Intensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patients (2010)
PublisherUniversity of Canterbury. Electrical and Computer Engineering
University of Canterbury. Mathematics and Statistics
University of Canterbury. Mechanical Engineering
AuthorsLin, J., Razak, N.N., Pretty, C.G., LeCompte, A.J., Docherty, P.D., Parente, J.D., Shaw, G.M., Hann, C.E., Chase, J.G.show all
A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition- Glucose (ICING) Model is presented and validated using data from critically ill patients. Glucose utilisation and its endogenous production in particular, are more distinctly expressed. A more robust glucose absorption model through ingestion is also added. Finally, this model also includes explicit pathways of insulin kinetics, clearance and utilisation. Identification of critical constant population parameters is carried out parametrically, optimising one hour forward prediction errors, while avoiding model identifiability issues. The identified population values are pG = 0.006 min-1, EGPb = 1.16 mmol/min and nI = 0.003 min-1, all of which are within reported physiological ranges. Insulin sensitivity, SI , is identified hourly for each individual. All other model parameters are kept at well-known population values or functions of body weight or surface area. A sensitivity study confirms the validity of limiting time-varying parameters to SI only. The model achieves median fitting error <1% in data from 173 patients (N = 42,941 hrs in total) who received insulin while in the Intensive Care Unit (ICU) and stayed for more than 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 significant that the 75th percentile prediction error is now within the lower bound of typical glucometer measurement errors of 7-12%. This result further confirms that the model is suitable for developing model-based insulin therapies, and capable of delivering tight blood glucose control, in a real-time model based control framework with a tight prediction error range.
CitationLin, J., Razak, N.N., Pretty, C.G., LeCompte, A.J., Docherty, P.D., Parente, J.D., Shaw, G.M., Hann, C.E., Chase, J.G. (2010) Intensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patients. Coventry, UK: UKACC International Conference on CONTROL 2010, 7-10 Sep 2010.
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Keywordsmodel-based control; tight blood glucose control; TGC; blood glucose; insulin therapy; insulin sensitivity; critical care; predictive performance
ANZSRC Fields of Research01 - Mathematical Sciences::0102 - Applied Mathematics::010203 - Calculus of Variations, Systems Theory and Control Theory
09 - Engineering::0903 - Biomedical Engineering::090302 - Biomechanical Engineering
11 - Medical and Health Sciences::1102 - Cardiovascular Medicine and Haematology::110202 - Haematology
11 - Medical and Health Sciences::1103 - Clinical Sciences::110310 - Intensive Care