Stochastic modelling of insulin sensitivity variability in critical care
Hyperglycaemia is prevalent in critical care, and tight control can reduce mortality by 29 - 45%. Targeted glucose control can be achieved by frequent fitting and prediction of a modelled insulin sensitivity index, SI. This parameter varies significantly in the critically ill due to condition evolution and drug therapies. A 3-D stochastic model of hourly SI variability is constructed using retrospective data from 18 long term critical care patients. The model can be used to produce the blood glucose level probability distribution one hour following a known insulin and/or nutrition intervention. Thus, it enables accurate prediction for glycemic control based on confidence intervals.