A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.
ambulatory glucose profile
composite metric
continuous glucose monitor
diabetes
glycemia risk index
hyperglycemia
hypoglycemia
time in range
Journal
Journal of diabetes science and technology
ISSN: 1932-2968
Titre abrégé: J Diabetes Sci Technol
Pays: United States
ID NLM: 101306166
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
medline:
4
9
2023
pubmed:
30
3
2022
entrez:
29
3
2022
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 35348391
doi: 10.1177/19322968221085273
pmc: PMC10563532
doi:
Substances chimiques
Blood Glucose
0
Glucose
IY9XDZ35W2
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1226-1242Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK045735
Pays : United States
Organisme : NIDDK NIH HHS
ID : K23 DK123384
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001855
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000130
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK111024
Pays : United States
Organisme : FDA HHS
ID : P50 FD006425
Pays : United States
Organisme : CSRD VA
ID : I01 CX001825
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK098722
Pays : United States
Références
Behav Res Methods. 2020 Aug;52(4):1459-1468
pubmed: 31823224
J Diabetes Sci Technol. 2015 Jan;9(1):56-62
pubmed: 25316714
J Diabetes Sci Technol. 2013 Mar 01;7(2):562-78
pubmed: 23567014
Diabetes Care. 2021 Nov;44(11):2589-2625
pubmed: 34593612
Biochim Biophys Acta Mol Basis Dis. 2021 Aug 1;1867(8):166148
pubmed: 33892081
BMJ. 2007 Apr 14;334(7597):786
pubmed: 17403713
JAMA. 2021 Jun 8;325(22):2273-2284
pubmed: 34077502
JAMA. 2017 Jan 24;317(4):371-378
pubmed: 28118453
Diabetes Technol Ther. 2009 Jun;11 Suppl 1:S55-67
pubmed: 19469679
Diabetes Spectr. 2021 May;34(2):109-118
pubmed: 34149251
Diabetes Technol Ther. 2021 Oct;23(10):692-704
pubmed: 34086495
Diabetes Care. 2017 Dec;40(12):1631-1640
pubmed: 29162583
Diabetes Care. 1997 Nov;20(11):1655-8
pubmed: 9353603
Ann Intern Med. 2017 Sep 19;167(6):365-374
pubmed: 28828487
J Diabetes Sci Technol. 2022 Jan;16(1):3-6
pubmed: 34711063
Diabetes Technol Ther. 2020 Aug;22(8):613-622
pubmed: 32069094
Diabetes Care. 2022 Jan 1;45(Suppl 1):S83-S96
pubmed: 34964868
N Engl J Med. 2019 Oct 31;381(18):1707-1717
pubmed: 31618560
Nat Rev Endocrinol. 2017 Jul;13(7):425-436
pubmed: 28304392
J Stat Softw. 2010;33(1):1-22
pubmed: 20808728
Diabetes Technol Ther. 2018 May;20(5):325-334
pubmed: 29792750
Diabetes Care. 2017 Apr;40(4):538-545
pubmed: 28209654
Diabetes Care. 1998 Nov;21(11):1870-5
pubmed: 9802735
Diabetes Care. 2019 Aug;42(8):1593-1603
pubmed: 31177185