Model predictive control for dose guidance in long acting insulin treatment of type 2 diabetes

Approximately 90% of the people with diabetes have type 2 diabetes (T2D), and more than half of the diabetes patients on insulin fail to reach the treatment targets. The reasons include fear of hypoglycemia, complexity of treatment, and work load related to treatment intensification. This paper proposes a model predictive control (MPC) based dose guidance algorithm to identify an individual’s optimal dosing of long acting insulin. We present a model for simulating the effect of long acting insulin on fasting glucose in T2D. We do this by adapting previous models such that slow and non-linear dynamics are identifiable from clinical data. For dose guidance, we use MPC with a novel approach to sub-frequency actuation, to increase safety between input samples. To test the controller, we simulate scenarios with biological variations and different levels of adherence to treatment. The results are compared to a standard of care (SOC) method in insulin dose adjustments.