Key result
Constrained Model Predictive Control (C-MPC) outperformed S-MPC in average glucose and time in range, with time in severe hypoglycemia remaining at 0% for in silico patients.
Why the study?
Recent portable devices with high computational power can overcome past hardware limitations, warranting reconsideration of unconstrained saturated model predictive control for artificial pancreas systems.
Does Constrained Model Predictive Control (C-MPC) improve glycemic control compared to unconstrained saturated Model Predictive Control (S-MPC) in an in silico artificial pancreas model?
Does Constrained Model Predictive Control (C-MPC) improve glycemic control compared to unconstrained saturated Model Predictive Control (S-MPC) in an in silico artificial pancreas model?
Constrained model predictive control improves simulated glycemic outcomes compared to unconstrained control for artificial pancreas applications.
C-MPC improves simulated glycemic control; leaves open translation to clinical artificial pancreas systems.
The technological advances reached in the last years released portable devices with high computational capabilities and able to overcome the relevant hardware limitations of the past artificial pancreas (AP) applications. In view of this, the choice of an unconstrained saturated model predictive control (S-MPC) can be reconsidered. A constrained MPC (C-MPC) is formulated here as a finite-horizon optimal control problem and it is retuned, as well as the S-MPC, using the new UVA/Padova simulator. A new calibration procedure is used to consider clinically significant performance indices. The C-MPC and the S-MPC are tested in silico on the 100 adult patients of the new simulator. Its capability to better represent real-life conditions allows to evaluate the MPCs in more realistic scenarios. The C-MPC outperforms the S-MPC in terms of average glucose, time spent in tight range, and time above 180 mg/dL. An acceptable increase of the time below 70 mg/dL to 4% is present for 25% of the patients, but the time in severe hypoglycemia remains equal to 0%. The development of a patient-tailored C-MPC is proposed as future development to mitigate these hypoglycemia phenomena.
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Toffanin et al. (2023) studied Diabetes (n=100). Constrained Model Predictive Control (C-MPC) vs. Unconstrained saturated model predictive control (S-MPC) was evaluated on Average glucose, time spent in tight range, and time above 180 mg/dL. Constrained Model Predictive Control (C-MPC) outperformed S-MPC in average glucose and time in range, with time in severe hypoglycemia remaining at 0% for in silico patients.
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