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BackgroundA 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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.

Background: Derived time in range (dTIR), calculated from self-monitored blood glucose (SMBG-dTIR) profiles, has demonstrated correlation with risk of cardiovascular and microvascular complications. This post hoc analysis of the DUAL V and DUAL VIII trials aimed to compare dTIR with an insulin degludec/liraglutide fixed-ratio combination (IDegLira) versus insulin glargine 100 units/mL (glargine U100) in people with type 2 diabetes (T2D)., Materials and Methods: Nine-point SMBG profiles were taken more than 24 hours at baseline and end of trial (EOT: 26 weeks [DUAL V] and 104 weeks [DUAL VIII]) and used to derive the percentage of readings within target range (70-180 mg/dL). Estimated treatment differences (ETDs, IDegLira-glargine U100) were analyzed using analysis of covariance, with treatment as fixed effects and baseline response as a covariate., Results: ETDs for change from baseline to EOT in dTIR were significantly greater with IDegLira versus glargine U100 in DUAL V (4.18%, P = .027) and DUAL VIII (5.17%, P = .001). The proportions of people achieving ≥70% dTIR at EOT with IDegLira and glargine U100, respectively, were 62% and 60% in DUAL V ( P = .7541), and 50% and 26% in DUAL VIII ( P < .0001). The proportion achieving a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 was 63% in both groups in DUAL V ( P = .9043), and 44% and 25%, respectively, in DUAL VIII ( P < .0001)., Conclusions: IDegLira was associated with significantly greater increases in dTIR versus basal insulin alone in people with T2D., Trial Id(s): ClinicalTrials.gov, NCT01952145 (DUAL V); ClinicalTrials.gov, NCT02501161 (DUAL VIII)., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AP-T provides consulting and research services to Novo Nordisk, Lilly, Bayer, Dexcom, and Medtronic on behalf of her employer but receives no direct or indirect reimbursement for the services. VRA reports consulting fees from Applied Therapeutics, Fractyl, Novo Nordisk, Pfizer, Sanofi, and institutional contracts for research from Applied Therapeutics/Medpace, Eli Lilly, Premier/Fractyl, Novo Nordisk, and Sanofi/Medpace. Her spouse is an employee of Janssen. CDB reports consulting fees and honoraria for speaking for Abbott, AstraZeneca, Boehringer-Ingelheim, Eli Lilly, Insulet, Medtronic, Novo Nordisk, and Roche. LKB reports honoraria for attending advisory boards or consulting services provided to Lilly, Novo Nordisk, Xeris, Bayer, and Sanofi. AL has received research support and honoraria for giving presentations and attending advisory boards: AstraZeneca, Bayer, Becton Dickinson, Boehringer-Ingelheim, Bristol Myers Squibb, Lilly, Medtronic, MSD, Novo Nordisk, Roche, and Sanofi. IL reports receiving advisory board fees and consulting fees from AstraZeneca, consulting fees from Bayer Healthcare Pharmaceuticals, Eli Lilly and Company, Intarcia, Intercept Pharmaceuticals, Janssen Global Services, MannKind Corporation, Target Pharma, Valeritas, and Zealand Pharma; advisory board fees from Boehringer-Ingelheim and Sanofi US Services; grant support, paid to UT Southwestern, from Merck; grant support, paid to her institution, from Mylan Pharmaceuticals and Pfizer; grant support, paid to UT Southwestern; and advisory board fees, consulting fees, and travel support from Novo Nordisk. RS and JMD’C are employees of Novo Nordisk A/S.

Hyperglycemia and glucose variability in the hospital environment are associated with higher rates of complications, longer lengths of stay, and mortality. Standardized metrics are needed to assess the efficacy and safety of glucose management interventions. Glucometric data were collected from 2024 inpatients in a San Diego hospital between 2009 and 2011. As a complementary measure of glucose control, individual patient excursion rates were calculated using counts of distinct excursions from normal to critical glucose ranges >180 or <70 mg/dL. Prediction models for excursion rates were devised, based on patient demographic and clinical characteristics. Patients were predominantly male (51.2%), Caucasian (86.0%), and elderly (median age 72 years). Obesity was prevalent: 32% were overweight and 33% were obese. Median length of hospitalization was 5.0 days (range, 0.8-139.4 days). Unadjusted rate of excursions >180 mg/dL was 0.456 per 24 hours. The proportion of zero excursions decreased as severity of illness decreased, but was unrelated to age. Excursion rates were slightly smaller for major and extreme severity of illness compared to mild or moderate illness severity. Excursion rates did not vary in a monotone fashion with age, although the general pattern reflected a reduction in excursion rates from the first age quartile (19 to 59) through the last age quartile (83 to 100). Using the Akaike information criterion, zero-inflated negative binomial models were identified as appropriate for analyzing glucose excursion rates. Systematic approaches to glucose reporting and management in the hospital environment offer "windows of opportunity" to improve diabetes care., (© 2014 Diabetes Technology Society.)