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

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 = 8132). Insulin delivery and sensor-glucose concentrations were analyzed. The times spent below 70 mg/dL, between 70 and 180 mg/dL, and above 180 mg/dL were assessed. Subgroup analyses were conducted to examine matched pre-/postoutcomes with experienced users (n = 1371) and performance over time for a mixed subgroup with >9 weeks of data (n = 3563). 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 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.