Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model (2005)
Type of ContentJournal Article
PublisherUniversity of Canterbury. Mechanical Engineering.
Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an Intensive Care Unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verifl the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycernic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.
CitationHann, C.E., Chase, J.G., Lin, J., Lotz, T., Doran, C.V., Shaw, G.M. (2005) Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Computer Methods and Programs in Biomedicine, 77(3), pp. 259-270.
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KeywordsCritical care; Glucose; Hyperglycemia; Insulin; Modeling; Integrals; Parameter identification
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Lin, J.; Chase, Geoff; Shaw, Geoff; Lotz, T.; Hann, C.E.; Doran, C.V.; Lee, D. (University of Canterbury. Mathematics and Statistics.University of Canterbury. Mechanical Engineering., 2004)Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using ...
Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care Chase, Geoff; Hann, C.E.; Jackson, M.; Lin, J.; Lotz, T.; Wong, X-W.; Shaw, Geoff (University of Canterbury. Mechanical Engineering., 2006)Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, ...
Integral-based identification of a physiological insulin and glucose model on euglycaemic clamp trials and IVGTT trials (Invited) Lotz, T.; Chase, Geoff; McAuley, K.A.; Lin, J.; Wong, J.; Hann, C.E.; Andreassen, S. (University of Canterbury. Mechanical Engineering., 2006)Modelling can enhance the diagnosis and control of metabolic disorders. Clinical effectiveness demands physiological accuracy, patient specificity and identification with limited data. A two-compartment insulin kinetics ...