Literature DB >> 19578532

Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology.

Boris Kovatchev1, William Clarke.   

Abstract

Therapeutic advances in type 1 diabetes (T1DM) are currently focused on developing a closed-loop control system using a continuous glucose monitor (CGM), subcutaneous insulin delivery, and a control algorithm. Because a CGM assesses blood glucose indirectly (and therefore often inaccurately), it limits the effectiveness of the controller. In order to improve the quality of CGM data, a series of analyses are suggested. These analyses evaluate and compensate for CGM errors, assess risks associated with glucose variability, predict glucose fluctuation, and forecast hypo- and hyperglycemia. These analyses are illustrated with data collected using the MiniMed CGMS® (Medtronic, Northridge, CA) and Freestyle Navigator(™) (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work with CGM data because consecutive CGM readings are highly interdependent.

Entities:  

Keywords:  CGM; continuous glucose monitoring; hypoglycemia; prediction methods; time series

Year:  2008        PMID: 19578532      PMCID: PMC2705169          DOI: 10.1177/193229680800200125

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


  24 in total

Review 1.  Continuous glucose monitoring: roadmap for 21st century diabetes therapy.

Authors:  David C Klonoff
Journal:  Diabetes Care       Date:  2005-05       Impact factor: 19.112

2.  A direct comparison of insulin aspart and insulin lispro in patients with type 1 diabetes.

Authors:  Johannes Plank; Andrea Wutte; Gernot Brunner; Andrea Siebenhofer; Barbara Semlitsch; Romana Sommer; Sabine Hirschberger; Thomas R Pieber
Journal:  Diabetes Care       Date:  2002-11       Impact factor: 19.112

3.  Feasibility of automating insulin delivery for the treatment of type 1 diabetes.

Authors:  Garry M Steil; Kerstin Rebrin; Christine Darwin; Farzam Hariri; Mohammed F Saad
Journal:  Diabetes       Date:  2006-12       Impact factor: 9.461

4.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application.

Authors:  Boris P Kovatchev; William L Clarke; Marc Breton; Kenneth Brayman; Anthony McCall
Journal:  Diabetes Technol Ther       Date:  2005-12       Impact factor: 6.118

5.  Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT.

Authors:  Roman Hovorka; Fariba Shojaee-Moradie; Paul V Carroll; Ludovic J Chassin; Ian J Gowrie; Nicola C Jackson; Romulus S Tudor; A Margot Umpleby; Richard H Jones
Journal:  Am J Physiol Endocrinol Metab       Date:  2002-05       Impact factor: 4.310

6.  The artificial pancreas: how sweet engineering will solve bitter problems.

Authors:  David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2007-01

7.  Closing the loop: the adicol experience.

Authors:  Roman Hovorka; Ludovic J Chassin; Malgorzata E Wilinska; Valentina Canonico; Joyce Akwe Akwi; Marco Orsini Federici; Massimo Massi-Benedetti; Ivo Hutzli; Claudio Zaugg; Heiner Kaufmann; Marcel Both; Thomas Vering; Helga C Schaller; Lukas Schaupp; Manfred Bodenlenz; Thomas R Pieber
Journal:  Diabetes Technol Ther       Date:  2004-06       Impact factor: 6.118

8.  Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

Authors:  Francesca Zanderigo; Giovanni Sparacino; Boris Kovatchev; Claudio Cobelli
Journal:  J Diabetes Sci Technol       Date:  2007-09

9.  Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp.

Authors:  Christopher King; Stacey M Anderson; Marc Breton; William L Clarke; Boris P Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2007-05

10.  Assessment of the severity of hypoglycemia and glycemic lability in type 1 diabetic subjects undergoing islet transplantation.

Authors:  Edmond A Ryan; Tami Shandro; Kristy Green; Breay W Paty; Peter A Senior; David Bigam; A M James Shapiro; Marie-Christine Vantyghem
Journal:  Diabetes       Date:  2004-04       Impact factor: 9.461

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  20 in total

1.  Measures of Risk and Glucose Variability in Adults Versus Youths.

Authors:  Boris P Kovatchev
Journal:  Diabetes Technol Ther       Date:  2015-09-08       Impact factor: 6.118

2.  Report from IPITA-TTS Opinion Leaders Meeting on the Future of β-Cell Replacement.

Authors:  Stephen T Bartlett; James F Markmann; Paul Johnson; Olle Korsgren; Bernhard J Hering; David Scharp; Thomas W H Kay; Jonathan Bromberg; Jon S Odorico; Gordon C Weir; Nancy Bridges; Raja Kandaswamy; Peter Stock; Peter Friend; Mitsukazu Gotoh; David K C Cooper; Chung-Gyu Park; Phillip OʼConnell; Cherie Stabler; Shinichi Matsumoto; Barbara Ludwig; Pratik Choudhary; Boris Kovatchev; Michael R Rickels; Megan Sykes; Kathryn Wood; Kristy Kraemer; Albert Hwa; Edward Stanley; Camillo Ricordi; Mark Zimmerman; Julia Greenstein; Eduard Montanya; Timo Otonkoski
Journal:  Transplantation       Date:  2016-02       Impact factor: 4.939

3.  Graphical and numerical evaluation of continuous glucose sensing time lag.

Authors:  Boris P Kovatchev; Devin Shields; Marc Breton
Journal:  Diabetes Technol Ther       Date:  2009-03       Impact factor: 6.118

Review 4.  Essential elements of the native glucoregulatory system, which, if appreciated, may help improve the function of glucose controllers in the intensive care unit setting.

Authors:  Leon DeJournett
Journal:  J Diabetes Sci Technol       Date:  2010-01-01

5.  Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring.

Authors:  Boris P Kovatchev
Journal:  Diabetes Technol Ther       Date:  2015-05-15       Impact factor: 6.118

6.  Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator.

Authors:  Marc D Breton; Rolf Hinzmann; Enrique Campos-Nañez; Susan Riddle; Michael Schoemaker; Guenther Schmelzeisen-Redeker
Journal:  J Diabetes Sci Technol       Date:  2016-12-13

7.  Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.

Authors:  Darpit Dave; Daniel J DeSalvo; Balakrishna Haridas; Siripoom McKay; Akhil Shenoy; Chester J Koh; Mark Lawley; Madhav Erraguntla
Journal:  J Diabetes Sci Technol       Date:  2020-06-01

Review 8.  Glycemic Variability: Risk Factors, Assessment, and Control.

Authors:  Boris Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2019-01-29

9.  Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring.

Authors:  Boris P Kovatchev; Stephen D Patek; Edward Andrew Ortiz; Marc D Breton
Journal:  Diabetes Technol Ther       Date:  2014-12-01       Impact factor: 6.118

10.  Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels.

Authors:  Claudia Eberle; Christoph Ament
Journal:  J Diabetes Sci Technol       Date:  2012-09-01
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