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. 2023 Nov;17(6):1527-1552.
doi: 10.1177/19322968231191104. Epub 2023 Aug 17.

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials

Elias K Spanakis  1 , Curtiss B Cook  2 , Kristen Kulasa  3 , Joseph A Aloi  4 , Lia Bally  5 , Georgia Davis  6 , Kathleen M Dungan  7 , Rodolfo J Galindo  8 , Carlos E Mendez  9 , Francisco J Pasquel  6 , Viral N Shah  10 , Guillermo E Umpierrez  6 , Rachel E Aaron  11 , Tiffany Tian  11 , Andrea M Yeung  11 , Jingtong Huang  11 , David C Klonoff  12
Affiliations

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials

Elias K Spanakis et al. J Diabetes Sci Technol. 2023 Nov.

Abstract

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

Keywords: continuous glucose monitor; glucose; glycemia risk index; hypoglycemia; insulin; metrics.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: EKS was partially supported by the VA MERIT award (#1I01CX001825) and CSP #2002 from the United States (U.S.) Department of Veterans Affairs. EKS has received unrestricted research support from Dexcom, MannKind Cooperation and Tandem (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. EKS has received fees from the Medscape and the Endocrine Society (ESAP). JAA receives research funding from Abbott Diabetes. LB has product and research support for investigator-initiated studies from Dexcom and Ypsomed in addition to speaker honoraria from Dexcom and Ypsomed. GD has received research support from Insulet and has consulted for Medscape. KMD received research support from Abbott, Dexcom, Sanofi, Viacyte, and Insulet, consulting fees from Dexcom, Eli Lilly, advisory board fees from Elsevier, and honoraria from UpToDate, Medscape, Academy for Continued Healthcare Learning, Med Learning Group, Cardiometabolic Health Congress. FJP has received research support: Dexcom, Insulet, Tandem, Ideal Medical technologies, Novo Nordisk and consults for Dexcom. FJP has received research support: Dexcom, Insulet, Tandem, Ideal Medical technologies, Novo Nordisk and consulting fees from Dexcom. GEU is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). GEU has received research support (to Emory University) from Abbott, Bayer, Dexcom and Sanofi. DCK is a consultant for Better Therapeutics, EOflow, Integrity, Lifecare, Nevro, Novo, Sanofi, and Thirdwayv. The remaining authors have no disclosures.

Figures

Figure 1.
Figure 1.
Pathophysiology of hyperglycemia in the hospital.
Figure 2.
Figure 2.
Relationship between clinical presentation (acuity, hypoglycemia risk), clinically acceptable glucose target ranges and goal hospital %TIR. Abbreviations: TIR, time in range.
Figure 3.
Figure 3.
Three different hypothetical patient glucose profiles with the same mean glucose over three days.
Figure 4.
Figure 4.
Ambulatory glucose profiles (AGP) of Abbott Libre CGM on the top and Dexcom CGM on the bottom with their available glycemic variability metrics. LibreView is a trademark of Abbott or its related companies. Top image reproduced with permission of Abbott, 2023. All rights reserved. Bottom image reproduced with permission from Dexcom. This image and data are for illustration purposes only. Abbreviations: AGP, ambulatory glucose profile; CGM, continuous glucose monitor; CV, coefficient of variability; GMI, glucose management indicator.
Figure 5.
Figure 5.
Glycemia Risk Index (GRI) grid showing five distinct zones of glycemic quality. Zone A represents the best while Zone E represents the worse glycemic quality based on exposure to hypoglycemia (X-axis) and hyperglycemia (y-axis). GRI score ranges from 0 to 100 where a lower number is better. Image is adapted from Diabetes Technology Society. https://www.diabetestechnology.org/gri/
Figure 6.
Figure 6.
Summary of FDA requirements for Integrated CGM (iCGM) Systems in Ambulatory Settings. Abbreviations: FDA, U.S. Food and Drug Administration; iCGM, Integrated Continuous Glucose Monitor.
See this image and copyright information in PMC

References

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