Intelligent closed-loop insulin delivery systems for ICU patients.

Abstract

Good glycemic control through insulin administration among intensive care unit (ICU) patients can reduce mortality significantly; however, it remains a big challenge because of scarcity of individualized models for ICU patients. To deal with this challenge, a new combination of particle swarm optimization (PSO) and model predictive control (MPC) has been proposed to identify the model online as well as to optimally design the input, i.e., the insulin delivery rate automatically. According to the population distribution, ten typical linear dynamic models were selected such that any patient's model could be approximated by a linear combination of these ten typical models. PSO was used to update the weight coefficients while MPC was used to design the insulin delivery rate based on the combination model identified by using PSO. The proposed strategy was compared with the Yale protocol on 30 virtual subjects. According to the control-variability grid analysis, the percentage values in A + B zone were, respectively, 100% under the proposed strategy and while 51% under the Yale protocol, which demonstrates the superior performance of the proposed strategy. As a good candidate for the full closed-loop insulin delivery method, this new combination can control the glucose level by bringing it to a safe range promptly thereby reducing the risk of death.