Journal of Statistical Software · 2022 · 50 citations · 17 references
Veterinary MedicineMicrobiologyAntimicrobial ResistanceDiagnostic MicrobiologyMedical MicrobiologyAntibiotic ResistanceAntimicrobial Resistance ControlAntimicrobial Susceptibility TestingPathogen DetectionResistance EpidemiologyResistance SurveillanceAntimicrobial Resistance Trend AnalysisLaboratory Data AcquisitionLaboratory Information System IntegrationLaboratory Reporting SystemsMicrobiological DiagnosticsMicrobiological SurveillancePathogen Resistance TestingResistance Data Curation
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. It is known that extensive efforts in data cleaning and validation are required when working with data from laboratory information systems. Furthermore, the global spread and relevance 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 provided by the World Health Organization).
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Hadley Wickham, Mara Averick, Jennifer Bryan et al. · The Journal of Open Source Software · 2019 · 21.6K citations · Full text
Anna-Pelagia Magiorakos, Arjun Srinivasan, Roberta B. Carey et al. · Clinical Microbiology and Infection · 2011 · 14K citations