Changes in Identified, Model-based Insulin Sensitivity can be used to Improve Risk and Variability Forecasting in Glycaemic Control

Abstract Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control glycaemia has improved outcomes, but have proven difficult to repeat or achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to forecast future distributions of insulin sensitivity (SI) based on its current value, to predict the range of future blood glucose outcomes for a given intervention. This study presents an improved 3D stochastic model, forecasting future distributions of SI based on its current value and prior variation. The percentage difference in the 5th, 50th, and 95th percentiles between the current 2D and new 3D models are compared. Results show the original 2D stochastic model is over-conservative for around 77% of the data, predominantly where prior variability was low. For higher prior variation (more than ±25% change in SI), the 3D stochastic model prediction range of future SI is wider. The new 3D model was found to have overall narrower 5th – 95th prediction ranges in SI, but to retain a similar per-patient (60 – 100%) and overall (92%) percentage of SI outcomes correctly predicted within these ranges. These results suggest the new 3D model is more patient-specific and will enable more optimal dosing, to increase both safety and performance. This improvement in forecasting may result in tighter and safer glycaemic control, improving performance within the STAR framework.

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