Type-1 Diabetes Patient Decision Simulator for In Silico Testing Safety and Effectiveness of Insulin Treatments.

OBJECTIVE: Type-1 diabetes (T1D) treatment requires exogenous insulin administrations finely tuned based on glucose monitoring to avoid hyper/hypoglycemia. The safety and effectiveness of insulin treatments is commonly assessed in clinical trials, which are time demanding and expensive. These limitations can be overtaken by in silico clinical trials (ISCT) that require realistic patient and treatment models. The aim is to develop a T1D patient decision simulator usable to perform reliable ISCT.

METHODS: The T1D patient decision simulator was developed by connecting the UVA/Padova T1D model, which describes glucose, insulin, and glucagon kinetics, with modules describing glucose monitoring devices, like self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM), the patient's behavior in making treatment decisions, and insulin administration. The reliability of the simulator was assessed by comparing its predictions with data collected in 44 T1D subjects using the Dexcom G5 Mobile CGM sensor as an adjunct to the Bayer Contour Next USB SMBG device.

RESULTS: Metrics like time spent in eu/hypo/hyperglycemia of simulated data well match those observed in subjects. In particular, mean time in euglycemia, mean time in hyperglycemia, and median time in hypoglycemia are 61.75% versus 63.60% (p-value = 0.4825), 33.38% versus 33.40% (p -value = 0.9950), and 3.17% versus 2.14% (p-value = 0.1134), respectively, in real versus simulated data.

CONCLUSION: The proposed simulator can be used to perform credible ISCT in realistic insulin treatment scenarios.

SIGNIFICANCE: The T1D patient decision simulator can be used to reliably assess novel insulin treatments, e.g., based on use of CGM only, in a realistic multiple-day scenario.