Modeling glucose and subcutaneous insulin dynamics in critical care

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Model-based decision support could be used to tailor insulin treatment to patients suffering from stress hyperglycemia, while avoiding hypoglycemia. This work combines a previously published glucose and insulin model with a subcutaneous insulin delivery model, herein simplified using Markov Chain Monte Carlo optimization and Kullback–Liebler distance, to capture fast-acting and regular insulin using two shared and one type-specific fitted parameter. Glucose data from a critical care population (N=48) receiving subcutaneous insulin are fit to within finger stick glucose measurement error of 5% using a regularized, time-varying parameter. The resulting virtual patient cohort provides a basis on which automated insulin delivery systems can be tested.