No abstract

In critically ill patients suffering from hyperglycemia, it has been recently shown that mortality and morbidity can be reduced by keeping blood glucose within the range of 80-110 mg/dL. However, maintaining glycemia within such range is difficult due to the time variability in insulin sensitivity in critically ill patients. In this paper, we propose a novel glycometabolism model of critically ill patients with an insulin sensitivity parameter and develop a nonlinear model predictive glycemic control system with online identification of insulin sensitivity at one-hour intervals. Simulation results show that our system keeps 70% of BG measurements within the range of 80-110 mg/dL without any severe hypoglycemic incidents, which indicates the effectiveness and safety of our system.

Hyperglycemia is common in critically ill patients and leads to various severe complications and even death. Keeping blood glucose within the range of 80-110 mg/dL (4.4-6.1 mmol/L) has been shown to reduce mortality and morbidity in intensive care units (ICU). Many studies on BG control systems for ICU patients have been reported. However, it is not easy to maintain blood glucose within the desired range because of the time variability of insulin sensitivity in critically ill patients. In this study, to improve the prediction accuracy of blood glucose level in patients, we modi ed a glycometabolism model developed in our previous study, by identifying parameter values from clinical ICU data. Then, we modi ed insulin sensitivity online identi cation algorithm to avoid a sudden change in insulin sensitivity during online identi cation that updates insulin sensitivity value at intervals of 30 min. Finally, since hypoglycemia prevention as important, we designed a glycemic control system using nonlinear model predictive control based on the modi ed model and the online identi cation algorithm of insulin sensitivity. The new glycemic control system achieved 71% of blood glucose measurements within the range of 80-110 mg/dL and 1.5% of measurements below 80 mg/dL, which indicated effectiveness and safety.

In critically ill patients suffering from hyperglycemia it has been recently shown that mortality and morbidity can be reduced by keeping glycemia within 80-110 mg/dL (4.4-6.1 mmol/L). However, maintaining blood glucose (BG) levels within such range is difficult because of the time variability in insulin sensitivity through the patient recovery. In this study, we introduce a nonlinear model predictive control algorithm using a time-invariant model of glucose-insulin metabolism for the eventual development of a control system that regulates BG levels of critically ill patients. Simulation results using virtual patients with time-varying insulin sensitivity show that BG levels can be kept 58.3% within the 80-110 mg/dL range with only 1.4% of BG levels decreasing below 80 mg/dL (4.4 mmol/L), which demonstrates the safety of the present control algorithm.

Diabetes is a metabolic disorder that is characterized by high blood glucose and either insufficient or ineffective insulin. Blood glucose measurement is crucial to diabetes control, and it is effective in reducing the risk of complications and improving life quality. Unfortunately, both elderly patients and their caregivers find it difficult to monitor glucose levels long term. This study attempts to develop an intelligent maintenance system for home glucose measurement, wireless data transformation, and information analysis. The developed system prompts diabetics to measure their blood glucose regularly at home, and provides remote caregivers with complete patient information for diagnosis and tracking. This aids in the improvement in diabetes control, thereby increasing the social activities and life quality of diabetics.