A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings
Authors: Klonoff David C. , Wang Jing , Rodbard David , Kohn Michael A. , Li Chengdong , Liepmann Dorian , Kerr David , Ahn David , Peters Anne L. , Umpierrez Guillermo E. , Seley Jane Jeffrie , Xu Nicole Y. , Nguyen Kevin T. , Simonson Gregg , Agus Michael S. D. , Al-Sofiani Mohammed E. , Armaiz-Pena Gustavo , Bailey Timothy S. , Basu Ananda , Battelino Tadej , Bekele Sewagegn Yeshiwas , Benhamou Pierre-Yves , Bequette B. Wayne , Blevins Thomas , Breton Marc D. , Castle Jessica R. , Chase James Geoffrey , Chen Kong Y. , Choudhary Pratik , Clements Mark A. , Close Kelly L. , Cook Curtiss B. , Danne Thomas , Doyle Francis J. , Drincic Angela , Dungan Kathleen M. , Edelman Steven V. , Ejskjaer Niels , Espinoza Juan C. , Fleming G. Alexander , Forlenza Gregory P. , Freckmann Guido , Galindo Rodolfo J. , Gomez Ana Maria , Gutow Hanna A. , Heinemann Lutz , Hirsch Irl B. , Hoang Thanh D. , Hovorka Roman , Jendle Johan H. , Ji Linong , Joshi Shashank R. , Joubert Michael , Koliwad Suneil K. , Lal Rayhan A. , Lansang M. Cecilia , Lee Wei-An (Andy) , Leelarathna Lalantha , Leiter Lawrence A. , Lind Marcus , Litchman Michelle L. , Mader Julia K. , Mahoney Katherine M. , Mankovsky Boris , Masharani Umesh , Mathioudakis Nestoras N. , Mayorov Alexander , Messler Jordan , Miller Joshua D. , Mohan Viswanathan , Nichols James H. , Nørgaard Kirsten , O’Neal David N. , Pasquel Francisco J. , Philis-Tsimikas Athena , Pieber Thomas , Phillip Moshe , Polonsky William H. , Pop-Busui Rodica , Rayman Gerry , Rhee Eun-Jung , Russell Steven J. , Shah Viral N. , Sherr Jennifer L. , Sode Koji , Spanakis Elias K. , Wake Deborah J. , Waki Kayo , Wallia Amisha , Weinberg Melissa E. , Wolpert Howard , Wright Eugene E. , Zilbermint Mihail , Kovatchev Boris 
Published: March 2022

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.