Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.