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Poster communications

A Relevant Glucose-Insulin Model: Validation using Clinical Data

Abstract : A relevant glucose-insulin model with realistic asymptotic properties provide the computation of the tools for functional insulin therapy like basal rate, insulin sensitivity factor, carbo ratio insulin on board. The objective is to validate this model using clinical data of a large number of patients and to prove its ability to predict the behavior of glycemia and insulinemia dynamics. Data (CGM, insulin injections, carbohydrates) from 12 type-1 diabetic subjects were collected. An unconstrained optimization algorithm estimated the model’s parameters on n samples. Then, the estimated model was simulated on a set of n/2 samples as a cross-validation. The estimation was performed over at least 24 hours. The model-fit was very satisfactory as the mean standard deviation (SD) was 26 mg/dl. The cross-validation (performed over 16 hours on average) provides accurate glucose prediction with a mean SD of 40 mg/dl. Moreover, the computed tools for functional insulin therapy are consistent with the values determined by the clinical protocol. Realistic behavior of this model was already mathematically proven. In this study the relevant model was validated using clinical data as it accurately fitted the blood glucose of real patients. It was also shown that future values were well predicted. Thus it is a good candidate for a model based controller. As its parameters provide the computation of the tools for functional insulin therapy it will be useful for patient’s education.
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Poster communications
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Contributor : Nicolas Magdelaine Connect in order to contact the contributor
Submitted on : Friday, January 29, 2016 - 2:51:10 PM
Last modification on : Wednesday, April 27, 2022 - 3:42:56 AM


  • HAL Id : hal-01264635, version 1


Nicolas Magdelaine, Lucy Chaillous, Isabelle Guilhem, Jean-Yves Poirier, Michel Krempf, et al.. A Relevant Glucose-Insulin Model: Validation using Clinical Data. ATTD 2016; 9th International Conference on Advanced Technologies & Treatments of Diabetes, Feb 2016, Milan, Italy., Feb 2016, Milano, Italy. ⟨hal-01264635⟩



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