The Development of New Composite Metrics for the Comprehensive Analytic and Visual Assessment of Hypoglycemia Using the Hypo-Triad

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes

This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.

Continuous Glucose Monitoring in Adults with Type 1 Diabetes: Real-World Data from the German/Austrian Prospective Diabetes Follow-Up Registry

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70-180 mg/dL] 54% vs. 49%) and hypoglycemic range <70 mg/dL (7% vs. 5%), less time in hyperglycemic range >180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged <30 years. We found no significant differences between genders. Multivariable regression models revealed the highest Time In Range (TIR) and lowest time with sensor glucose >250 mg/dL, CV and daytime-night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control

We performed a literature review of composite metrics for describing the quality of glycemic control, as measured by continuous glucose monitors (CGMs). Nine composite metrics that describe CGM data were identified. They are described in detail along with their advantages and disadvantages. The primary benefit to using composite metrics in clinical practice is to be able to quickly evaluate a patient's glycemic control in the form of a single number that accounts for multiple dimensions of glycemic control. Very little data exist about (1) how to select the optimal components of composite metrics for CGM; (2) how to best score individual components of composite metrics; and (3) how to correlate composite metric scores with empiric outcomes. Nevertheless, composite metrics are an attractive type of scoring system to present clinicians with a single number that accounts for many dimensions of their patients' glycemia. If a busy health care professional is looking for a single-number summary statistic to describe glucose levels monitored by a CGM, then a composite metric has many attractive features.

Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels

Background: Complex changes of glycemia that occur in diabetes are not fully captured by any single measure. The Comprehensive Glucose Pentagon (CGP) measures multiple aspects of glycemia to generate the prognostic glycemic risk (PGR), which constitutes the relative risk of hypoglycemia combined with long-term complications. We compare the components of CGP and PGR across type 1 and type 2 diabetes. Methods: Participants: n = 60 type 1 and n = 100 type 2 who underwent continuous glucose monitoring (CGM). Mean glucose, coefficient of variation (%CV), intensity of hypoglycemia (INT_hypo), intensity of hyperglycemia (INT_hyper), time out-of-range (TOR <3.9 and >10 mmol/L), and PGR were calculated. PGR (median, interquartile ranges [IQR]) for diabetes types, and HbA1c classes were compared. Results: While HbA1c was lower in type 1 (type 1 vs. type 2: 8.0 ± 1.6 vs. 8.6 ± 1.7, P = 0.02), CGM-derived mean glucoses were similar across both groups (P > 0.05). TOR, %CV, INT_hypo, and INT_hyper were all higher in type 1 [type 1 vs. type 2: 665 (500, 863) vs. 535 (284, 823) min/day; 39% (33, 46) vs. 29% (24, 34); 905 (205, 2951) vs. 18 (0, 349) mg/dL × min²; 42,906 (23,482, 82,120) vs. 30,166 (10,276, 57,183) mg/dL × min², respectively, all P < 0.05]. Across each HbA1c class, the PGR remained consistently and significantly higher in type 1. While mean glucose remained the same across HbA1c classes, %CV, TOR, INT_hyper, and INT_hypo were significantly higher for type 1. Even within the same HbA1c class, the variation (IQR) of each parameter in type 1 was wider. The PGR increased across diabetes groups; type 2 on orals versus type 2 on insulin versus type 1 (PGR: 1.6 vs. 2.2 vs. 2.9, respectively, P < 0.05). Conclusion: Composite indices such as the CGP capture significant differences in glycemia independent of HbA1c and mean glucose. The use of such indices must be explored in both the clinical and research settings.

Going beyond HbA1c to understand the benefits of advanced diabetes therapies

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.

Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices"

OBJECTIVE

We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia.

RESEARCH DESIGN AND METHODS

We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups.

RESULTS

There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M₁₀₀, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE_{%Hypoglycemia}), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE_{%Hyperglycemia}), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in M_R, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE_{%Hypoglycemia}, or GRADE_{%Hyperglycemia}.

CONCLUSIONS

Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.

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

BACKGROUND

Composite metrics have the potential to provide more complete and clinically useful information about glycemic control than traditional individual metrics such as hemoglobin A1C, %/time/area under curve of hypoglycemia and hyperglycemia.

METHODS

Using five key metrics that are derived from continuous glucose monitoring, we developed a new, multicomponent composite metric, the Comprehensive Glucose Pentagon (CGP) that demonstrates glycemic control both numerically and visually. Two of its axes are composite metrics-the intensity of hypoglycemia and intensity of hyperglycemia. This approach eliminates the use of the surrogate marker, hemoglobin A1C (A1C), and replaces it with glucose-centric metrics.

RESULTS

We reanalyzed the data from two randomized control trials, the STAR 3 and ASPIRE In-Home studies using the CGP. It provided new insights into the effect of sensor-augmented pumping (SAP) in the STAR 3 trial and sensor-integrated pumping with low-glucose threshold suspend (SIP+TS) in the ASPIRE In-Home trial.

CONCLUSIONS

The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).