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AMR – An R Package for Working with Antimicrobial Resistance Data

View ORCID ProfileMatthijs S. Berends, View ORCID ProfileChristian F. Luz, View ORCID ProfileAlexander W. Friedrich, View ORCID ProfileBhanu N.M. Sinha, View ORCID ProfileCasper J. Albers, View ORCID ProfileCorinna Glasner
doi: https://doi.org/10.1101/810622
Matthijs S. Berends
1Certe Medical Diagnostics and Advice, Van Swietenlaan 2, 9728 NZ Groningen, The Netherlands , University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9713 GZ Groningen, The Netherlands, E-mail: ,
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  • For correspondence: m.berends@certe.nl m.berends@certe.nl m.s.berends@umcg.nl
Christian F. Luz
2University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Alexander W. Friedrich
3University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1,9713 GZ Groningen, The Netherlands
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Bhanu N.M. Sinha
4University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Casper J. Albers
5University of Groningen, Heymans Institute for Psychological Research, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands
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Corinna Glasner
6University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Abstract

Antimicrobial resistance is an increasing threat to global health. Evidence for this trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and guidelines are in place for this process as well as for reporting data on (inter-)national levels. However, there is a gap in the availability of standardized and reproducible tools for working with laboratory data to produce the required reports. Of data coming from laboratory information systems, it is known that extensive efforts in data cleaning and validation are required. Furthermore, the global nature of antimicrobial resistance demands to incorporate international reference data in the analysis process.

In this paper, we introduce the AMR package for R that aims at closing this gap by providing tools to simplify antimicrobial resistance data cleaning and analysis, while incorporating international guidelines and scientifically reliable reference data. The AMR package enables standardized and reproducible antimicrobial resistance analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. The AMR package works independently of any laboratory information system and provides several functions to integrate into international workflows (e.g. WHONET software by the World Health Organization).

Footnotes

  • Section 4.2 updated to include newly added functionalities to the mdro() function, that can now apply the MDR/XDR/PDR definitions as proposed by Magiorakos et al. (2012, DOI 10.1111/j.1469-0691.2011.03570.x). For her input on adding and testing this implementation, the authors thank Dr. Sofia Ny in the Acknowledgement section. Furthermore, the German national guideline for determination of MDRO's (Mueller et al., 2015, DOI 10.1186/s13756-015-0047-6) was added in section 4.2 as well.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 08, 2019.
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AMR – An R Package for Working with Antimicrobial Resistance Data
Matthijs S. Berends, Christian F. Luz, Alexander W. Friedrich, Bhanu N.M. Sinha, Casper J. Albers, Corinna Glasner
bioRxiv 810622; doi: https://doi.org/10.1101/810622
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AMR – An R Package for Working with Antimicrobial Resistance Data
Matthijs S. Berends, Christian F. Luz, Alexander W. Friedrich, Bhanu N.M. Sinha, Casper J. Albers, Corinna Glasner
bioRxiv 810622; doi: https://doi.org/10.1101/810622

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