Intensive insulin therapy for critically ill subjects based on direct data-driven model predictive control

Patients in the intensive care units (ICU) can suffer from stress-induced hyperglycemia, which can result in negative outcomes and even death. Recent studies show that, regulation of blood glucose (BG) brings in improved outcomes. In this study, a novel direct data-driven model predictive control (MPC) strategy is developed to tightly regulate BG concentration in the ICU. The effectiveness of the proposed direct data-driven MPC strategy is validated on 30 virtual ICU patients, and the in silico results demonstrate the proposed method's excellent robustness with respect to intersubject variability and measurement noises. In addition, the mean percentage values in A-zone of the control variability grid analysis (CVGA) plots are 14% under the Yale protocol, 67% under the combination of particle swarm optimization (PSO) and MPC method (for short, termed as PSO-MPC method), and 90% under the proposed method. In summary, as a good candidate for full closed-loop glycemic control algorithm, the proposed method has superior performance to the nurse-driven Yale protocol and the closed-loop PSO-MPC method.