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
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.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsmodel-based control; tight blood glucose control; TGC; blood glucose; insulin therapy; insulin sensitivity; critical care; predictive performance
ANZSRC Fields of Research49 - Mathematical sciences::4901 - Applied mathematics::490103 - Calculus of variations, mathematical aspects of systems theory and control theory
40 - Engineering::4003 - Biomedical engineering::400303 - Biomechanical engineering
32 - Biomedical and clinical sciences::3201 - Cardiovascular medicine and haematology::320102 - Haematology
32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive care
Showing items related by title, author, creator and subject.
Tight Glycemic Control in Critical Care - The leading role of insulin sensitivity and patient variability – A review and model-based analysis Chase, Geoff; Le Compte, A.J.; Suhaimi, F.; Shaw, Geoff; Lynn, A.; Lin, J.; Pretty, Christopher; Razak, N.N.; Parente, J.D.; Hann, C.E.; Preiser, J-C.; Desaive, T. (University of Canterbury. Electrical and Computer EngineeringUniversity of Canterbury. Mathematics and StatisticsUniversity of Canterbury. Mechanical Engineering, 2011)Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced ...
Interface design and human factors considerations for model-based tight glycemic control in critical care Ward, L.; Steel., J; LeCompte, A.J.; Evans, A.; Tan, C-S.; Penning, S.; Shaw, Geoff; Desaive, T.; Chase, Geoff (University of Canterbury. Mechanical Engineering, 2012)Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research ...
Ward, L.; Steel, J.; LeCompte, A.J.; Evans, A.; Tan, C.S.; Penning, S.; Shaw, Geoff; Desaive, T.; Chase, Geoff (University of Canterbury. Mechanical Engineering, 2012)Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. Model-based methods and computerized protocols offer the opportunity to improve TGC quality but require human ...