Literature DB >> 17705688

Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes.

Pier Giorgio Fabietti1, Valentina Canonico, Marco Orsini-Federici, Eugenio Sarti, Massimo Massi-Benedetti.   

Abstract

BACKGROUND: The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms.
METHODS: Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G(rms)) and the Clarke error grid analysis were used to evaluate the system efficacy.
RESULTS: Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G(rms), and clinical acceptability by the Clarke error grid analysis, in both clinical data series.
CONCLUSIONS: Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

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Year:  2007        PMID: 17705688     DOI: 10.1089/dia.2006.0030

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  6 in total

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6.  Personalized glucose forecasting for type 2 diabetes using data assimilation.

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  6 in total

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