Mathematical modeling of glucose-insulin system behavior in Hospital Tengku Ampuan Afzan intensive care unit patients - Jamaludin, Ummu K.; Suhaimi, Fatanah M.; A. Razak, Normy N.; Dzaharudin, Fatimah; Md Ralib, Azrina; Mat Nor, Mohd Basri (ARPN (Asian Research Publishing Network), 2016)
Mathematical modeling of glucose-insulin system is significantly important to understand the body regulation control, to analyze experimental data based on clinical trials, to identify and quantify relevant physiological parameters, to design proper clinical trials and to assess diabetes therapies. In general, critically ill patients with blood glucose concentrations between 10.0 to 12.2 mmol/l is identified to develop an acute hyperglycaemia or high blood glucose (BG). Thus, to monitor hyperglycaemia among critically ill patients, this study is focused on observing the glucose-insulin system behaviour based on 40 patients’ clinical data collected in Hospital Tengku Ampuan Afzan, Kuantan, Pahang with clinically validated mathematical glucose-insulin model. By using this model, a critical model-based parameter known as insulin sensitivity (SI) that illustrates patient’s severity were identified hourly for all patients whose on insulin infusion therapy protocol for average four to six days. The results show that a BG normal distribution is attained with median kurtosis of 2.72. While, the 40 patient-specific SI indicate that an outliers-prone distribution occurred as kurtosis 3.96. Thus, abrupt changes in SI is basically due to chaotic interaction between blood glucose and insulin concentrations in bloodstreams. Also, the glucose-insulin behavior pattern among these 40 critically ill patients might be varied due to their main diagnotics illness such as acute kidney failure, cardiovascular disease, etc. Overall, these results might assist clinicians and researchers to understand the glucose-insulin behavior based on patient’s severity illness and helps to inform glycaemic control protocol development in a larger group of critically ill patients.