Literature DB >> 26204135

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial.

Eyal Dassau1, Sue A Brown1, Ananda Basu1, Jordan E Pinsker1, Yogish C Kudva1, Ravi Gondhalekar1, Steve Patek1, Dayu Lv1, Michele Schiavon1, Joon Bok Lee1, Chiara Dalla Man1, Ling Hinshaw1, Kristin Castorino1, Ashwini Mallad1, Vikash Dadlani1, Shelly K McCrady-Spitzer1, Molly McElwee-Malloy1, Christian A Wakeman1, Wendy C Bevier1, Paige K Bradley1, Boris Kovatchev1, Claudio Cobelli1, Howard C Zisser1, Francis J Doyle1.   

Abstract

CONTEXT: Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort.
OBJECTIVE: The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC.
DESIGN: This study reports a multicenter, outpatient, randomized, crossover clinical trial. PATIENTS: Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites.
INTERVENTIONS: Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session. MAIN OUTCOMES AND MEASURES: Time in the glucose range was 80-140 mg/dL, compared between both arms.
RESULTS: Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL.
CONCLUSIONS: A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

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Year:  2015        PMID: 26204135      PMCID: PMC4596045          DOI: 10.1210/jc.2015-2081

Source DB:  PubMed          Journal:  J Clin Endocrinol Metab        ISSN: 0021-972X            Impact factor:   5.958


  24 in total

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Journal:  Diabetes Technol Ther       Date:  2014-09-26       Impact factor: 6.118

2.  Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites.

Authors:  Howard Zisser; Eric Renard; Boris Kovatchev; Claudio Cobelli; Angelo Avogaro; Revital Nimri; Lalo Magni; Bruce A Buckingham; H Peter Chase; Francis J Doyle; John Lum; Peter Calhoun; Craig Kollman; Eyal Dassau; Anne Farret; Jerome Place; Marc Breton; Stacey M Anderson; Chiara Dalla Man; Simone Del Favero; Daniela Bruttomesso; Alessio Filippi; Rachele Scotton; Moshe Phillip; Eran Atlas; Ido Muller; Shahar Miller; Chiara Toffanin; Davide Martino Raimondo; Giuseppe De Nicolao; Roy W Beck
Journal:  Diabetes Technol Ther       Date:  2014-07-08       Impact factor: 6.118

3.  Periodic-zone model predictive control for diurnal closed-loop operation of an artificial pancreas.

Authors:  Ravi Gondhalekar; Eyal Dassau; Howard C Zisser; Francis J Doyle
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5.  Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events.

Authors:  Benyamin Grosman; Eyal Dassau; Howard C Zisser; Lois Jovanovic; Francis J Doyle
Journal:  J Diabetes Sci Technol       Date:  2010-07-01

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7.  Reduction to normal of plasma glucose in juvenile diabetes by subcutaneous administration of insulin with a portable infusion pump.

Authors:  W V Tamborlane; R S Sherwin; M Genel; P Felig
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8.  Continuous subcutaneous insulin infusion: an approach to achieving normoglycaemia.

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10.  Clinical evaluation of a personalized artificial pancreas.

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Authors:  Sheri R Colberg; Wendy C Bevier; Jordan E Pinsker; Joon Bok Lee; Brigid Ehrlich; Eyal Dassau; Francis J Doyle; Kong Y Chen; David Kerr
Journal:  J Diabetes Sci Technol       Date:  2016-06-28

2.  Evaluation of an Artificial Pancreas with Enhanced Model Predictive Control and a Glucose Prediction Trust Index with Unannounced Exercise.

Authors:  Jordan E Pinsker; Alejandro J Laguna Sanz; Joon Bok Lee; Mei Mei Church; Camille Andre; Laura E Lindsey; Francis J Doyle; Eyal Dassau
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3.  Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

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Journal:  Diabetes Technol Ther       Date:  2019-06       Impact factor: 6.118

4.  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
Journal:  J Diabetes Sci Technol       Date:  2016-12-01

5.  Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial.

Authors:  Gregory P Forlenza; Sunil Deshpande; Trang T Ly; Daniel P Howsmon; Faye Cameron; Nihat Baysal; Eric Mauritzen; Tatiana Marcal; Lindsey Towers; B Wayne Bequette; Lauren M Huyett; Jordan E Pinsker; Ravi Gondhalekar; Francis J Doyle; David M Maahs; Bruce A Buckingham; Eyal Dassau
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6.  Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

Authors:  Jordan E Pinsker; Joon Bok Lee; Eyal Dassau; Dale E Seborg; Paige K Bradley; Ravi Gondhalekar; Wendy C Bevier; Lauren Huyett; Howard C Zisser; Francis J Doyle
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7.  Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.

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

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Journal:  Automatica (Oxf)       Date:  2016-06-01       Impact factor: 5.944

9.  Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties.

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10.  Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

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