Jason Wong

TL;DR: The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability, and adaptive control method incorporating S(I) variability is shown to produce improved gly glucose control in simulated trials compared to current clinical results.

TL;DR: Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels and shows very tight control in safe glycaemic bands.

TL;DR: The results indicate that the stochastic model provides first order estimate of insulin sensitivity, S I , variation and resulting glycemic variation in critical care.

TL;DR: Clinically current insulin types and older insulin types are included in a unified framework that accounts for nonlinear concentration and dose dependency and can be considered a posteriori identifiable.

TL;DR: A benchmark data set based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies provides a benchmark for comparing and analyzing performance in model-based glycemic control in a medical intensive care unit (ICU).