Continuous subcutaneous insulin infusion (CSII) therapy is becoming increasingly popular. CSII provides convenient insulin delivery, precise dosing, easy adjustments for physical activity, stress, or illness, and integration with continuous glucose monitors in hybrid or other closed-loop systems. However, even as insulin pump hardware and software have advanced, technology for insulin infusion sets (IISs) has stayed relatively stagnant over time and is often referred to as the "Achilles heel" of CSII. To discuss barriers to insulin pump therapy and present information about advancements in, and results from clinical trials of extended wear IISs, Diabetes Technology Society virtually hosted the "Improving the Patient Experience with Longer Wear Infusion Sets Symposium" on December 1, 2021. The symposium featured experts in the field of IISs, including representatives from Steno Diabetes Center Copenhagen, University of California San Francisco, Stanford University, Medtronic Diabetes, and Science Consulting in Diabetes. The webinar's seven speakers covered (1) advancements in insulin pump therapy, (2) efficacy of longer wear infusion sets, and (3) innovations to reduce plastics and insulin waste.

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.

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.