Literature DB >> 24876534

The UVA/PADOVA Type 1 Diabetes Simulator: New Features.

Chiara Dalla Man1, Francesco Micheletto1, Dayu Lv2, Marc Breton2, Boris Kovatchev2, Claudio Cobelli3.   

Abstract

Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013). The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions. S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF. S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.
© 2014 Diabetes Technology Society.

Entities:  

Keywords:  computer simulation; diabetes control; modeling

Year:  2014        PMID: 24876534      PMCID: PMC4454102          DOI: 10.1177/1932296813514502

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  20 in total

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Journal:  Diabetes       Date:  2011-11       Impact factor: 9.461

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Authors:  Ling Hinshaw; Chiara Dalla Man; Debashis K Nandy; Ahmed Saad; Adil E Bharucha; James A Levine; Robert A Rizza; Rita Basu; Rickey E Carter; Claudio Cobelli; Yogish C Kudva; Ananda Basu
Journal:  Diabetes       Date:  2013-02-27       Impact factor: 9.461

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  99 in total

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Authors:  Michele Schiavon; Chiara Dalla Man; Yogish C Kudva; Ananda Basu; Claudio Cobelli
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4.  Circadian variability of insulin sensitivity: physiological input for in silico artificial pancreas.

Authors:  Roberto Visentin; Chiara Dalla Man; Yogish C Kudva; Ananda Basu; Claudio Cobelli
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5.  An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions.

Authors:  Alejandro J Laguna Sanz; Francis J Doyle; Eyal Dassau
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6.  Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.

Authors:  Ravi Gondhalekar; Eyal Dassau; Francis J Doyle
Journal:  Automatica (Oxf)       Date:  2018-03-20       Impact factor: 5.944

7.  Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study.

Authors:  Enrique Campos-Náñez; Marc D Breton
Journal:  J Diabetes Sci Technol       Date:  2017-05-31

8.  Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study.

Authors:  Enrique Campos-Náñez; Kurt Fortwaengler; Marc D Breton
Journal:  J Diabetes Sci Technol       Date:  2017-06-01

9.  Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes.

Authors:  Ravi Gondhalekar; Eyal Dassau; Francis J Doyle
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10.  Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties.

Authors:  Dawei Shi; Eyal Dassau; Francis J Doyle
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