Why the study?
Do linear and nonlinear model predictive control (MPC) strategies improve insulin titration in a type 2 diabetes model?
Do linear and nonlinear model predictive control (MPC) strategies improve insulin titration in a type 2 diabetes model?
Model predictive control strategies show potential for improving automated insulin titration in type 2 diabetes based on computational modeling.
Daily MPC insulin titration in T2D is feasible in simulation; leaves open prospective validation before any clinical consideration.
This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.
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Midtgaard et al. (2024) studied this question.
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