Literature DB >> 29030383

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia.

Eyal Dassau1,2, Jordan E Pinsker2, Yogish C Kudva3, Sue A Brown4, Ravi Gondhalekar1,2, Chiara Dalla Man5, Steve Patek4, Michele Schiavon5, Vikash Dadlani3, Isuru Dasanayake2,6, Mei Mei Church2, Rickey E Carter7, Wendy C Bevier2, Lauren M Huyett2,6, Jonathan Hughes4, Stacey Anderson4, Dayu Lv4, Elaine Schertz4, Emma Emory4, Shelly K McCrady-Spitzer3, Tyler Jean2, Paige K Bradley2, Ling Hinshaw3, Alejandro J Laguna Sanz1,2, Ananda Basu3, Boris Kovatchev4, Claudio Cobelli5, Francis J Doyle8,2.   

Abstract

OBJECTIVE: Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS: Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials.
RESULTS: Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events.
CONCLUSIONS: Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.
© 2017 by the American Diabetes Association.

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Year:  2017        PMID: 29030383      PMCID: PMC5711334          DOI: 10.2337/dc17-1188

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  35 in total

1.  Clinical update on optimal prandial insulin dosing using a refined run-to-run control algorithm.

Authors:  Howard Zisser; Cesar C Palerm; Wendy C Bevier; Francis J Doyle; Lois Jovanovic
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

2.  Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry.

Authors:  Kellee M Miller; Nicole C Foster; Roy W Beck; Richard M Bergenstal; Stephanie N DuBose; Linda A DiMeglio; David M Maahs; William V Tamborlane
Journal:  Diabetes Care       Date:  2015-06       Impact factor: 19.112

3.  Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

Authors:  Fraser Cameron; Günter Niemeyer; Darrell M Wilson; B Wayne Bequette; Kari S Benassi; Paula Clinton; Bruce A Buckingham
Journal:  Diabetes Technol Ther       Date:  2014-09-26       Impact factor: 6.118

4.  "Learning" Can Improve the Blood Glucose Control Performance for Type 1 Diabetes Mellitus.

Authors:  Youqing Wang; Jinping Zhang; Fanmao Zeng; Na Wang; Xiaoping Chen; Bo Zhang; Dong Zhao; Wenying Yang; Claudio Cobelli
Journal:  Diabetes Technol Ther       Date:  2017-01-06       Impact factor: 6.118

5.  2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial.

Authors:  Jort Kropff; Simone Del Favero; Jerome Place; Chiara Toffanin; Roberto Visentin; Marco Monaro; Mirko Messori; Federico Di Palma; Giordano Lanzola; Anne Farret; Federico Boscari; Silvia Galasso; Paolo Magni; Angelo Avogaro; Patrick Keith-Hynes; Boris P Kovatchev; Daniela Bruttomesso; Claudio Cobelli; J Hans DeVries; Eric Renard; Lalo Magni
Journal:  Lancet Diabetes Endocrinol       Date:  2015-09-30       Impact factor: 32.069

6.  Comparative pharmacokinetics and pharmacodynamics of the novel rapid-acting insulin analogue, insulin aspart, in healthy volunteers.

Authors:  P D Home; L Barriocanal; A Lindholm
Journal:  Eur J Clin Pharmacol       Date:  1999-05       Impact factor: 2.953

7.  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
Journal:  Automatica (Oxf)       Date:  2016-06-01       Impact factor: 5.944

8.  A Run-to-Run Control Strategy to Adjust Basal Insulin Infusion Rates in Type 1 Diabetes.

Authors:  Cesar C Palerm; Howard Zisser; Lois Jovanovič; Francis J Doyle
Journal:  J Process Control       Date:  2008       Impact factor: 3.666

9.  Home Use of an Artificial Beta Cell in Type 1 Diabetes.

Authors:  H Thabit; M Tauschmann; J M Allen; L Leelarathna; S Hartnell; M E Wilinska; C L Acerini; S Dellweg; C Benesch; L Heinemann; J K Mader; M Holzer; H Kojzar; J Exall; J Yong; J Pichierri; K D Barnard; C Kollman; P Cheng; P C Hindmarsh; F M Campbell; S Arnolds; T R Pieber; M L Evans; D B Dunger; R Hovorka
Journal:  N Engl J Med       Date:  2015-09-17       Impact factor: 91.245

10.  Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study.

Authors:  Hood Thabit; Alexandra Lubina-Solomon; Marietta Stadler; Lalantha Leelarathna; Emma Walkinshaw; Andrew Pernet; Janet M Allen; Ahmed Iqbal; Pratik Choudhary; Kavita Kumareswaran; Marianna Nodale; Chloe Nisbet; Malgorzata E Wilinska; Katharine D Barnard; David B Dunger; Simon R Heller; Stephanie A Amiel; Mark L Evans; Roman Hovorka
Journal:  Lancet Diabetes Endocrinol       Date:  2014-06-16       Impact factor: 32.069

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  18 in total

1.  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
Journal:  Diabetes Technol Ther       Date:  2018-07       Impact factor: 6.118

Review 2.  Designing a bioelectronic treatment for Type 1 diabetes: targeted parasympathetic modulation of insulin secretion.

Authors:  Elliott W Dirr; Morgan E Urdaneta; Yogi Patel; Richard D Johnson; Martha Campbell-Thompson; Kevin J Otto
Journal:  Bioelectron Med (Lond)       Date:  2020-07-28

3.  Ongoing Debate About Models for Artificial Pancreas Systems and In Silico Studies.

Authors:  Gregory P Forlenza
Journal:  Diabetes Technol Ther       Date:  2018-03       Impact factor: 6.118

4.  The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results.

Authors:  Pau Herrero; Mohamed El-Sharkawy; John Daniels; Narvada Jugnee; Chukwuma N Uduku; Monika Reddy; Nick Oliver; Pantelis Georgiou
Journal:  J Diabetes Sci Technol       Date:  2019-10-14

Review 5.  Advances in Closed-Loop Insulin Delivery Systems in Patients with Type 1 Diabetes.

Authors:  Vikash Dadlani; Jordan E Pinsker; Eyal Dassau; Yogish C Kudva
Journal:  Curr Diab Rep       Date:  2018-08-29       Impact factor: 4.810

6.  Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy.

Authors:  Michele Schiavon; Chiara Dalla Man; Claudio Cobelli
Journal:  Diabetes Technol Ther       Date:  2018-02       Impact factor: 6.118

7.  Design and Clinical Evaluation of the Interoperable Artificial Pancreas System (iAPS) Smartphone App: Interoperable Components with Modular Design for Progressive Artificial Pancreas Research and Development.

Authors:  Sunil Deshpande; Jordan E Pinsker; Stamatina Zavitsanou; Dawei Shi; Randy Tompot; Mei Mei Church; Camille Andre; Francis J Doyle; Eyal Dassau
Journal:  Diabetes Technol Ther       Date:  2018-12-14       Impact factor: 6.118

8.  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

9.  The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability.

Authors:  Stacey M Anderson; Eyal Dassau; Dan Raghinaru; John Lum; Sue A Brown; Jordan E Pinsker; Mei Mei Church; Carol Levy; David Lam; Yogish C Kudva; Bruce Buckingham; Gregory P Forlenza; R Paul Wadwa; Lori Laffel; Francis J Doyle; J Hans DeVries; Eric Renard; Claudio Cobelli; Federico Boscari; Simone Del Favero; Boris P Kovatchev
Journal:  Diabetes Technol Ther       Date:  2019-01-16       Impact factor: 6.118

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
Journal:  IEEE Trans Biomed Eng       Date:  2018-08-21       Impact factor: 4.538

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