More Green, Less Red: How Color Standardization May Facilitate Effective Use of CGM Data

Self Cite

115

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.

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 98%

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Klonoff

Wang

Rodbard³

et al. 2022

Self Cite

115

“…The AGP report helps organize the data for effective action with a goal of more TIR (the green zone) and less time below range (the red zone). 43…”

Section: Session 1: Best Metrics For Glycemia Using Cgmmentioning

confidence: 99%

Diabetes Technology Meeting 2021

Nguyen

DuBord

et al. 2022

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

“…High values were coded with amber color by system B, amber, pink, and red colors by system C (depending on the specific glucose concentration), and red color by system D. Ideally, all BGMSs should use the same coloring scheme for various glucose levels as it has been recommended for continuous glucose monitoring (CGM) systems, and it is used in the ambulatory glucose profile for both CGM systems and BGMS. 23,24 This is important from a safety point of view as it would help to avoid confusion, and would not necessitate healthcare professionals to be aware of manufacturer-specific differences of coloring of various glucose levels when assessing glycemic control and making decisions with their diabetes patients. The use of inaccurate BGMSs is associated with increased healthcare costs, 25 and inaccuracy has a relevant effect on clinical outcomes when therapy is guided by BGMSs or by manually calibrated CGM systems.…”

Section: Figurementioning

confidence: 99%

User Performance Evaluation and System Accuracy Assessment of Four Blood Glucose Monitoring Systems With Color Coding of Measurement Results

Pleus

Baumstark

Schauer

et al. 2022

Background: Blood glucose monitoring systems (BGMSs) are a cornerstone in diabetes management. They have to provide sufficiently accurate results in the hands of lay users, particularly in insulin-treated patients. The aim of this study was user performance evaluation and system accuracy assessment of four BGMSs with color coding of results. Methods: Study procedures were based on ISO 15197:2013. User performance evaluation included data from 100 participants, each of whom used every system with one reagent lot. Study personnel observed user techniques. For the system accuracy assessment, 100 capillary samples were obtained for measurement in duplicate with each of three reagent system lots per system, resulting in 600 results per system. Results: All assessed BGMSs exhibited a sufficient level of accuracy, with small differences between them. In the user performance evaluation, study personnel observed the smallest total number of user errors with Contour Next (Ascensia), followed by Accu-Chek Instant (Roche), Medisafe Fit Smile (Terumo), and OneTouch Ultra Plus Reflect (LifeScan). Approximately 90% of participants stated that a consistent color scheme, eg, for low blood glucose (BG) values, should be used across all BGMSs. There was no clear preference among the four tested BGMSs regarding the specific way of displaying color coding. Conclusions: The four BGMSs assessed in this study showed only small differences in an overall sufficient level of accuracy. User handling errors, as observed by study personnel, differed between the systems.