cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data


Autoři: Tim Vigers aff001;  Christine L. Chan aff001;  Janet Snell-Bergeon aff002;  Petter Bjornstad aff001;  Philip S. Zeitler aff001;  Gregory Forlenza aff002;  Laura Pyle aff001
Působiště autorů: Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, Colorado, United States of America aff001;  Barbara Davis Center, University of Colorado School of Medicine, Aurora, Colorado, United States of America aff002;  Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0216851

Souhrn

Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the various commercial systems still report CGM data in disparate, non-standard ways. Accordingly, there is a need for a standardized, free, open-source approach to CGM data management and analysis. A package titled cgmanalysis was developed in the free programming language R to provide a rapid, easy, and consistent methodology for CGM data management, summary measure calculation, and descriptive analysis. Variables calculated by our package compare well to those generated by various CGM software, and our functions provide a more comprehensive list of summary measures available to clinicians and researchers. Consistent handling of CGM data using our R package may facilitate collaboration between research groups and contribute to a better understanding of free-living glucose patterns.

Klíčová slova:

Blood sugar – Data management – Diabetes mellitus – Glucose – HbA1c – Software tools – Open source software – Programming languages


Zdroje

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PLOS One


2019 Číslo 10

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