Intensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patients

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dc.contributor.authorLin, J.
dc.contributor.authorRazak, N.N.
dc.contributor.authorPretty, C.G.
dc.contributor.authorLeCompte, A.J.
dc.contributor.authorDocherty, P.D.
dc.contributor.authorParente, J.D.
dc.contributor.authorShaw, Geoff
dc.contributor.authorHann, C.E.
dc.contributor.authorChase, Geoff
dc.date.accessioned2011-05-03T23:21:36Z
dc.date.available2011-05-03T23:21:36Z
dc.date.issued2010en
dc.description.abstractA 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.en
dc.identifier.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.en
dc.identifier.urihttp://hdl.handle.net/10092/5165
dc.language.isoen
dc.publisherUniversity of Canterbury. Electrical and Computer Engineeringen
dc.publisherUniversity of Canterbury. Mathematics and Statisticsen
dc.publisherUniversity of Canterbury. Mechanical Engineeringen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subjectmodel-based controlen
dc.subjecttight blood glucose controlen
dc.subjectTGCen
dc.subjectblood glucoseen
dc.subjectinsulin therapyen
dc.subjectinsulin sensitivityen
dc.subjectcritical careen
dc.subjectpredictive performanceen
dc.subject.anzsrcFields of Research::49 - Mathematical sciences::4901 - Applied mathematics::490103 - Calculus of variations, mathematical aspects of systems theory and control theoryen
dc.subject.anzsrcFields of Research::40 - Engineering::4003 - Biomedical engineering::400303 - Biomechanical engineeringen
dc.subject.anzsrcFields of Research::32 - Biomedical and clinical sciences::3201 - Cardiovascular medicine and haematology::320102 - Haematologyen
dc.subject.anzsrcFields of Research::32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive careen
dc.titleIntensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patientsen
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