A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Abstract

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care units (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identi ability issues are carefully considered for clinical settings. This article also contains signi cant reference to relevant physiology and clinical literature, as well as some references to the modeling e orts in this eld. Identi cation of critical constant population parameters were performed Preprint submitted to Computer Methods and Programs in BiomedicineSeptember 16, 2010 in two stages, thus addressing model identi ability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI , the only dynamic, time-varying parameter, is identi ed hourly for each individual. All population parameters are justi ed physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study con rms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median tting error of <1% over data from 173 patients (N = 42,941 hrs in total) who received insulin while in the ICU and stayed for 72 hrs. Most importantly, the median per-patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is signi cant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7{12%. These results con rm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care