New approaches to determine insulin dose in type 1 diabetes treatment using continuous glucose monitoring data

Type 1 Diabetes is a chronic metabolic disorder that is normally treated by subcutaneous administration of exogenous insulin several times per day, by manual injections or by a pump, with individual doses determined using empirical guidelines which exploit knowledge of current blood glucose level assessed by the patient using fingerprick devices. The recent advent of long-lasting (weeks) minimally-invasive continuous glucose monitoring (CGM) sensors, and the growing field of therapeutic applicability granted to them by regulatory agencies in both EU and US, has stimulated investigation on new guidelines to determine insulin dosing exploiting glucose trend information. In this work we first assess, in an in silico clinical trial, the relative performance of three popular methods to determine the size of the insulin bolus at meal using CGM-based glucose trend information. Then we devise, and assess in the same population of virtual subjects, a new method based on a neural network modelling approach, which permits a personalization of the therapy. Preliminary results show that the new method can potentially improve glucose control, encouraging further development of the research.