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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
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<li>
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<a href="../articles/AMR.html">
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<span class="fa fa-directions"></span>
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Conduct AMR analysis
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<a href="../articles/resistance_predict.html">
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Predict antimicrobial resistance
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Data sets for download / own use
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<a href="../articles/PCA.html">
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Conduct principal component analysis for AMR
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Determine multi-drug resistance (MDR)
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Work with WHONET data
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Import data from SPSS/SAS/Stata
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Apply EUCAST rules
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<a href="../reference/mo_property.html">
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<span class="fa fa-bug"></span>
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Get properties of a microorganism
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Get properties of an antibiotic
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</a>
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<a href="../articles/benchmarks.html">
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<span class="fa fa-shipping-fast"></span>
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Other: benchmarks
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</a>
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Manual
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Changelog
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Source Code
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</header><div class="row">
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<div class="col-md-9 contents">
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<div class="page-header toc-ignore">
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<h1 data-toc-skip>Welcome to the <code>AMR</code> package</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/welcome_to_AMR.Rmd" class="external-link"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
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<div class="hidden name"><code>welcome_to_AMR.Rmd</code></div>
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</div>
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<p>Note: to keep the package size as small as possible, we only included
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this vignette on CRAN. You can read more vignettes on our website about
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how to conduct AMR data analysis, determine MDRO’s, find explanation of
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EUCAST rules, and much more: <a href="https://msberends.github.io/AMR/articles/" class="uri">https://msberends.github.io/AMR/articles/</a>.</p>
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<hr>
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<p><code>AMR</code> is a free, open-source and independent R package
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(see <a href="https://msberends.github.io/AMR/#copyright">Copyright</a>)
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to simplify the analysis and prediction of Antimicrobial Resistance
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(AMR) and to work with microbial and antimicrobial data and properties,
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by using evidence-based methods. <strong>Our aim is to provide a
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standard</strong> for clean and reproducible antimicrobial resistance
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data analysis, that can therefore empower epidemiological analyses to
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continuously enable surveillance and treatment evaluation in any
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setting.</p>
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<p>After installing this package, R knows ~71,000 distinct microbial
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species and all ~570 antibiotic, antimycotic and antiviral drugs by name
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and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and
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knows all about valid R/SI and MIC values. It supports any data format,
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including WHONET/EARS-Net data.</p>
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<p>The <code>AMR</code> package is available in Danish, Dutch, English,
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French, German, Italian, Portuguese, Russian, Spanish and Swedish.
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Antimicrobial drug (group) names and colloquial microorganism names are
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provided in these languages.</p>
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<p>This package is fully independent of any other R package and works on
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Windows, macOS and Linux with all versions of R since R-3.0 (April
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2013). <strong>It was designed to work in any setting, including those
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with very limited resources</strong>. Since its first public release in
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early 2018, this package has been downloaded from more than 175
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countries.</p>
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<p>This package can be used for:</p>
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<ul>
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<li>Reference for the taxonomy of microorganisms, since the package
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contains all microbial (sub)species from the Catalogue of Life and List
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of Prokaryotic names with Standing in Nomenclature</li>
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<li>Interpreting raw MIC and disk diffusion values, based on the latest
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CLSI or EUCAST guidelines</li>
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<li>Retrieving antimicrobial drug names, doses and forms of
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administration from clinical health care records</li>
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<li>Determining first isolates to be used for AMR data analysis</li>
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<li>Calculating antimicrobial resistance</li>
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<li>Determining multi-drug resistance (MDR) / multi-drug resistant
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organisms (MDRO)</li>
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<li>Calculating (empirical) susceptibility of both mono therapy and
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combination therapies</li>
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<li>Predicting future antimicrobial resistance using regression
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models</li>
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<li>Getting properties for any microorganism (like Gram stain, species,
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genus or family)</li>
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<li>Getting properties for any antibiotic (like name, code of
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EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
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<li>Plotting antimicrobial resistance</li>
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<li>Applying EUCAST expert rules</li>
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<li>Getting SNOMED codes of a microorganism, or getting properties of a
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microorganism based on a SNOMED code</li>
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<li>Getting LOINC codes of an antibiotic, or getting properties of an
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antibiotic based on a LOINC code</li>
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<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
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translate MIC values and disk diffusion diameters to R/SI</li>
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<li>Principal component analysis for AMR</li>
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</ul>
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<p>All reference data sets (about microorganisms, antibiotics, R/SI
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interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
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publicly and freely available. We continually export our data sets to
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formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat
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files that are machine-readable and suitable for input in any software
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program, such as laboratory information systems. Please find <a href="https://msberends.github.io/AMR/articles/datasets.html">all
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download links on our website</a>, which is automatically updated with
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every code change.</p>
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<p>This R package was created for both routine data analysis and
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academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
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with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
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Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>. This
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R package formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
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10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
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10.33612/diss.192486375</a>) but is actively and durably maintained (see
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<a href="https://msberends.github.io/AMR/news/index.html">changelog)</a>)
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by two public healthcare organisations in the Netherlands.</p>
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<p></p>
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<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
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Erwin E. A. Hassing.</p>
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