title: 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 unit (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 identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability 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, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm 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.
authors.affiliation: Department of Medicine, University of Otago Christchurch, New Zealand. jesschlin@gmail.com
authors.forename: Jessica
authors.initials: J
authors.lastname: Lin
authors.forename: Normy N
authors.initials: NN
authors.lastname: Razak
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Pretty
authors.forename: Aaron
authors.initials: A
authors.lastname: Le Compte
authors.forename: Paul
authors.initials: P
authors.lastname: Docherty
authors.forename: Jacquelyn D
authors.initials: JD
authors.lastname: Parente
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Hann
authors.forename: J
authors.initials: J
authors.lastname: Geoffrey Chase
mesh_terms: D001786:Blood Glucose / Q000378:metabolism*; D003198:Computer Simulation; D003422:Critical Care; D016638:Critical Illness / Q000628:therapy*; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000188:drug therapy / Q000628:therapy; D007328:Insulin / Q000008:administration & dosage* / Q000378:metabolism / Q000493:pharmacokinetics; D007333:Insulin Resistance; D008954:Models, Biological; D009747:Nutritional Physiological Phenomena; D013813:Therapy, Computer-Assisted / Q000379:methods*
chemical_list: D001786:Blood Glucose; D007328:Insulin