Abstract
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.
Highlights
Continuous glucose monitoring (CGM) technology has transformed diabetes care over the past 15 years by allowing clinicians to measure free-living glucose patterns
With recent reports detailing the benefits of CGM time in range metrics as predictive of long-term vascular outcomes [2] and as an indicator of glucose management or estimated hemoglobin A1c (HbA1c) [3], CGM use will likely continue to increase in both research and clinical settings
The American Diabetes Association (ADA) recently proposed a set of key metrics for reporting CGM data [8], all of which are calculated by our code, in addition to the glucose management indicator (GMI) [3], time in range [2], and other variables proposed by Hernandez et al [4]
Summary
Continuous glucose monitoring (CGM) technology has transformed diabetes care over the past 15 years by allowing clinicians to measure free-living glucose patterns. During this period, CGM use has increased from < 5% of patients to almost 50% in some age groups [1]. With recent reports detailing the benefits of CGM time in range metrics as predictive of long-term vascular outcomes [2] and as an indicator of glucose management or estimated hemoglobin A1c (HbA1c) [3], CGM use will likely continue to increase in both research and clinical settings. Despite the increasing use of CGM for treatment and research, a standardized, free, open-source approach to data management and analysis is lacking [4]. It may be difficult to compare results obtained using different CGM devices and to understand the sources of variability
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