Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18-45% is enabled by prediction of insulin sensitivity, S-I. However, this parameter can vary significantly over a given hour in the critically ill as their condition evolves. A stochastic model of S-I variability is constructed using data from 165 critical care patients. Given S-I for an hour, the stochastic model returns the probability density function of S-I for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control. Cross validation of the S-I variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. Virtual Patients with S-I behaving to the overall S-I variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%). Finally, adaptive control method incorporating S-I variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability. (c) 2007 Elsevier Ireland Ltd. All rights reserved.