Stochastic modelling of insulin sensitivity variability in critical care

Tight glycemic control has been shown to reduce mortality by 29 45% in critical care. Targeted glycemic control in critical care patients can be achieved by frequent fitting and prediction of a patient's modelled insulin sensitivity index, S-1. This parameter can vary significantly in the critically in due to the evolution of their condition and drug therapy. A three-dimensional stochastic model of S-1 variability is constructed using 18 long-term retrospective critical care patients' data. Given S-1 for an hour, the stochastic model returns the probability distribution of S-1 for the next hour. Consequently, the resulting glycemic distribution 1 h following a known insulin and/or nutrition intervention can be derived. Knowledge of this distribution enables more accurate prediction for glycemic control with pre-determined likelihood based on confidence intervals. Clinical control data front eight independent critical care glycemic control trials were re-evaluated using the stochastic model. The stochastic model successfully captures the identified S-1 variation trend, accounting for 84% of measurements over time within the 0.90 confidence hand, and 45% with a 0.50 confidence. Incorporating the stochastic model into the numerical glucose-insulin dynamics model, a virtual cohort was generated, imitating typical glucose insulin dynamics in a critically in population. Control trial simulations on this virtual cohort showed that the 0.90 confidence intervals cover 88% of measurements, and the 0.5 confidence intervals cover 46%. These: results indicate that the stochastic model provides first order estimate oh insulin sensitivity, S-1, variation and resulting glycemic variation in critical care. (C) 2006 Elsevier Ltd. All rights reserved.