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

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    Author
    Lin, 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.
    Date
    2010
    Permanent Link
    http://hdl.handle.net/10092/5165

    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.

    Subjects
    model-based control
     
    tight blood glucose control
     
    TGC
     
    blood glucose
     
    insulin therapy
     
    insulin sensitivity
     
    critical care
     
    predictive performance
     
    Field of Research::01 - Mathematical Sciences::0102 - Applied Mathematics::010203 - Calculus of Variations, Systems Theory and Control Theory
     
    Field of Research::09 - Engineering::0903 - Biomedical Engineering::090302 - Biomechanical Engineering
     
    Field of Research::11 - Medical and Health Sciences::1102 - Cardiovascular Medicine and Haematology::110202 - Haematology
     
    Field of Research::11 - Medical and Health Sciences::1103 - Clinical Sciences::110310 - Intensive Care
    Collections
    • Engineering: Conference Contributions [1920]
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