Validation of an Algorithm for Predicting Hypoglycemia From Continuous Glucose Measurements and Heart Rate Variability Data

摘要

Hypoglycemia is associated with increased physical and psychosocial morbidity and is a risk factor for mortality.1,2 Hypoglycemia is very common in patients with type 1 diabetes mellitus. Patients trying to obtain or maintain a tight glycemic control suffer from frequent episodes of asymptomatic hypoglycemia. Some studies suggest that plasma glucose levels may be less than 60 mg/dL (3.3 mmol/L) 10% of the time. Moreover, on average, patients with type 1 diabetes suffer from two weekly incidents of symptomatic hypoglycemia.1,3,4 We have in two previous studies5,6 investigated the potential of using continuous glucose monitor (CGM) and additional heart rate variability (HRV) data to predict hypoglycemia. Data from ten patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained and tested.5 Furthermore, in 21 patients with type 1 diabetes prone to hypoglycemia monitored with CGM and a Holter device while they performed normal daily activities, we found that using information from both HRV and CGM yielded more accurate predictions than using information from CGM only.6 However, questions still remained if these results could be generalized to a large cohort of patients. Therefore, we sought to validate the algorithm developed in the previous studies on a new larger and more diverse population of patients with type 1 diabetes.