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A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

  • 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, 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 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
  • Sutter Health
  • Florida State University
  • Biomedical Informatics Consultants LLC
  • University of California at San Francisco
  • University of California at Berkeley
  • Sansum Diabetes Research Institute
  • Hoag Memorial Hospital
  • University of Southern California
  • Emory University
  • Cornell University
  • Diabetes Technology Society
  • International Diabetes Center
  • Harvard University
  • King Saud University
  • Johns Hopkins University
  • University of Texas Health Science Center at San Antonio
  • AMCR Institute
  • University of Virginia
  • University of Ljubljana
  • Addis Ababa University
  • CHU de Grenoble
  • Rensselaer Polytechnic Institute
  • Texas Diabetes and Endocrinology
  • Oregon Health and Science University
  • University of Canterbury
  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
  • University of Leicester
  • Children’s Mercy Hospital
  • Close Concerns
  • Mayo Clinic Scottsdale, AZ
  • Hannover Medical School
  • University of Nebraska Omaha
  • Ohio State University
  • University of California at San Diego
  • Aalborg University
  • Harpers Ferry
  • University of Colorado Boulder
  • Institute for Diabetes-Technology GmbH
  • Science Consulting in Diabetes GmbH
  • University of Washington
  • Walter Reed Army Institute of Research
  • University of Cambridge
  • Örebro University
  • Peking University
  • Joshi Clinic
  • Université de Caen
  • Stanford University
  • Cleveland Clinic Foundation
  • Cleveland Clinic Lerner College of Medicine of Case Western Reserve University
  • Los Angeles County USC Medical Center
  • Manchester University NHS Foundation Trust
  • Li Ka Shing Knowledge Institute
  • University of Gothenburg
  • University of Utah
  • Medical University of Graz
  • Shupyk National Healthcare University of Ukraine
  • National Medical Research Center for Endocrinology
  • Morton Plant Mease Health Care
  • Stony Brook University
  • Dr. Mohan’s Diabetes Specialities Centre
  • Madras Medical College
  • Vanderbilt University Medical Center
  • Steno Diabetes Center Copenhagen
  • University of Melbourne
  • Scripps Whittier Diabetes Institute
  • Tel Aviv University
  • Behavioral Diabetes Institute
  • University of Michigan, Ann Arbor
  • East Suffolk and North Essex NHS Foundation Trust
  • Kangbuk Samsung Hospital
  • Massachusetts General Hospital
  • Yale University
  • University of North Carolina
  • North Carolina State University
  • University of Maryland, Baltimore
  • University of Edinburgh
  • The University of Tokyo
  • Northwestern University
  • California Pacific Medical Center
  • Boston University
  • Charlotte AHEC
  • Johns Hopkins Community Physicians

Research output: Contribution to journalArticlepeer-review

226 Scopus citations

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.

Original languageEnglish
Pages (from-to)1226-1242
Number of pages17
JournalJournal of Diabetes Science and Technology
Volume17
Issue number5
DOIs
StatePublished - Sep 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • ambulatory glucose profile
  • composite metric
  • continuous glucose monitor
  • diabetes
  • glycemia risk index
  • hyperglycemia
  • hypoglycemia
  • time in range

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