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2025-12-22 08:48:41 +00:00
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reference/antibiogram.html
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@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<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="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">
@@ -282,7 +282,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span><span class="co"># this is equal to:</span></span>
<span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">your_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">"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><p></p></div>
<p>WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).</p></li>
<p>WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).</p></li>
</ol></div>
<div class="section">
@@ -587,9 +587,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical 73.4% (67.6-78.6%) 92.4% (90.6-93.7%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU 57.4% (49.7-65.6%) 85% (82.1-87.6%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient 56.9% (46.9-66.7%) 74.4% (69-79.7%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical 73.5% (68.1-78.6%) 92.3% (90.8-93.8%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU 57.3% (49.8-64.9%) 84.8% (82-87.7%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient 56.8% (47-67%) 74.4% (68.6-79.7%) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
@@ -614,9 +614,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Syndromic Group |Piperacillin/tazobactam |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |:---------------|:-----------------------|</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Clinical |73.6% (68.4-79%) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |ICU |57.4% (49.7-65.4%) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Outpatient |57% (47.2-66.7%) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Clinical |73.6% (68.5-78.7%) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |ICU |57.4% (49.3-65.8%) |</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> |Outpatient |57% (47.1-67.3%) |</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Generate plots with ggplot2 or base R --------------------------------</span></span></span>