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Clinical Evaluation of a Personalized Artificial Pancreas

OBJECTIVE: An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden o...

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Autores principales: Dassau, Eyal, Zisser, Howard, Harvey, Rebecca A., Percival, Matthew W., Grosman, Benyamin, Bevier, Wendy, Atlas, Eran, Miller, Shahar, Nimri, Revital, Jovanovič, Lois, Doyle, Francis J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Diabetes Association 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3609541/
https://www.ncbi.nlm.nih.gov/pubmed/23193210
http://dx.doi.org/10.2337/dc12-0948
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author Dassau, Eyal
Zisser, Howard
Harvey, Rebecca A.
Percival, Matthew W.
Grosman, Benyamin
Bevier, Wendy
Atlas, Eran
Miller, Shahar
Nimri, Revital
Jovanovič, Lois
Doyle, Francis J.
author_facet Dassau, Eyal
Zisser, Howard
Harvey, Rebecca A.
Percival, Matthew W.
Grosman, Benyamin
Bevier, Wendy
Atlas, Eran
Miller, Shahar
Nimri, Revital
Jovanovič, Lois
Doyle, Francis J.
author_sort Dassau, Eyal
collection PubMed
description OBJECTIVE: An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS: We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or “brain,” can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS: Initial glucose values ranged from 84–251 mg/dL. Blood glucose was kept in the near-normal range (80–180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS: These encouraging short-term results reveal the ability of a control algorithm tailored to an individual’s glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.
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spelling pubmed-36095412014-04-01 Clinical Evaluation of a Personalized Artificial Pancreas Dassau, Eyal Zisser, Howard Harvey, Rebecca A. Percival, Matthew W. Grosman, Benyamin Bevier, Wendy Atlas, Eran Miller, Shahar Nimri, Revital Jovanovič, Lois Doyle, Francis J. Diabetes Care Original Research OBJECTIVE: An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS: We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or “brain,” can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS: Initial glucose values ranged from 84–251 mg/dL. Blood glucose was kept in the near-normal range (80–180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS: These encouraging short-term results reveal the ability of a control algorithm tailored to an individual’s glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes. American Diabetes Association 2013-04 2013-03-14 /pmc/articles/PMC3609541/ /pubmed/23193210 http://dx.doi.org/10.2337/dc12-0948 Text en © 2013 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
spellingShingle Original Research
Dassau, Eyal
Zisser, Howard
Harvey, Rebecca A.
Percival, Matthew W.
Grosman, Benyamin
Bevier, Wendy
Atlas, Eran
Miller, Shahar
Nimri, Revital
Jovanovič, Lois
Doyle, Francis J.
Clinical Evaluation of a Personalized Artificial Pancreas
title Clinical Evaluation of a Personalized Artificial Pancreas
title_full Clinical Evaluation of a Personalized Artificial Pancreas
title_fullStr Clinical Evaluation of a Personalized Artificial Pancreas
title_full_unstemmed Clinical Evaluation of a Personalized Artificial Pancreas
title_short Clinical Evaluation of a Personalized Artificial Pancreas
title_sort clinical evaluation of a personalized artificial pancreas
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3609541/
https://www.ncbi.nlm.nih.gov/pubmed/23193210
http://dx.doi.org/10.2337/dc12-0948
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