Does a novel meal-detection algorithm based on continuous glucose measurements accurately detect meals and reduce postprandial hyperglycemia in patients with Type 1 Diabetes?
A novel meal-detection algorithm for an artificial pancreas system accurately detects meals without manual input, enabling automated insulin boluses and potentially reducing postprandial hyperglycemia in Type 1 Diabetes.
A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) mg/dl for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.
Turksoy et al. (Tue,) studied this question.