Optimal Tight Glycaemic Control Supported by Differential Geometric Methods

Optimal control of an Intensive Care Unit (ICU) metabolic model from nonlinear control point of view is presented in the current paper. The transformation of a clinically validated nonlinear model into a series of integrators via exact linearization and asymptotic output tracking is performed. Both methods need the value of the state variables; therefore Kalman-filter extended for nonlinear systems is applied. Finally, linear optimal LQ method is applied on the ICU model handled with differential geometric approach. Results are demonstrated on input data recorded in actual clinical environment.

[1]  Christopher E. Hann,et al.  Model-based glycaemic control in critical care - A review of the state of the possible , 2006, Biomed. Signal Process. Control..

[2]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[3]  Niels Haverbeke,et al.  Glycemia Prediction in Critically Ill Patients Using an Adaptive Modeling Approach , 2007, Journal of diabetes science and technology.

[4]  A. Isidori.  Nonlinear Control Systems , 1985 .

[5]  Liu Xinbing,et al.  Intensive insulin therapy for the critically ill patients with stress hyperglycemia , 2008 .

[6]  S Andreassen,et al.  Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study. , 2006, Medical engineering & physics.

[7]  G. V. Berghe,et al.  Intensive insulin therapy in critically ill patients. , 2001, The New England journal of medicine.

[8]  H. Gerstein,et al.  Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview , 2000, The Lancet.

[9]  G. Van den Berghe,et al.  Analysis of healthcare resource utilization with intensive insulin therapy in critically ill patients* , 2006, Critical care medicine.

[10]  Christopher E. Hann,et al.  Monte Carlo analysis of a new model-based method for insulin sensitivity testing , 2008, Comput. Methods Programs Biomed..

[11]  Dominic S. Lee,et al.  Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change , 2008, Critical care.