The Comprehensive Glucose Pentagon: A Glucose-Centric Composite Metric for Assessing Glycemic Control in Persons With Diabetes

Vigersky, Robert A.; Shin, John; Jiang, Bingbing; Siegmund, Thorsten; McMahon, Chantal; Thomas, Andreas

doi:10.1177/1932296817718561

Cited by 37 publications

(31 citation statements)

References 33 publications

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“…Several published studies have used composite metrics, usually combining HbA1c with hypoglycemia or mean glucose with hypoglycemia, to display outcomes of intervention studies. We have developed a three‐component metric (“hypo‐triad”) and a five‐component metric (comprehensive glucose pentagon [CGP]) to help clinicians, researchers and patients better understand overall glycemic control at a glance …”

Section: Continuous Glucose Monitoring Permits the Development Of Commentioning

confidence: 99%

“…The CGP contains all five key metrics proposed by the Beyond A1C Writing Group . The construction of the CGP has been described in detail elsewhere . Briefly, it plots the time out of range (TOR; which is the reciprocal of time in range), mean sensor glucose, coefficient of variation (CV), intensity of hypoglycemia (the vector composed of the area under the curve [AUC] and duration of hypoglycemia) and intensity of hyperglycemia (the vector composed of the AUC and duration of hyperglycemia) on weighted axes to obtain visual and mathematical reports.…”

Section: Continuous Glucose Monitoring Permits the Development Of Commentioning

confidence: 99%

“…Traditional representation of individual outcome metrics from the Automation to Stimulate Pancreatic Insulin REsponse (ASPIRE) In‐Home trial showing the primary efficacy end‐point, namely reduction in the area under the curve (AUC) for nocturnal hypoglycemia A, and the primary safety end‐point, namely the change in HbA1c B, LGS, low glucose suspend. (modified from Vigersky et al)…”

Section: Non‐hba1c Metrics Help Understand the Benefits Of Advanced Dmentioning

confidence: 99%

“…We have developed a three-component metric ("hypo-triad") and a five-component metric (comprehensive glucose pentagon [CGP]) to help clinicians, researchers and patients better understand overall glycemic control at a glance. 16,17 The "hypo-triad" uses frequency, severity and time to numerically and graphically display CGM data. An example of how the "hypo-triad" can provide a robust understanding is shown in Figure 2, where the CGM data from the first randomized controlled trial of a low glucose suspend (LGS) pump are displayed at baseline and at the end of 3 months in the control group (using a sensor-augmented pump [SAP]) and in the LGS group.…”

Section: Hba1c May Be Inaccurate In a Wide Range Of Physiologic And Pmentioning

confidence: 99%

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Going beyond HbA1c to understand the benefits of advanced diabetes therapies

Vigersky

2018

Journal of Diabetes

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The gold standard for monitoring overall glycemia is HbA1c. However, HbA1c has several important limitations, giving more weight to the prior 2 to 3 months rather than short-term glycemic control. In addition, the level of the HbA1c does not reflect the important interpersonal differences in its relationship with mean glucose, and HbA1c is affected by many common clinical conditions (anemia, uremia) that can interfere with the accuracy of its measurement in the laboratory. The development and refinement of continuous glucose monitoring (CGM), a glucose- and patient-centric technology, over the past two decades have permitted the creation of new single and composite metrics, such as the percentage of time in range and the glucose pentagon, respectively, which provide clinically relevant insights into short-term glycemic control. In addition, CGM creates new outcome metrics for clinical management and investigational studies (percentage of time in hypoglycemia, percentage of time in target range) that can accurately and meaningfully report the effects of an intervention, whether that is a drug, a device, or a psychosocial program, and CGM provides the key input to drive algorithm-based insulin delivery. Finally, CGM linked with artificial intelligence permits real-time feedback to patients about modifiable patterns of glycemic excursions.

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Section: Continuous Glucose Monitoring Permits the Development Of Commentioning

confidence: 99%

Section: Continuous Glucose Monitoring Permits the Development Of Commentioning

confidence: 99%

Section: Non‐hba1c Metrics Help Understand the Benefits Of Advanced Dmentioning

confidence: 99%

Section: Hba1c May Be Inaccurate In a Wide Range Of Physiologic And Pmentioning

confidence: 99%

See 2 more Smart Citations

Going beyond HbA1c to understand the benefits of advanced diabetes therapies

Vigersky

2018

Journal of Diabetes

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“…Most of them are composite metrics, such as the J-index [16] which perpetuates the inclusion of Mean and SD into one measurement, the M-value [17] which measures the difference between the observed glycemia and an Ideal Glucose Value (IGV, normally taken as 6.67 mmol/L) while incorporating the glycemic range, and more recently, the COGI [18], a weighted linear combination of TIR, TBR and SD. One can refer to [19] [20] [21] [22] for other composite metrics.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics

Zheng

Jalbert

Forbes

et al. 2020

Diabetes Technology & Therapeutics

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Background: Glycemic variability is an important component of glycemic control for patients with type 1 diabetes. The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although glycemic variability, from one day to another, can show very different patterns, few metrics have been dedicated to daily evaluations. Method: A reference (stable-glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their βscore (composite score for grading success) ≥ 6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily glycemic variability severity. In addition, determining the likelihood value that best separates the daily glycemia with β-score = 0 from that with β-score ≥ 6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable". Results: The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily glycemic variability index, based on which, long term variability evaluations and investigations on the implicit link between variability and β-score can be carried out. Evaluation, in a daily glycemic variability classification task, shows that the proposed method is highly concordant to the experience of diabetologists. Conclusion: A multivariate statistical model is proposed to characterize the daily glycemic variability of subjects with type 1 diabetes. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.

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Positioning time in range in diabetes management

Advani

2019

Diabetologia

129

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Recent upswings in the use of continuous glucose monitoring (CGM) technologies have given people with diabetes and healthcare professionals unprecedented access to a range of new indicators of glucose control. Some of these metrics are useful research tools and others have been welcomed by patient groups for providing insights into the quality of glucose control not captured by conventional laboratory testing. Among the latter, time in range (TIR) is an intuitive metric that denotes the proportion of time that a person's glucose level is within a desired target range (usually 3.9-10.0 mmol/l [3.5-7.8 mmol/l in pregnancy]). For individuals choosing to use CGM technology, TIR is now often part of the expected conversation between patient and healthcare professional, and consensus recommendations have recently been produced to facilitate the adoption of standardised TIR targets. At a regulatory level, emerging evidence linking TIR to risk of complications may see TIR being more widely accepted as a valid endpoint in future clinical trials. However, given the skewed distribution of possible glucose values outside of the target range, TIR (on its own) is a poor indicator of the frequency or severity of hypoglycaemia. Here, the state-of-the-art linking TIR with complications risk in diabetes and the inverse association between TIR and HbA 1c are reviewed. Moreover, the importance of including the amount and severity of time below range (TBR) in any discussions around TIR and, by inference, time above range (TAR) is discussed. This review also summarises recent guidance in setting 'time in ranges' goals for individuals with diabetes who wish to make use of these metrics. For most people with type 1 or type 2 diabetes, a TIR >70%, a TBR <3.9 mmol/l of <4%, and a TBR <3.0 mmol/l of <1% are recommended targets, with less stringent targets for older or high-risk individuals and for those under 25 years of age. As always though, glycaemic targets should be individualised and rarely is that more applicable than in the personal use of CGM and the data it provides.

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The Comprehensive Glucose Pentagon: A Glucose-Centric Composite Metric for Assessing Glycemic Control in Persons With Diabetes

Cited by 37 publications

References 33 publications

Going beyond HbA1c to understand the benefits of advanced diabetes therapies

Going beyond HbA1c to understand the benefits of advanced diabetes therapies

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics

Positioning time in range in diabetes management

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