Reducing Hypoglycemia in the Real World: A Retrospective Analysis of Predictive Low-Glucose Suspend Technology in an Ambulatory Insulin-Dependent Cohort

Abstract

Abstract Objective: Analyze real-world usage and impact of a predictive low-glucose suspend (PLGS) insulin delivery system for maintenance of euglycemia and prevention of hypoglycemic events in people with insulin-dependent diabetes. Methods: Retrospective analysis of Tandem Basal-IQ users who uploaded at least 21 days of PLGS usage data between August 31, 2018, and March 14, 2019 (N\u2009=\u20098132). Insulin delivery and sensor-glucose concentrations were analyzed. The times spent below 70\u2009mg/dL, between 70 and 180\u2009mg/dL, and above 180\u2009mg/dL were assessed. Subgroup analyses were conducted to examine matched pre-/postoutcomes with experienced users (n\u2009=\u20091371) and performance over time for a mixed subgroup with >9 weeks of data (n\u2009=\u20093563). Results: The mean age of patients was 32.4 years, 52% were female, 96% had type 1 diabetes, and 4% had type 2 diabetes. Mean duration on PLGS was 65 days. Algorithm introduction led to a 45% median relative risk reduction in sensor time <70\u2009mg/dL, pre/post (% <70:2.0, 1.1), while the mean glucose remained stable (168 and 168\u2009mg/dL). Mean frequency of hypoglycemic events decreased from one every 9 days to one every 30 days. Total daily insulin dose decreased from 43.4 to 42.3\u2009U in the pre/post subgroup. Manual override of the system was low (4.5%). The number of daily suspensions remained stable (4.9). Conclusions: Introduction of PLGS resulted in effective and sustained prevention of hypoglycemia without a significant increase in mean blood glucose and may be considered for people with type 1 diabetes at risk for hypoglycemia.