Abstract
AID systems aim to reduce mental burden experienced by people with diabetes by automatically adjusting insulin delivery in response to real-time glucose levels; however, systems still require pre-meal boluses for optimal outcomes. A system providing an automatic bolus response (‘AutoBolus’) to rising glucose levels (e.g., after meals) lessens user burden and reduces hyperglycemic events by compensating for missed or under-estimated pre-meal boluses. We propose a novel method to deliver an AutoBolus based on rising glucose levels via a machine learning based meal detection algorithm. AutoBolus meal detection is paired with a two-part novel insulin delivery scheme with an immediate safe upfront delivery, followed by temporarily tuning AID algorithm parameters to progressively respond to the post-prandial glucose response by delivering additional insulin. If a decreasing glucose condition is detected, the AID algorithm parameters revert to baseline. In-silico simulation demonstrates AutoBolus increases time in euglycemia by as much as 11% in adolescents, 8% in adults, and 6% in children for missed boluses for three meals in a 24-hour period. Our results show that the proposed AutoBolus insulin delivery partially compensates for missed pre-meal boluses increasing time in euglycemia, reducing user burden. Figure. Illustrative diagram showing the two-part novel insulin delivery scheme upon meal detection. Disclosure R.Narayanaswami: None. Y.Zheng: Employee; Insulet Corporation. W.J.Whiteley: Employee; Insulet Corporation. M.Sevil: None. S.Salavati: Employee; Insulet Corporation.
Published Version
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