A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings
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A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. / 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; 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.
In: Journal of Diabetes Science and Technology, Vol. 17, No. 5, 2023, p. 1226–1242.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings
AU - Klonoff, David C.
AU - Wang, Jing
AU - Rodbard, David
AU - Kohn, Michael A.
AU - Li, Chengdong
AU - Liepmann, Dorian
AU - Kerr, David
AU - Ahn, David
AU - Peters, Anne L.
AU - Umpierrez, Guillermo E.
AU - Seley, Jane Jeffrie
AU - Xu, Nicole Y.
AU - Nguyen, Kevin T.
AU - Simonson, Gregg
AU - Agus, Michael S.D.
AU - Al-Sofiani, Mohammed E.
AU - Armaiz-Pena, Gustavo
AU - Bailey, Timothy S.
AU - Basu, Ananda
AU - Battelino, Tadej
AU - Bekele, Sewagegn Yeshiwas
AU - Benhamou, Pierre Yves
AU - Bequette, B. Wayne
AU - Blevins, Thomas
AU - Breton, Marc D.
AU - Castle, Jessica R.
AU - Chase, James Geoffrey
AU - Chen, Kong Y.
AU - Choudhary, Pratik
AU - Clements, Mark A.
AU - Close, Kelly L.
AU - Cook, Curtiss B.
AU - Danne, Thomas
AU - Doyle, Francis J.
AU - Drincic, Angela
AU - Dungan, Kathleen M.
AU - Edelman, Steven V.
AU - Ejskjaer, Niels
AU - Espinoza, Juan C.
AU - Fleming, G. Alexander
AU - Forlenza, Gregory P.
AU - Freckmann, Guido
AU - Galindo, Rodolfo J.
AU - Gomez, Ana Maria
AU - Gutow, Hanna A.
AU - Heinemann, Lutz
AU - Hirsch, Irl B.
AU - Hoang, Thanh D.
AU - Hovorka, Roman
AU - Jendle, Johan H.
AU - Ji, Linong
AU - Joshi, Shashank R.
AU - Joubert, Michael
AU - Koliwad, Suneil K.
AU - Lal, Rayhan A.
AU - Lansang, M. Cecilia
AU - Lee, Wei An
AU - Leelarathna, Lalantha
AU - Leiter, Lawrence A.
AU - Lind, Marcus
AU - Litchman, Michelle L.
AU - Mader, Julia K.
AU - Mahoney, Katherine M.
AU - Mankovsky, Boris
AU - Masharani, Umesh
AU - Mathioudakis, Nestoras N.
AU - Mayorov, Alexander
AU - Messler, Jordan
AU - Miller, Joshua D.
AU - Mohan, Viswanathan
AU - Nichols, James H.
AU - Nørgaard, Kirsten
AU - O’Neal, David N.
AU - Pasquel, Francisco J.
AU - Philis-Tsimikas, Athena
AU - Pieber, Thomas
AU - Phillip, Moshe
AU - Polonsky, William H.
AU - Pop-Busui, Rodica
AU - Rayman, Gerry
AU - Rhee, Eun Jung
AU - Russell, Steven J.
AU - Shah, Viral N.
AU - Sherr, Jennifer L.
AU - Sode, Koji
AU - Spanakis, Elias K.
AU - Wake, Deborah J.
AU - Waki, Kayo
AU - Wallia, Amisha
AU - Weinberg, Melissa E.
AU - Wolpert, Howard
AU - Wright, Eugene E.
AU - Zilbermint, Mihail
AU - Kovatchev, Boris
N1 - Publisher Copyright: © 2022 Diabetes Technology Society.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - ambulatory glucose profile
KW - composite metric
KW - continuous glucose monitor
KW - diabetes
KW - glycemia risk index
KW - hyperglycemia
KW - hypoglycemia
KW - time in range
U2 - 10.1177/19322968221085273
DO - 10.1177/19322968221085273
M3 - Journal article
C2 - 35348391
AN - SCOPUS:85127832115
VL - 17
SP - 1226
EP - 1242
JO - Journal of diabetes science and technology
JF - Journal of diabetes science and technology
SN - 1932-2968
IS - 5
ER -
ID: 328891587