Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study.

Abstract

Standard management of type 1 diabetes (T1D) relies on blood glucose monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize BGM for calibration and dosing. The question of how the accuracy of both technologies interact is still not well understood. We use a recently developed data-driven simulation approach to characterize the relationship between CGM and BGM accuracy especially how BGM accuracy impacts CGM performance in four different use cases with increasing levels of reliance on twice daily calibrated CGM. Simulations are used to estimate clinical outcomes and isolate CGM and BGM accuracy characteristics that drive performance. Our results indicate that meter (BGM) accuracy, and more specifically systematic positive or negative bias, has a significant effect on clinical performance (HbA1c and severe hypoglycemia events) in all use-cases generated for twice daily calibrated CGMs. Moreover, CGM sensor accuracy can amplify or mitigate, but not eliminate these effects. As a system, BGM and CGM and their mode of use (use-case) interact to determine clinical outcomes. Clinical outcomes (eg, HbA1c, severe hypoglycemia, time in range) can be closely approximated by linear relationships with two BGM accuracy characteristics, namely error and bias. In turn, the coefficients of this linear relationship are determined by the use-case and by CGM accuracy (MARD).