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, SI. This parameter can vary significantly in the critically ill due to the evolution of their condition and drug therapy.A three-dimensional stochastic model of SI variability is constructed using 18 long-term retrospective critical care patients’ data. Given SI for an hour, the stochastic model returns the probability distribution of SI 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 predictions for glycemic control with pre-determined likelihood based on confidence intervals.Clinical control data from eight independent critical care glycemic control trials were re-evaluated using the stochastic model. The stochastic model successfully captures the identified SI variation trend, accounting for 84% of measurements over time within the 0.90 confidence band, 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 ill 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 of insulin sensitivity, SI, variation and resulting glycemic variation in critical care.