Authors: David C Klonoff, Jing Wang, David Rodbard, Michael A Kohn, Chengdong Li, Dorian Liepmann, David Kerr, David Ahn, Anne L Peters, Guillermo E Umpierrez, Jane Jeffrie Seley, Nicole Y Xu, Kevin T Nguyen, Gregg Simonson, Michael S D Agus, Mohammed E Al-Sofiani, Gustavo Armaiz-Pena, Timothy S Bailey, Ananda Basu, Tadej Battelino, Sewagegn Yeshiwas Bekele, Pierre-Yves Benhamou, B Wayne Bequette, Thomas Blevins, Marc D Breton, Jessica R Castle, James Geoffrey Chase, Kong Y Chen, Pratik Choudhary, Mark A Clements, Kelly L Close, Curtiss B Cook, Thomas Danne, Francis J Doyle 3rd, Angela Drincic, Kathleen M Dungan, Steven V Edelman, Niels Ejskjaer, Juan C Espinoza, G Alexander Fleming, Gregory P Forlenza, Guido Freckmann, Rodolfo J Galindo, Ana Maria Gomez, Hanna A Gutow, Lutz Heinemann, Irl B Hirsch, Thanh D Hoang, Roman Hovorka, Johan H Jendle, Linong Ji, Shashank R Joshi, Michael Joubert, Suneil K Koliwad, Rayhan A Lal, M Cecilia Lansang, Wei-An Andy Lee, Lalantha Leelarathna, Lawrence A Leiter, Marcus Lind, Michelle L Litchman, Julia K Mader, Katherine M Mahoney, Boris Mankovsky, Umesh Masharani, Nestoras N Mathioudakis, Alexander Mayorov, Jordan Messler, Joshua D Miller, Viswanathan Mohan, James H Nichols, Kirsten Nørgaard, David N O’Neal, Francisco J Pasquel, Athena Philis-Tsimikas, Thomas Pieber, Moshe Phillip, William H Polonsky, Rodica Pop-Busui, Gerry Rayman, Eun-Jung Rhee, Steven J Russell, Viral N Shah, Jennifer L Sherr, Koji Sode, Elias K Spanakis, Deborah J Wake, Kayo Waki, Amisha Wallia, Melissa E Weinberg, Howard Wolpert, Eugene E Wright, Mihail Zilbermint, Boris Kovatchev
Abstract
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.