Literature DB >> 23046396

Assessing performance of closed-loop insulin delivery systems by continuous glucose monitoring: drawbacks and way forward.

Roman Hovorka1, Marianna Nodale, Ahmad Haidar, Malgorzata E Wilinska.   

Abstract

BACKGROUND: We investigated whether continuous glucose monitoring (CGM) levels can accurately assess glycemic control while directing closed-loop insulin delivery. SUBJECTS AND METHODS: Data were analyzed retrospectively from 33 subjects with type 1 diabetes who underwent closed-loop and conventional pump therapy on two separate nights. Glycemic control was evaluated by reference plasma glucose and contrasted against three methods based on Navigator (Abbott Diabetes Care, Alameda, CA) CGM levels.
RESULTS: Glucose mean and variability were estimated by unmodified CGM levels with acceptable clinical accuracy. Time when glucose was in target range was overestimated by CGM during closed-loop nights (CGM vs. plasma glucose median [interquartile range], 86% [65-97%] vs. 75% [59-91%]; P=0.04) but not during conventional pump therapy (57% [32-72%] vs. 51% [29-68%]; P=0.82) providing comparable treatment effect (mean [SD], 28% [29%] vs. 23% [21%]; P=0.11). Using the CGM measurement error of 15% derived from plasma glucose-CGM pairs (n=4,254), stochastic interpretation of CGM gave unbiased estimate of time in target during both closed-loop (79% [62-86%] vs. 75% [59-91%]; P=0.24) and conventional pump therapy (54% [33-66%] vs. 51% [29-68%]; P=0.44). Treatment effect (23% [24%] vs. 23% [21%]; P=0.96) and time below target were accurately estimated by stochastic CGM. Recalibrating CGM using reference plasma glucose values taken at the start and end of overnight closed-loop was not superior to stochastic CGM.
CONCLUSIONS: CGM is acceptable to estimate glucose mean and variability, but without adjustment it may overestimate benefit of closed-loop. Stochastic CGM provided unbiased estimate of time when glucose is in target and below target and may be acceptable for assessment of closed-loop in the outpatient setting.

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Year:  2012        PMID: 23046396      PMCID: PMC3540898          DOI: 10.1089/dia.2012.0185

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


  26 in total

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Journal:  Diabetes Care       Date:  2002-03       Impact factor: 19.112

2.  A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas.

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Journal:  Diabetes Technol Ther       Date:  2005-02       Impact factor: 6.118

3.  Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: summary of the results.

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4.  A bihormonal closed-loop artificial pancreas for type 1 diabetes.

Authors:  Firas H El-Khatib; Steven J Russell; David M Nathan; Robert G Sutherlin; Edward R Damiano
Journal:  Sci Transl Med       Date:  2010-04-14       Impact factor: 17.956

5.  Time lag characterization of two continuous glucose monitoring systems.

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Journal:  Diabetes Res Clin Pract       Date:  2010-03       Impact factor: 5.602

6.  Prevention of hypoglycemia by using low glucose suspend function in sensor-augmented pump therapy.

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Journal:  J Diabetes Sci Technol       Date:  2011-01-01

9.  MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes.

Authors:  Eran Atlas; Revital Nimri; Shahar Miller; Eli A Grunberg; Moshe Phillip
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10.  Closed-loop insulin delivery using a subcutaneous glucose sensor and intraperitoneal insulin delivery: feasibility study testing a new model for the artificial pancreas.

Authors:  Eric Renard; Jerome Place; Martin Cantwell; Hugues Chevassus; Cesar C Palerm
Journal:  Diabetes Care       Date:  2009-10-21       Impact factor: 19.112

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  20 in total

1.  Algorithms for a closed-loop artificial pancreas: the case for model predictive control.

Authors:  B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2013-11-01

2.  Evaluation of stochastic adjustment for glucose sensor bias during closed-loop insulin delivery.

Authors:  Craig Kollman; Peter Calhoun; John Lum; Werner Sauer; Roy W Beck
Journal:  Diabetes Technol Ther       Date:  2013-11-15       Impact factor: 6.118

3.  Analysis of continuous glucose monitoring data to assess outpatient closed-loop studies: considerations for different sensors.

Authors:  Tina Maria Mitre; Laurent Legault; Rémi Rabasa-Lhoret; Ahmad Haidar
Journal:  Diabetes Technol Ther       Date:  2014-01-21       Impact factor: 6.118

Review 4.  Managing diabetes with nanomedicine: challenges and opportunities.

Authors:  Omid Veiseh; Benjamin C Tang; Kathryn A Whitehead; Daniel G Anderson; Robert Langer
Journal:  Nat Rev Drug Discov       Date:  2014-11-28       Impact factor: 84.694

5.  Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems.

Authors:  Cosimo Scuffi
Journal:  Eur Endocrinol       Date:  2014-02-28

Review 6.  Drug Delivery Strategies for the Treatment of Metabolic Diseases.

Authors:  Sanjun Shi; Na Kong; Chan Feng; Aram Shajii; Claire Bejgrowicz; Wei Tao; Omid C Farokhzad
Journal:  Adv Healthc Mater       Date:  2019-04-08       Impact factor: 9.933

7.  Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study.

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Journal:  J Diabetes Sci Technol       Date:  2014-08-04

Review 8.  Nanomedicine-Based Strategies for Diabetes: Diagnostics, Monitoring, and Treatment.

Authors:  Luke R Lemmerman; Devleena Das; Natalia Higuita-Castro; Raghavendra G Mirmira; Daniel Gallego-Perez
Journal:  Trends Endocrinol Metab       Date:  2020-03-04       Impact factor: 12.015

9.  Performance comparison of the medtronic sof-sensor and enlite glucose sensors in inpatient studies of individuals with type 1 diabetes.

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Journal:  Diabetes Technol Ther       Date:  2013-05-31       Impact factor: 6.118

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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