this
|
||||
uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
|
||||
distributions.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-bayesian-engine-behind-wisca">The Bayesian engine behind WISCA<a class="anchor" aria-label="anchor" href="#the-bayesian-engine-behind-wisca"></a>
|
||||
<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
|
||||
@@ -180,27 +189,38 @@ distributions.</p>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
|
||||
be the number of pathogens,</li>
|
||||
<li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math>
|
||||
be a <strong>Dirichlet</strong> prior (uniform),</li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
|
||||
be a
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
|
||||
prior (uniform, non-informative),</li>
|
||||
<li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">n = (n_1, \ldots, n_K)</annotation></semantics></math>
|
||||
be the observed counts per species.</li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
|
||||
be the observed isolate counts per species.</li>
|
||||
</ul>
|
||||
<p>Then the posterior incidence is:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
|
||||
<p>To simulate from this, we use:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
|
||||
<p>The Dirichlet is the conjugate prior for multinomial data. With the
|
||||
non-informative prior
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
|
||||
the posterior is dominated by the data once sample sizes are reasonable.
|
||||
With small samples, the posterior is appropriately more diffuse,
|
||||
reflecting genuine uncertainty, and the resulting credible intervals
|
||||
will be wider.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
|
||||
</h3>
|
||||
<p>Each pathogen–regimen pair has a prior and data:</p>
|
||||
<p>Each pathogen-regimen pair has a prior and observed data:</p>
|
||||
<ul>
|
||||
<li>Prior:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>,
|
||||
with default
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Default prior:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
|
||||
(Jeffreys prior)</li>
|
||||
<li>Intrinsically resistant pairs:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
|
||||
forcing near-zero susceptibility regardless of observed data (based on
|
||||
EUCAST Expected Resistant Phenotypes)</li>
|
||||
<li>Data:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
|
||||
susceptible out of
|
||||
@@ -224,27 +244,76 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Simulate susceptibility:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Combine:</li>
|
||||
</ol>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
|
||||
<p>Repeat this simulation (e.g. 1000×) and summarise:</p>
|
||||
<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Mean</strong> = expected coverage</li>
|
||||
<li>
|
||||
<strong>Quantiles</strong> = credible interval</li>
|
||||
<strong>Quantiles</strong> = credible interval (95% by default)</li>
|
||||
</ul>
|
||||
<p>Because each simulation draws from the full posterior, the resulting
|
||||
distribution of coverage estimates naturally captures the joint
|
||||
uncertainty in both pathogen incidence and susceptibility. The credible
|
||||
interval tells you how confident you can be in the coverage estimate,
|
||||
something a traditional antibiogram never provides.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
|
||||
</h2>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th>Goal</th>
|
||||
<th>Recommended approach</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>Guide empirical therapy decisions</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Compare regimens for a syndrome</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Evaluate combination regimens</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Antimicrobial stewardship (A-team)</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Track resistance trends per species</td>
|
||||
<td>Traditional / Combination</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>AMR surveillance reporting</td>
|
||||
<td>Traditional / Syndromic</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Understand species-level epidemiology</td>
|
||||
<td>Traditional</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>In short: if the end goal involves a <em>patient</em> who does not
|
||||
yet have a culture result, WISCA is the appropriate tool. If the end
|
||||
goal is <em>surveillance</em> of resistance at the species level, the
|
||||
traditional antibiogram remains fit for purpose.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="prepare-data-and-simulate-synthetic-syndrome">Prepare data and simulate synthetic syndrome<a class="anchor" aria-label="anchor" href="#prepare-data-and-simulate-synthetic-syndrome"></a>
|
||||
<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><-</span> <span class="va">example_isolates</span></span>
|
||||
<span></span>
|
||||
@@ -271,13 +340,13 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<span><span class="co">#> <span style="color: #949494;"># TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …</span></span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Add a fake syndrome column</span></span>
|
||||
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"No UTI"</span><span class="op">)</span></span></code></pre></div>
|
||||
<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
|
||||
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="basic-wisca-antibiogram">Basic WISCA antibiogram<a class="anchor" aria-label="anchor" href="#basic-wisca-antibiogram"></a>
|
||||
<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
@@ -288,16 +357,19 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<th align="left">Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">73.7% (71.7-75.8%)</td>
|
||||
<td align="left">77% (74.3-79.4%)</td>
|
||||
<td align="left">72.8% (70.7-74.8%)</td>
|
||||
<td align="left">74.2% (72.1-76.1%)</td>
|
||||
<td align="left">78.4% (75.6-81.1%)</td>
|
||||
<td align="left">72.5% (70.4-74.6%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<p>Combination regimens are specified with a <code>+</code> separator.
|
||||
WISCA evaluates whether <em>at least one</em> agent in the combination
|
||||
covers the pathogen:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
@@ -313,16 +385,18 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">73.8% (71.8-75.7%)</td>
|
||||
<td align="left">87.5% (85.9-89%)</td>
|
||||
<td align="left">89.7% (88.2-91.1%)</td>
|
||||
<td align="left">74.2% (72.2-76.1%)</td>
|
||||
<td align="left">88.8% (87.2-90.4%)</td>
|
||||
<td align="left">90.8% (89.4-92.2%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
|
||||
per clinical stratum. You can pass a column name or any expression:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
|
||||
@@ -342,22 +416,22 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">No UTI</td>
|
||||
<td align="left">70.1% (67.8-72.3%)</td>
|
||||
<td align="left">85.2% (83.1-87.2%)</td>
|
||||
<td align="left">87.1% (85.3-88.7%)</td>
|
||||
<td align="left">Non-UTI</td>
|
||||
<td align="left">70.3% (67.9-72.7%)</td>
|
||||
<td align="left">86.8% (84.9-88.7%)</td>
|
||||
<td align="left">88.4% (86.4-90.2%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">UTI</td>
|
||||
<td align="left">80.9% (77.7-83.8%)</td>
|
||||
<td align="left">88.2% (85.7-90.5%)</td>
|
||||
<td align="left">90.9% (88.7-93%)</td>
|
||||
<td align="left">80.3% (77-83.3%)</td>
|
||||
<td align="left">88.4% (85.7-90.8%)</td>
|
||||
<td align="left">91% (88.3-93.3%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>The <code>AMR</code> package is available in 28 languages, which can
|
||||
all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
|
||||
@@ -378,20 +452,36 @@ all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></c
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">No UCI</td>
|
||||
<td align="left">70% (67.8-72.4%)</td>
|
||||
<td align="left">85.3% (83.3-87.2%)</td>
|
||||
<td align="left">87% (85.3-88.8%)</td>
|
||||
<td align="left">Non-UCI</td>
|
||||
<td align="left">70.4% (68-72.8%)</td>
|
||||
<td align="left">86.7% (84.6-88.7%)</td>
|
||||
<td align="left">88.5% (86.5-90.2%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">UCI</td>
|
||||
<td align="left">80.9% (77.7-83.9%)</td>
|
||||
<td align="left">88.2% (85.5-90.6%)</td>
|
||||
<td align="left">90.9% (88.7-93%)</td>
|
||||
<td align="left">80.3% (77.2-83.5%)</td>
|
||||
<td align="left">88.4% (85.5-90.8%)</td>
|
||||
<td align="left">91% (88.4-93.1%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
|
||||
</h3>
|
||||
<p>Each row shows the estimated empirical coverage for a regimen, with a
|
||||
95% credible interval. When comparing regimens:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Overlapping credible intervals</strong> mean there is no
|
||||
statistically significant difference in coverage. If a narrower-spectrum
|
||||
regimen overlaps with a broader one, the narrower-spectrum option can be
|
||||
preferred on stewardship grounds.</li>
|
||||
<li>
|
||||
<strong>Non-overlapping credible intervals</strong> indicate a
|
||||
clinically meaningful difference in coverage.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
|
||||
@@ -407,14 +497,43 @@ susceptible</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
|
||||
</h2>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>First isolates only</strong>: always deduplicate using
|
||||
<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
|
||||
introduce bias.</li>
|
||||
<li>
|
||||
<strong>Pathogen selection</strong>: consider filtering with
|
||||
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
|
||||
(e.g. CoNS without clinical context) can distort estimates and may
|
||||
artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
|
||||
<li>
|
||||
<strong>Sample size</strong>: coverage estimates become reliable
|
||||
with approximately 100+ isolates. For smaller datasets, consider pooling
|
||||
data from multiple sites, but only after verifying that pathogen
|
||||
distributions are sufficiently similar (Bielicki <em>et al.</em>,
|
||||
2016).</li>
|
||||
<li>
|
||||
<strong>Culture request bias</strong>: WISCA is only as good as the
|
||||
data it is based on. If cultures are selectively requested (e.g. only
|
||||
after treatment failure), the dataset will be biased towards resistant
|
||||
isolates. A robust culture policy is essential for reliable
|
||||
estimates.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
|
||||
</h2>
|
||||
<ul>
|
||||
<li>It assumes your data are representative</li>
|
||||
<li>No adjustment for patient-level covariates, although these could be
|
||||
passed onto the <code>syndromic_group</code> argument</li>
|
||||
<li>WISCA does not model resistance over time, you might want to use
|
||||
<code>tidymodels</code> for that, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
|
||||
<li>It assumes your data are representative of the patient population
|
||||
you are treating</li>
|
||||
<li>No direct adjustment for patient-level covariates, although these
|
||||
can be passed onto the <code>syndromic_group</code> argument for
|
||||
stratification</li>
|
||||
<li>WISCA does not model resistance trends over time; for that, you
|
||||
might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
|
||||
basic introduction</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -424,24 +543,48 @@ basic introduction</a>
|
||||
</h2>
|
||||
<p>WISCA enables:</p>
|
||||
<ul>
|
||||
<li>Empirical regimen comparison,</li>
|
||||
<li>Syndrome-specific coverage estimation,</li>
|
||||
<li>Fully probabilistic interpretation.</li>
|
||||
<li>
|
||||
<strong>Empirical regimen comparison</strong>, answering the
|
||||
clinician’s actual question</li>
|
||||
<li>
|
||||
<strong>Syndrome-specific coverage estimation</strong>, stratifiable
|
||||
by any clinical variable</li>
|
||||
<li>
|
||||
<strong>Fully probabilistic interpretation</strong>, with credible
|
||||
intervals that honestly communicate uncertainty</li>
|
||||
</ul>
|
||||
<p>It is available in the <code>AMR</code> package via either:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
|
||||
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
|
||||
</h2>
|
||||
<p>Bielicki, JA, et al. (2016). <em>Selecting appropriate empirical
|
||||
antibiotic regimens for paediatric bloodstream infections: application
|
||||
of a Bayesian decision model to local and pooled antimicrobial
|
||||
resistance surveillance data.</em> <strong>J Antimicrob
|
||||
Chemother</strong>. 71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a></p>
|
||||
<ol style="list-style-type: decimal">
|
||||
<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
|
||||
Demonstration of the weighted-incidence syndromic combination
|
||||
antibiogram: an empiric prescribing decision aid. <em>Infect Control
|
||||
Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
|
||||
</li>
|
||||
<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
|
||||
Weighted-incidence syndromic combination antibiograms to guide empiric
|
||||
treatment of critical care infections: a retrospective cohort study.
|
||||
<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
|
||||
</li>
|
||||
<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
|
||||
Selecting appropriate empirical antibiotic regimens for paediatric
|
||||
bloodstream infections: application of a Bayesian decision model to
|
||||
local and pooled antimicrobial resistance surveillance data. <em>J
|
||||
Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
|
||||
</li>
|
||||
<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
|
||||
prescribing in pediatric bloodstream infections: a potential application
|
||||
of weighted-incidence syndromic combination antibiograms (WISCA).
|
||||
<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
|
||||
</li>
|
||||
</ol>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
|
||||
@@ -1,59 +1,61 @@
|
||||
# Estimating Empirical Coverage with WISCA
|
||||
|
||||
> This explainer was largely written by our [AMR for R
|
||||
> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
|
||||
> model able to answer any question about the `AMR` package.
|
||||
## Why WISCA?
|
||||
|
||||
## Introduction
|
||||
When a clinician starts empirical antimicrobial therapy, the causative
|
||||
pathogen is unknown. The question they need answered is not *“what
|
||||
proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
|
||||
*“what is the probability that this regimen will adequately cover
|
||||
whatever pathogen turns out to be causing my patient’s infection?”*
|
||||
|
||||
Clinical guidelines for empirical antimicrobial therapy require
|
||||
*probabilistic reasoning*: what is the chance that a regimen will cover
|
||||
the likely infecting organisms, before culture results are available?
|
||||
The traditional cumulative antibiogram, as standardised by CLSI M39,
|
||||
cannot answer that question. It presents susceptibility percentages per
|
||||
species per antibiotic, but:
|
||||
|
||||
This is the purpose of **WISCA**, or **Weighted-Incidence Syndromic
|
||||
Combination Antibiogram**.
|
||||
- **It fragments information by organism.** The clinician must mentally
|
||||
combine susceptibility rates across multiple species, weighting by how
|
||||
often each species causes the syndrome, a calculation nobody does at
|
||||
the bedside.
|
||||
- **It ignores pathogen incidence.** A species that causes 2% of
|
||||
infections is given the same visual weight as one that causes 60%.
|
||||
- **It does not evaluate combination regimens.** Much empirical therapy
|
||||
consists of two or more agents, but the traditional antibiogram only
|
||||
shows monotherapy per organism.
|
||||
- **It provides no measure of uncertainty.** A reported “90%
|
||||
susceptible” based on 50 isolates has a 95% confidence interval of
|
||||
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
|
||||
point estimate without context.
|
||||
|
||||
WISCA is a Bayesian approach that integrates:
|
||||
**WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
|
||||
resolves all four limitations. It estimates the probability that a
|
||||
regimen will provide adequate empirical coverage for a given infection
|
||||
syndrome, weighted by local pathogen incidence, with full uncertainty
|
||||
quantification via Bayesian inference.
|
||||
|
||||
- **Pathogen prevalence** (how often each species causes the syndrome),
|
||||
- **Regimen susceptibility** (how often a regimen works *if* the
|
||||
pathogen is known),
|
||||
The concept was introduced by Hebert *et al.* (2012), who demonstrated
|
||||
that traditional antibiogram susceptibility rates could be misleading:
|
||||
ciprofloxacin appeared 84% effective against *E. coli* in the
|
||||
traditional antibiogram, but WISCA revealed only 62% coverage for UTI
|
||||
and 37% for abdominal infections, because enterococci (intrinsically
|
||||
resistant) and other species contribute substantially to these
|
||||
syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
|
||||
selection could improve time-to-adequate-coverage on the ICU by over
|
||||
40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
|
||||
in this package, enabling credible intervals and multi-centre pooling.
|
||||
Cook *et al.* (2022) applied it globally across 52 hospitals in 23
|
||||
countries.
|
||||
|
||||
to estimate the **overall empirical coverage** of antimicrobial
|
||||
regimens, with quantified uncertainty.
|
||||
|
||||
This vignette explains how WISCA works, why it is useful, and how to
|
||||
apply it using the `AMR` package.
|
||||
|
||||
## Why traditional antibiograms fall short
|
||||
|
||||
A standard antibiogram gives you:
|
||||
|
||||
Species → Antibiotic → Susceptibility %
|
||||
|
||||
But clinicians don’t know the species *a priori*. They need to choose a
|
||||
regimen that covers the **likely pathogens**, without knowing which one
|
||||
is present.
|
||||
|
||||
Traditional antibiograms calculate the susceptibility % as just the
|
||||
number of resistant isolates divided by the total number of tested
|
||||
isolates. Therefore, traditional antibiograms:
|
||||
|
||||
- Fragment information by organism,
|
||||
- Do not weight by real-world prevalence,
|
||||
- Do not account for combination therapy or sample size,
|
||||
- Do not provide uncertainty.
|
||||
|
||||
## The idea of WISCA
|
||||
## The idea
|
||||
|
||||
WISCA asks:
|
||||
|
||||
> “What is the **probability** that this regimen **will cover** the
|
||||
> pathogen, given the syndrome?”
|
||||
|
||||
This means combining two things:
|
||||
This means combining two quantities:
|
||||
|
||||
- **Incidence** of each pathogen in the syndrome,
|
||||
- **Pathogen incidence** in the syndrome (how often each species causes
|
||||
it),
|
||||
- **Susceptibility** of each pathogen to the regimen.
|
||||
|
||||
We can write this as:
|
||||
@@ -62,11 +64,11 @@ We can write this as:
|
||||
\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
|
||||
```
|
||||
|
||||
For example, suppose:
|
||||
For example, suppose in your hospital:
|
||||
|
||||
- *E. coli* causes 60% of cases, and 90% of *E. coli* are susceptible to
|
||||
- *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
|
||||
a drug.
|
||||
- *Klebsiella* causes 40% of cases, and 70% of *Klebsiella* are
|
||||
- *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
|
||||
susceptible.
|
||||
|
||||
Then:
|
||||
@@ -75,24 +77,32 @@ Then:
|
||||
\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
|
||||
```
|
||||
|
||||
But in real data, incidence and susceptibility are **estimated from
|
||||
samples**, so they carry uncertainty. WISCA models this
|
||||
**probabilistically**, using conjugate Bayesian distributions.
|
||||
That 82% is a far more clinically meaningful number than the
|
||||
species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
|
||||
separately in a traditional antibiogram, because it directly answers the
|
||||
question the clinician actually faces.
|
||||
|
||||
## The Bayesian engine behind WISCA
|
||||
But in real data, both incidence and susceptibility are **estimated from
|
||||
finite samples**, so they carry uncertainty. A sample of 50 isolates is
|
||||
not a census. WISCA models this uncertainty **probabilistically**, using
|
||||
conjugate Bayesian distributions.
|
||||
|
||||
## The Bayesian engine
|
||||
|
||||
### Pathogen incidence
|
||||
|
||||
Let:
|
||||
|
||||
- $`K`$ be the number of pathogens,
|
||||
- $`\alpha = (1, 1, \ldots, 1)`$ be a **Dirichlet** prior (uniform),
|
||||
- $`n = (n_1, \ldots, n_K)`$ be the observed counts per species.
|
||||
- $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
|
||||
prior (uniform, non-informative),
|
||||
- $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
|
||||
per species.
|
||||
|
||||
Then the posterior incidence is:
|
||||
|
||||
``` math
|
||||
p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
|
||||
\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
|
||||
```
|
||||
|
||||
To simulate from this, we use:
|
||||
@@ -101,12 +111,21 @@ To simulate from this, we use:
|
||||
x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
|
||||
```
|
||||
|
||||
The Dirichlet is the conjugate prior for multinomial data. With the
|
||||
non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
|
||||
posterior is dominated by the data once sample sizes are reasonable.
|
||||
With small samples, the posterior is appropriately more diffuse,
|
||||
reflecting genuine uncertainty, and the resulting credible intervals
|
||||
will be wider.
|
||||
|
||||
### Susceptibility
|
||||
|
||||
Each pathogen–regimen pair has a prior and data:
|
||||
Each pathogen-regimen pair has a prior and observed data:
|
||||
|
||||
- Prior: $`\text{Beta}(\alpha_0, \beta_0)`$, with default
|
||||
$`\alpha_0 = \beta_0 = 1`$
|
||||
- Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
|
||||
- Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
|
||||
near-zero susceptibility regardless of observed data (based on EUCAST
|
||||
Expected Resistant Phenotypes)
|
||||
- Data: $`S`$ susceptible out of $`N`$ tested
|
||||
|
||||
The $`S`$ category could also include values SDD (susceptible,
|
||||
@@ -126,21 +145,44 @@ Putting it together:
|
||||
1. Simulate pathogen incidence:
|
||||
$`\boldsymbol{p} \sim \text{Dirichlet}`$
|
||||
2. Simulate susceptibility:
|
||||
$`\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)`$
|
||||
$`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
|
||||
3. Combine:
|
||||
|
||||
``` math
|
||||
\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
|
||||
```
|
||||
|
||||
Repeat this simulation (e.g. 1000×) and summarise:
|
||||
Repeat this simulation (e.g., 1000 times) and summarise:
|
||||
|
||||
- **Mean** = expected coverage
|
||||
- **Quantiles** = credible interval
|
||||
- **Quantiles** = credible interval (95% by default)
|
||||
|
||||
Because each simulation draws from the full posterior, the resulting
|
||||
distribution of coverage estimates naturally captures the joint
|
||||
uncertainty in both pathogen incidence and susceptibility. The credible
|
||||
interval tells you how confident you can be in the coverage estimate,
|
||||
something a traditional antibiogram never provides.
|
||||
|
||||
## When to use WISCA vs. traditional antibiograms
|
||||
|
||||
| Goal | Recommended approach |
|
||||
|---------------------------------------|---------------------------|
|
||||
| Guide empirical therapy decisions | **WISCA** |
|
||||
| Compare regimens for a syndrome | **WISCA** |
|
||||
| Evaluate combination regimens | **WISCA** |
|
||||
| Antimicrobial stewardship (A-team) | **WISCA** |
|
||||
| Track resistance trends per species | Traditional / Combination |
|
||||
| AMR surveillance reporting | Traditional / Syndromic |
|
||||
| Understand species-level epidemiology | Traditional |
|
||||
|
||||
In short: if the end goal involves a *patient* who does not yet have a
|
||||
culture result, WISCA is the appropriate tool. If the end goal is
|
||||
*surveillance* of resistance at the species level, the traditional
|
||||
antibiogram remains fit for purpose.
|
||||
|
||||
## Practical use in the `AMR` package
|
||||
|
||||
### Prepare data and simulate synthetic syndrome
|
||||
### Prepare data
|
||||
|
||||
``` r
|
||||
|
||||
@@ -170,11 +212,11 @@ data
|
||||
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
|
||||
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
|
||||
|
||||
# Add a fake syndrome column
|
||||
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "No UTI")
|
||||
# Add a synthetic syndrome column for demonstration
|
||||
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
|
||||
```
|
||||
|
||||
### Basic WISCA antibiogram
|
||||
### Basic WISCA
|
||||
|
||||
``` r
|
||||
|
||||
@@ -183,12 +225,15 @@ wisca(data,
|
||||
)
|
||||
```
|
||||
|
||||
| Amoxicillin/clavulanic acid | Ciprofloxacin | Gentamicin |
|
||||
|:----------------------------|:-----------------|:-------------------|
|
||||
| 73.7% (71.7-75.8%) | 77% (74.3-79.4%) | 72.8% (70.7-74.8%) |
|
||||
| Amoxicillin/clavulanic acid | Ciprofloxacin | Gentamicin |
|
||||
|:----------------------------|:-------------------|:-------------------|
|
||||
| 74.2% (72.1-76.1%) | 78.4% (75.6-81.1%) | 72.5% (70.4-74.6%) |
|
||||
|
||||
### Use combination regimens
|
||||
|
||||
Combination regimens are specified with a `+` separator. WISCA evaluates
|
||||
whether *at least one* agent in the combination covers the pathogen:
|
||||
|
||||
``` r
|
||||
|
||||
wisca(data,
|
||||
@@ -198,10 +243,13 @@ wisca(data,
|
||||
|
||||
| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:---|:---|:---|
|
||||
| 73.8% (71.8-75.7%) | 87.5% (85.9-89%) | 89.7% (88.2-91.1%) |
|
||||
| 74.2% (72.2-76.1%) | 88.8% (87.2-90.4%) | 90.8% (89.4-92.2%) |
|
||||
|
||||
### Stratify by syndrome
|
||||
|
||||
Use `syndromic_group` to produce separate WISCA estimates per clinical
|
||||
stratum. You can pass a column name or any expression:
|
||||
|
||||
``` r
|
||||
|
||||
wisca(data,
|
||||
@@ -212,8 +260,8 @@ wisca(data,
|
||||
|
||||
| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:---|:---|:---|:---|
|
||||
| No UTI | 70.1% (67.8-72.3%) | 85.2% (83.1-87.2%) | 87.1% (85.3-88.7%) |
|
||||
| UTI | 80.9% (77.7-83.8%) | 88.2% (85.7-90.5%) | 90.9% (88.7-93%) |
|
||||
| Non-UTI | 70.3% (67.9-72.7%) | 86.8% (84.9-88.7%) | 88.4% (86.4-90.2%) |
|
||||
| UTI | 80.3% (77-83.3%) | 88.4% (85.7-90.8%) | 91% (88.3-93.3%) |
|
||||
|
||||
The `AMR` package is available in 28 languages, which can all be used
|
||||
for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
@@ -230,8 +278,20 @@ wisca(data,
|
||||
|
||||
| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
|
||||
|:---|:---|:---|:---|
|
||||
| No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) |
|
||||
| UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) |
|
||||
| Non-UCI | 70.4% (68-72.8%) | 86.7% (84.6-88.7%) | 88.5% (86.5-90.2%) |
|
||||
| UCI | 80.3% (77.2-83.5%) | 88.4% (85.5-90.8%) | 91% (88.4-93.1%) |
|
||||
|
||||
### Interpreting the output
|
||||
|
||||
Each row shows the estimated empirical coverage for a regimen, with a
|
||||
95% credible interval. When comparing regimens:
|
||||
|
||||
- **Overlapping credible intervals** mean there is no statistically
|
||||
significant difference in coverage. If a narrower-spectrum regimen
|
||||
overlaps with a broader one, the narrower-spectrum option can be
|
||||
preferred on stewardship grounds.
|
||||
- **Non-overlapping credible intervals** indicate a clinically
|
||||
meaningful difference in coverage.
|
||||
|
||||
## Sensible defaults, which can be customised
|
||||
|
||||
@@ -239,22 +299,45 @@ wisca(data,
|
||||
- `conf_interval = 0.95`: coverage interval width
|
||||
- `combine_SI = TRUE`: count “I” and “SDD” as susceptible
|
||||
|
||||
## Practical considerations
|
||||
|
||||
- **First isolates only**: always deduplicate using
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
|
||||
before running WISCA. Repeat isolates introduce bias.
|
||||
- **Pathogen selection**: consider filtering with
|
||||
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
|
||||
Including rare contaminants (e.g. CoNS without clinical context) can
|
||||
distort estimates and may artificially lower coverage (Cook *et al.*,
|
||||
2022).
|
||||
- **Sample size**: coverage estimates become reliable with approximately
|
||||
100+ isolates. For smaller datasets, consider pooling data from
|
||||
multiple sites, but only after verifying that pathogen distributions
|
||||
are sufficiently similar (Bielicki *et al.*, 2016).
|
||||
- **Culture request bias**: WISCA is only as good as the data it is
|
||||
based on. If cultures are selectively requested (e.g. only after
|
||||
treatment failure), the dataset will be biased towards resistant
|
||||
isolates. A robust culture policy is essential for reliable estimates.
|
||||
|
||||
## Limitations
|
||||
|
||||
- It assumes your data are representative
|
||||
- No adjustment for patient-level covariates, although these could be
|
||||
passed onto the `syndromic_group` argument
|
||||
- WISCA does not model resistance over time, you might want to use
|
||||
`tidymodels` for that, for which we [wrote a basic
|
||||
- It assumes your data are representative of the patient population you
|
||||
are treating
|
||||
- No direct adjustment for patient-level covariates, although these can
|
||||
be passed onto the `syndromic_group` argument for stratification
|
||||
- WISCA does not model resistance trends over time; for that, you might
|
||||
want to use `tidymodels`, for which we [wrote a basic
|
||||
introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
|
||||
|
||||
## Summary
|
||||
|
||||
WISCA enables:
|
||||
|
||||
- Empirical regimen comparison,
|
||||
- Syndrome-specific coverage estimation,
|
||||
- Fully probabilistic interpretation.
|
||||
- **Empirical regimen comparison**, answering the clinician’s actual
|
||||
question
|
||||
- **Syndrome-specific coverage estimation**, stratifiable by any
|
||||
clinical variable
|
||||
- **Fully probabilistic interpretation**, with credible intervals that
|
||||
honestly communicate uncertainty
|
||||
|
||||
It is available in the `AMR` package via either:
|
||||
|
||||
@@ -265,10 +348,26 @@ wisca(...)
|
||||
antibiogram(..., wisca = TRUE)
|
||||
```
|
||||
|
||||
## Reference
|
||||
## References
|
||||
|
||||
Bielicki, JA, et al. (2016). *Selecting appropriate empirical antibiotic
|
||||
regimens for paediatric bloodstream infections: application of a
|
||||
Bayesian decision model to local and pooled antimicrobial resistance
|
||||
surveillance data.* **J Antimicrob Chemother**. 71(3):794-802.
|
||||
<https://doi.org/10.1093/jac/dkv397>
|
||||
1. Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
|
||||
Demonstration of the weighted-incidence syndromic combination
|
||||
antibiogram: an empiric prescribing decision aid. *Infect Control
|
||||
Hosp Epidemiol.* 2012;33(4):381-388.
|
||||
<https://doi.org/10.1086/664768>
|
||||
2. Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
|
||||
Weighted-incidence syndromic combination antibiograms to guide
|
||||
empiric treatment of critical care infections: a retrospective
|
||||
cohort study. *Crit Care.* 2014;18(3):R112.
|
||||
<https://doi.org/10.1186/cc13901>
|
||||
3. Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
|
||||
Selecting appropriate empirical antibiotic regimens for paediatric
|
||||
bloodstream infections: application of a Bayesian decision model to
|
||||
local and pooled antimicrobial resistance surveillance data. *J
|
||||
Antimicrob Chemother.* 2016;71(3):794-802.
|
||||
<https://doi.org/10.1093/jac/dkv397>
|
||||
4. Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
|
||||
prescribing in pediatric bloodstream infections: a potential
|
||||
application of weighted-incidence syndromic combination antibiograms
|
||||
(WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
|
||||
<https://doi.org/10.1080/14787210.2021.1967145>
|
||||
|
||||
@@ -12,8 +12,8 @@
|
||||
<link rel="icon" sizes="any" href="../favicon.ico">
|
||||
<link rel="manifest" href="../site.webmanifest">
|
||||
<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
|
||||
<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
@@ -80,7 +80,7 @@
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
|
||||
|
||||
<h4 data-toc-skip class="date">02 May 2026</h4>
|
||||
<h4 data-toc-skip class="date">23 June 2026</h4>
|
||||
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>datasets.Rmd</code></div>
|
||||
@@ -104,42 +104,43 @@ available in Python.</p>
|
||||
<h2 id="microorganisms-full-microbial-taxonomy">
|
||||
<code>microorganisms</code>: Full Microbial Taxonomy<a class="anchor" aria-label="anchor" href="#microorganisms-full-microbial-taxonomy"></a>
|
||||
</h2>
|
||||
<p>A data set with 78 679 rows and 26 columns, containing the following
|
||||
column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>kingdom</em>,
|
||||
<em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>,
|
||||
<em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>,
|
||||
<em>ref</em>, <em>oxygen_tolerance</em>, <em>source</em>, <em>lpsn</em>,
|
||||
<p>A data set with 96 982 rows and 28 columns, containing the following
|
||||
column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>domain</em>,
|
||||
<em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>,
|
||||
<em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>,
|
||||
<em>rank</em>, <em>ref</em>, <em>oxygen_tolerance</em>,
|
||||
<em>morphology</em>, <em>source</em>, <em>lpsn</em>,
|
||||
<em>lpsn_parent</em>, <em>lpsn_renamed_to</em>, <em>mycobank</em>,
|
||||
<em>mycobank_parent</em>, <em>mycobank_renamed_to</em>, <em>gbif</em>,
|
||||
<em>gbif_parent</em>, <em>gbif_renamed_to</em>, <em>prevalence</em>, and
|
||||
<em>snomed</em>.</p>
|
||||
<p>This data set is in R available as <code>microorganisms</code>, after
|
||||
you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 18 September 2025 12:58:34 UTC. Find more info
|
||||
<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds" class="external-link">original
|
||||
R Data Structure (RDS) file</a> (1.8 MB)<br>
|
||||
R Data Structure (RDS) file</a> (2.2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt" class="external-link">tab-separated
|
||||
text file</a> (17.7 MB)<br>
|
||||
text file</a> (23.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (8.8 MB)<br>
|
||||
Excel workbook</a> (11.4 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather" class="external-link">Apache
|
||||
Feather file</a> (8.4 MB)<br>
|
||||
Feather file</a> (11 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet" class="external-link">Apache
|
||||
Parquet file</a> (3.8 MB)<br>
|
||||
Parquet file</a> (4.6 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (28.4 MB)<br>
|
||||
SPSS Statistics data file</a> (35.2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta" class="external-link">Stata
|
||||
DTA file</a> (89.5 MB)</li>
|
||||
DTA file</a> (96.6 MB)</li>
|
||||
</ul>
|
||||
<p><strong>NOTE: The exported files for SPSS and Stata contain only the
|
||||
first 50 SNOMED codes per record, as their file size would otherwise
|
||||
@@ -157,41 +158,42 @@ all SNOMED codes as comma separated values.</p>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="center">(unknown kingdom)</td>
|
||||
<td align="center">1</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">20</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">Animalia</td>
|
||||
<td align="center">1 628</td>
|
||||
<td align="center">(unknown kingdom)</td>
|
||||
<td align="center">8</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">Animalia</td>
|
||||
<td align="center">2 015</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">Archaea</td>
|
||||
<td align="center">1 419</td>
|
||||
<td align="center">150</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">Bacillati</td>
|
||||
<td align="center">24 200</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">39 249</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">Chromista</td>
|
||||
<td align="center">178</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">Fungi</td>
|
||||
<td align="center">28 137</td>
|
||||
<td align="center">2</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>First 6 rows when filtering on genus <em>Escherichia</em>:</p>
|
||||
<table style="width:100%;" class="table">
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="4%">
|
||||
<col width="6%">
|
||||
<col width="5%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
<col width="3%">
|
||||
<col width="4%">
|
||||
<col width="4%">
|
||||
<col width="3%">
|
||||
<col width="4%">
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
@@ -199,6 +201,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<col width="2%">
|
||||
<col width="5%">
|
||||
<col width="6%">
|
||||
<col width="2%">
|
||||
<col width="1%">
|
||||
<col width="1%">
|
||||
<col width="2%">
|
||||
@@ -210,12 +213,13 @@ all SNOMED codes as comma separated values.</p>
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
<col width="2%">
|
||||
<col width="10%">
|
||||
<col width="9%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="center">mo</th>
|
||||
<th align="center">fullname</th>
|
||||
<th align="center">status</th>
|
||||
<th align="center">domain</th>
|
||||
<th align="center">kingdom</th>
|
||||
<th align="center">phylum</th>
|
||||
<th align="center">class</th>
|
||||
@@ -227,6 +231,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<th align="center">rank</th>
|
||||
<th align="center">ref</th>
|
||||
<th align="center">oxygen_tolerance</th>
|
||||
<th align="center">morphology</th>
|
||||
<th align="center">source</th>
|
||||
<th align="center">lpsn</th>
|
||||
<th align="center">lpsn_parent</th>
|
||||
@@ -246,6 +251,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia</td>
|
||||
<td align="center">accepted</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -256,6 +262,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">genus</td>
|
||||
<td align="center">Castellani et al., 1919</td>
|
||||
<td align="center">facultative anaerobe</td>
|
||||
<td align="center">rods</td>
|
||||
<td align="center">LPSN</td>
|
||||
<td align="center">515602</td>
|
||||
<td align="center">482</td>
|
||||
@@ -263,8 +270,8 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">11158430</td>
|
||||
<td align="center">CS33H</td>
|
||||
<td align="center">CRYWR</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">1</td>
|
||||
<td align="center">407310004, 407251000, 407281008, …</td>
|
||||
@@ -274,6 +281,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia adecarboxylata</td>
|
||||
<td align="center">synonym</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -284,6 +292,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">species</td>
|
||||
<td align="center">Leclerc, 1962</td>
|
||||
<td align="center">likely facultative anaerobe</td>
|
||||
<td align="center">rods</td>
|
||||
<td align="center">LPSN</td>
|
||||
<td align="center">776052</td>
|
||||
<td align="center">515602</td>
|
||||
@@ -291,9 +300,9 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">CS33J</td>
|
||||
<td align="center">CS33H</td>
|
||||
<td align="center">3SVX6</td>
|
||||
<td align="center">1</td>
|
||||
<td align="center"></td>
|
||||
</tr>
|
||||
@@ -302,6 +311,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia albertii</td>
|
||||
<td align="center">accepted</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -312,6 +322,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">species</td>
|
||||
<td align="center">Huys et al., 2003</td>
|
||||
<td align="center">facultative anaerobe</td>
|
||||
<td align="center">rods</td>
|
||||
<td align="center">LPSN</td>
|
||||
<td align="center">776053</td>
|
||||
<td align="center">515602</td>
|
||||
@@ -319,8 +330,8 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">5427575</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">3BGTB</td>
|
||||
<td align="center">CS33H</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">1</td>
|
||||
<td align="center">419388003</td>
|
||||
@@ -330,6 +341,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia blattae</td>
|
||||
<td align="center">synonym</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -340,6 +352,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">species</td>
|
||||
<td align="center">Burgess et al., 1973</td>
|
||||
<td align="center">likely facultative anaerobe</td>
|
||||
<td align="center">rods</td>
|
||||
<td align="center">LPSN</td>
|
||||
<td align="center">776056</td>
|
||||
<td align="center">515602</td>
|
||||
@@ -347,9 +360,9 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">CS33K</td>
|
||||
<td align="center">CS33H</td>
|
||||
<td align="center">4X4P7</td>
|
||||
<td align="center">1</td>
|
||||
<td align="center"></td>
|
||||
</tr>
|
||||
@@ -358,6 +371,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia coli</td>
|
||||
<td align="center">accepted</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -368,6 +382,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">species</td>
|
||||
<td align="center">Castellani et al., 1919</td>
|
||||
<td align="center">facultative anaerobe</td>
|
||||
<td align="center">rods</td>
|
||||
<td align="center">LPSN</td>
|
||||
<td align="center">776057</td>
|
||||
<td align="center">515602</td>
|
||||
@@ -375,8 +390,8 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">11286021</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">NT3L7</td>
|
||||
<td align="center">CS33H</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">1</td>
|
||||
<td align="center">1095001000112106, 715307006, 737528008, …</td>
|
||||
@@ -386,6 +401,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">Escherichia coli coli</td>
|
||||
<td align="center">accepted</td>
|
||||
<td align="center">Bacteria</td>
|
||||
<td align="center">Pseudomonadati</td>
|
||||
<td align="center">Pseudomonadota</td>
|
||||
<td align="center">Gammaproteobacteria</td>
|
||||
<td align="center">Enterobacterales</td>
|
||||
@@ -396,6 +412,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center">subspecies</td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">GBIF</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">776057</td>
|
||||
@@ -404,7 +421,7 @@ all SNOMED codes as comma separated values.</p>
|
||||
<td align="center"></td>
|
||||
<td align="center"></td>
|
||||
<td align="center">12233256</td>
|
||||
<td align="center">11286021</td>
|
||||
<td align="center">NT3L7</td>
|
||||
<td align="center"></td>
|
||||
<td align="center">1</td>
|
||||
<td align="center"></td>
|
||||
@@ -424,8 +441,8 @@ column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>a
|
||||
<em>iv_ddd</em>, <em>iv_units</em>, and <em>loinc</em>.</p>
|
||||
<p>This data set is in R available as <code>antimicrobials</code>, after
|
||||
you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 2 May 2026 12:56:26 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
|
||||
<p>It was last updated on 23 June 2026 12:38:59 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
@@ -595,14 +612,14 @@ inhibitors</td>
|
||||
<code>clinical_breakpoints</code>: Interpretation from MIC values
|
||||
& 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 45 730 rows and 14 columns, containing the following
|
||||
<p>A data set with 45 555 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 2 April 2026 09:42:19 UTC. Find more info
|
||||
<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/clinical_breakpoints.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -620,7 +637,7 @@ Excel workbook</a> (2.7 MB)<br>
|
||||
Feather file</a> (2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet" class="external-link">Apache
|
||||
Parquet file</a> (0.2 MB)<br>
|
||||
Parquet file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (7.5 MB)<br>
|
||||
@@ -784,13 +801,13 @@ DTA file</a> (12.6 MB)</li>
|
||||
<code>microorganisms.groups</code>: Species Groups and
|
||||
Microbiological Complexes<a class="anchor" aria-label="anchor" href="#microorganisms-groups-species-groups-and-microbiological-complexes"></a>
|
||||
</h2>
|
||||
<p>A data set with 534 rows and 4 columns, containing the following
|
||||
<p>A data set with 530 rows and 4 columns, containing the following
|
||||
column names:<br><em>mo_group</em>, <em>mo</em>, <em>mo_group_name</em>, and
|
||||
<em>mo_name</em>.</p>
|
||||
<p>This data set is in R available as
|
||||
<code>microorganisms.groups</code>, after you load the <code>AMR</code>
|
||||
package.</p>
|
||||
<p>It was last updated on 26 March 2025 16:19:17 UTC. Find more info
|
||||
<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.groups.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -802,7 +819,7 @@ R Data Structure (RDS) file</a> (6 kB)<br>
|
||||
text file</a> (50 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (20 kB)<br>
|
||||
Excel workbook</a> (19 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather" class="external-link">Apache
|
||||
Feather file</a> (19 kB)<br>
|
||||
@@ -811,10 +828,10 @@ Feather file</a> (19 kB)<br>
|
||||
Parquet file</a> (13 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (65 kB)<br>
|
||||
SPSS Statistics data file</a> (64 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta" class="external-link">Stata
|
||||
DTA file</a> (83 kB)</li>
|
||||
DTA file</a> (82 kB)</li>
|
||||
</ul>
|
||||
<p><strong>Example content</strong></p>
|
||||
<table class="table">
|
||||
@@ -876,11 +893,11 @@ DTA file</a> (83 kB)</li>
|
||||
<code>intrinsic_resistant</code>: Intrinsic Bacterial
|
||||
Resistance<a class="anchor" aria-label="anchor" href="#intrinsic_resistant-intrinsic-bacterial-resistance"></a>
|
||||
</h2>
|
||||
<p>A data set with 285 928 rows and 2 columns, containing the following
|
||||
<p>A data set with 294 079 rows and 2 columns, containing the following
|
||||
column names:<br><em>mo</em> and <em>ab</em>.</p>
|
||||
<p>This data set is in R available as <code>intrinsic_resistant</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 22 April 2026 06:16:44 UTC. Find more info
|
||||
<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/intrinsic_resistant.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -889,10 +906,10 @@ here</a>.</p>
|
||||
R Data Structure (RDS) file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt" class="external-link">tab-separated
|
||||
text file</a> (10.6 MB)<br>
|
||||
text file</a> (10.9 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (3.3 MB)<br>
|
||||
Excel workbook</a> (3.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather" class="external-link">Apache
|
||||
Feather file</a> (2.5 MB)<br>
|
||||
@@ -901,10 +918,10 @@ Feather file</a> (2.5 MB)<br>
|
||||
Parquet file</a> (0.3 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (15.5 MB)<br>
|
||||
SPSS Statistics data file</a> (16 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta" class="external-link">Stata
|
||||
DTA file</a> (27.5 MB)</li>
|
||||
DTA file</a> (28.6 MB)</li>
|
||||
</ul>
|
||||
<p><strong>Example content</strong></p>
|
||||
<p>Example rows when filtering on <em>Enterobacter cloacae</em>:</p>
|
||||
@@ -1810,17 +1827,17 @@ set here</a>.</p>
|
||||
<h2 id="microorganisms-codes-common-laboratory-codes">
|
||||
<code>microorganisms.codes</code>: Common Laboratory Codes<a class="anchor" aria-label="anchor" href="#microorganisms-codes-common-laboratory-codes"></a>
|
||||
</h2>
|
||||
<p>A data set with 6 050 rows and 2 columns, containing the following
|
||||
<p>A data set with 6 029 rows and 2 columns, containing the following
|
||||
column names:<br><em>code</em> and <em>mo</em>.</p>
|
||||
<p>This data set is in R available as <code>microorganisms.codes</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 30 March 2026 08:01:49 UTC. Find more info
|
||||
<p>It was last updated on 22 June 2026 23:38:13 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.codes.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds" class="external-link">original
|
||||
R Data Structure (RDS) file</a> (28 kB)<br>
|
||||
R Data Structure (RDS) file</a> (27 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt" class="external-link">tab-separated
|
||||
text file</a> (0.1 MB)<br>
|
||||
@@ -1832,7 +1849,7 @@ Excel workbook</a> (98 kB)<br>
|
||||
Feather file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet" class="external-link">Apache
|
||||
Parquet file</a> (69 kB)<br>
|
||||
Parquet file</a> (68 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (0.2 MB)<br>
|
||||
|
||||
@@ -15,44 +15,44 @@ laboratory information systems.
|
||||
|
||||
## `microorganisms`: Full Microbial Taxonomy
|
||||
|
||||
A data set with 78 679 rows and 26 columns, containing the following
|
||||
A data set with 96 982 rows and 28 columns, containing the following
|
||||
column names:
|
||||
*mo*, *fullname*, *status*, *kingdom*, *phylum*, *class*, *order*,
|
||||
*family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
|
||||
*oxygen_tolerance*, *source*, *lpsn*, *lpsn_parent*, *lpsn_renamed_to*,
|
||||
*mycobank*, *mycobank_parent*, *mycobank_renamed_to*, *gbif*,
|
||||
*gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
|
||||
*mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
|
||||
*order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
|
||||
*oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
|
||||
*lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
|
||||
*gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
|
||||
|
||||
This data set is in R available as `microorganisms`, after you load the
|
||||
`AMR` package.
|
||||
|
||||
It was last updated on 18 September 2025 12:58:34 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this [data set
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
|
||||
(1.8 MB)
|
||||
(2.2 MB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
|
||||
(17.7 MB)
|
||||
(23.1 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
|
||||
(8.8 MB)
|
||||
(11.4 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
|
||||
(8.4 MB)
|
||||
(11 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
|
||||
(3.8 MB)
|
||||
(4.6 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
|
||||
(28.4 MB)
|
||||
(35.2 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
|
||||
(89.5 MB)
|
||||
(96.6 MB)
|
||||
|
||||
**NOTE: The exported files for SPSS and Stata contain only the first 50
|
||||
SNOMED codes per record, as their file size would otherwise exceed 100
|
||||
@@ -69,23 +69,23 @@ Included (sub)species per taxonomic kingdom:
|
||||
|
||||
| Kingdom | Number of (sub)species |
|
||||
|:-----------------:|:----------------------:|
|
||||
| (unknown kingdom) | 1 |
|
||||
| Animalia | 1 628 |
|
||||
| Archaea | 1 419 |
|
||||
| Bacteria | 39 249 |
|
||||
| Chromista | 178 |
|
||||
| Fungi | 28 137 |
|
||||
| | 20 |
|
||||
| (unknown kingdom) | 8 |
|
||||
| Animalia | 2 015 |
|
||||
| Archaea | 150 |
|
||||
| Bacillati | 24 200 |
|
||||
| Bacteria | 2 |
|
||||
|
||||
First 6 rows when filtering on genus *Escherichia*:
|
||||
|
||||
| mo | fullname | status | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | LPSN | 515602 | 482 | | | | | | 11158430 | | 1 | 407310004, 407251000, 407281008, … |
|
||||
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | LPSN | 776052 | 515602 | 777447 | | | | | | | 1 | |
|
||||
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | LPSN | 776053 | 515602 | | | | | 5427575 | | | 1 | 419388003 |
|
||||
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | LPSN | 776056 | 515602 | 788468 | | | | | | | 1 | |
|
||||
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | LPSN | 776057 | 515602 | | | | | 11286021 | | | 1 | 1095001000112106, 715307006, 737528008, … |
|
||||
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | GBIF | | 776057 | | | | | 12233256 | 11286021 | | 1 | |
|
||||
| mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 | | | | | CS33H | CRYWR | | 1 | 407310004, 407251000, 407281008, … |
|
||||
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 | | | | CS33J | CS33H | 3SVX6 | 1 | |
|
||||
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 | | | | | 3BGTB | CS33H | | 1 | 419388003 |
|
||||
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 | | | | CS33K | CS33H | 4X4P7 | 1 | |
|
||||
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 | | | | | NT3L7 | CS33H | | 1 | 1095001000112106, 715307006, 737528008, … |
|
||||
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | | GBIF | | 776057 | | | | | 12233256 | NT3L7 | | 1 | |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
@@ -100,8 +100,8 @@ names:
|
||||
This data set is in R available as `antimicrobials`, after you load the
|
||||
`AMR` package.
|
||||
|
||||
It was last updated on 2 May 2026 12:56:26 UTC. Find more info about the
|
||||
contents, (scientific) source, and structure of this [data set
|
||||
It was last updated on 23 June 2026 12:38:59 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/antimicrobials.html).
|
||||
|
||||
**Direct download links:**
|
||||
@@ -147,7 +147,7 @@ as comma separated values.
|
||||
|
||||
## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
|
||||
|
||||
A data set with 45 730 rows and 14 columns, containing the following
|
||||
A data set with 45 555 rows and 14 columns, containing the following
|
||||
column names:
|
||||
*guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
|
||||
*ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
|
||||
@@ -156,7 +156,7 @@ column names:
|
||||
This data set is in R available as `clinical_breakpoints`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 2 April 2026 09:42:19 UTC. Find more info about
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/clinical_breakpoints.html).
|
||||
|
||||
@@ -176,7 +176,7 @@ here](https://amr-for-r.org/reference/clinical_breakpoints.html).
|
||||
(2 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
|
||||
(0.2 MB)
|
||||
(0.1 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
|
||||
(7.5 MB)
|
||||
@@ -199,14 +199,14 @@ here](https://amr-for-r.org/reference/clinical_breakpoints.html).
|
||||
|
||||
## `microorganisms.groups`: Species Groups and Microbiological Complexes
|
||||
|
||||
A data set with 534 rows and 4 columns, containing the following column
|
||||
A data set with 530 rows and 4 columns, containing the following column
|
||||
names:
|
||||
*mo_group*, *mo*, *mo_group_name*, and *mo_name*.
|
||||
|
||||
This data set is in R available as `microorganisms.groups`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 26 March 2025 16:19:17 UTC. Find more info about
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.groups.html).
|
||||
|
||||
@@ -220,7 +220,7 @@ here](https://amr-for-r.org/reference/microorganisms.groups.html).
|
||||
(50 kB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
|
||||
(20 kB)
|
||||
(19 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
|
||||
(19 kB)
|
||||
@@ -229,10 +229,10 @@ here](https://amr-for-r.org/reference/microorganisms.groups.html).
|
||||
(13 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
|
||||
(65 kB)
|
||||
(64 kB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
|
||||
(83 kB)
|
||||
(82 kB)
|
||||
|
||||
**Example content**
|
||||
|
||||
@@ -249,14 +249,14 @@ here](https://amr-for-r.org/reference/microorganisms.groups.html).
|
||||
|
||||
## `intrinsic_resistant`: Intrinsic Bacterial Resistance
|
||||
|
||||
A data set with 285 928 rows and 2 columns, containing the following
|
||||
A data set with 294 079 rows and 2 columns, containing the following
|
||||
column names:
|
||||
*mo* and *ab*.
|
||||
|
||||
This data set is in R available as `intrinsic_resistant`, after you load
|
||||
the `AMR` package.
|
||||
|
||||
It was last updated on 22 April 2026 06:16:44 UTC. Find more info about
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/intrinsic_resistant.html).
|
||||
|
||||
@@ -267,10 +267,10 @@ here](https://amr-for-r.org/reference/intrinsic_resistant.html).
|
||||
(0.1 MB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
|
||||
(10.6 MB)
|
||||
(10.9 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
|
||||
(3.3 MB)
|
||||
(3.1 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
|
||||
(2.5 MB)
|
||||
@@ -279,10 +279,10 @@ here](https://amr-for-r.org/reference/intrinsic_resistant.html).
|
||||
(0.3 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
|
||||
(15.5 MB)
|
||||
(16 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
|
||||
(27.5 MB)
|
||||
(28.6 MB)
|
||||
|
||||
**Example content**
|
||||
|
||||
@@ -464,14 +464,14 @@ here](https://amr-for-r.org/reference/example_isolates_unclean.html).
|
||||
|
||||
## `microorganisms.codes`: Common Laboratory Codes
|
||||
|
||||
A data set with 6 050 rows and 2 columns, containing the following
|
||||
A data set with 6 029 rows and 2 columns, containing the following
|
||||
column names:
|
||||
*code* and *mo*.
|
||||
|
||||
This data set is in R available as `microorganisms.codes`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 30 March 2026 08:01:49 UTC. Find more info about
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.codes.html).
|
||||
|
||||
@@ -479,7 +479,7 @@ here](https://amr-for-r.org/reference/microorganisms.codes.html).
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
|
||||
(28 kB)
|
||||
(27 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
|
||||
(0.1 MB)
|
||||
@@ -491,7 +491,7 @@ here](https://amr-for-r.org/reference/microorganisms.codes.html).
|
||||
(0.1 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
|
||||
(69 kB)
|
||||
(68 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
|
||||
(0.2 MB)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/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>
|
||||
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.1.9057</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9061</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
|
||||
7
deps/bootstrap-5.3.1/bootstrap.bundle.min.js
vendored
7
deps/bootstrap-5.3.1/bootstrap.bundle.min.js
vendored
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
5
deps/bootstrap-5.3.1/bootstrap.min.css
vendored
5
deps/bootstrap-5.3.1/bootstrap.min.css
vendored
File diff suppressed because one or more lines are too long
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
7
deps/bootstrap-5.3.8/bootstrap.bundle.min.js
vendored
Normal file
7
deps/bootstrap-5.3.8/bootstrap.bundle.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
1
deps/bootstrap-5.3.8/bootstrap.bundle.min.js.map
vendored
Normal file
1
deps/bootstrap-5.3.8/bootstrap.bundle.min.js.map
vendored
Normal file
File diff suppressed because one or more lines are too long
5
deps/bootstrap-5.3.8/bootstrap.min.css
vendored
Normal file
5
deps/bootstrap-5.3.8/bootstrap.min.css
vendored
Normal file
File diff suppressed because one or more lines are too long
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user