Authors: Sufyan Hussain, William Polonsky, Renza Scibilia, Timor Glatzer
Abstract
Advances in diabetes technologies such as continuous glucose monitoring (CGM) have provided significant opportunities to improve glycemic and quality-of-life outcomes for people with type 1 diabetes (T1D). The ambulatory glucose profile and the introduction of glucose thresholds helped a lot to identify patterns, which was the first step toward improving hyper-and hypoglycemia management. Despite these innovations, the relentless burden of day-to-day T1D management continues to be a challenge for individuals and their families. In particular, hypoglycemia remains a significant cause of morbidity and mortality, as well as a barrier to achieving optimal glycemia, contributing to anxiety, fear, worry, and distress. Algorithm developments have led to CGM device-based thresholds and predictive alarms to warn individuals of impending hypoglycemia. More recent developments with artificial intelligence technology now allow for forecasting glucose trends and values over longer time frames, thereby aiding therapy decision-making. In this article, we focus on hypoglycemia and summarize recent developments in glucose prediction from CGM devices. While not intended to be a comprehensive review, we provide an update, highlight anticipated developments, and speculate on potential pitfalls and the potential value from medical, psychosocial, and lived experience perspective.
Keywords: artificial intelligence; continuous glucose monitoring; diabetes; digital health; glucose prediction; predictive analytics.
