Literature DB >> 16735960

Value and limitations of the Continuous Glucose Monitoring System in the management of type 1 diabetes.

V Melki1, F Ayon, M Fernandez, H Hanaire-Broutin.   

Abstract

The CGMS (Continuous Glucose Monitoring System) is a portable device allowing continuous measuring of glucose. It provides recordings of at least 72 h, during which 288 measures/day are performed. Results are visualised in the form of a set of curves, illustrating the variations in blood glucose levels over time. The quality of the records has often been questioned by several authors. Some of the system's physiologically related limitations can be explained by the less than perfect coincidence of variations in glucose levels observed in the interstitial tissue, where CGMS measurings are done, and in the blood, where calibrations are performed. Other limitations, such as defects in accuracy or in reproducibility of tracings or premature curtailments of recordings, are due to technical weaknesses which were considerably improved during the past few years, particularly with regard to the quality of the electrodes providing a more stable signal over time. In clinical practice, CGMS is a tool for investigating the glycaemic patterns of diabetic patients in conjonction with SMBG. It allows the identification of overlooked hyper- or hypoglycaemia. Generally well accepted, it is a usefull tool to analyse the nocturnal period, or any situation where glucose checks are rare. The visual nature of its results provides a facilitating support in the discussion between the patient and the care-provider during consultations or educational sessions. CGMS utilisation was proposed for guiding treatment adjustment. At present, it is still difficult to state with certainty that this tool allows effective improvement in the metabolic control of patients with type 1 diabetes, in view of the paucity of controlled studies showing an impact on HbA1c values or on the frequency of hypoglycaemia, even if such a tendency emerges from most non-controlled intervention trials.

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Year:  2006        PMID: 16735960     DOI: 10.1016/s1262-3636(07)70258-6

Source DB:  PubMed          Journal:  Diabetes Metab        ISSN: 1262-3636            Impact factor:   6.041


  10 in total

1.  Telemedicine-based KADIS combined with CGMS has high potential for improving outpatient diabetes care.

Authors:  Eckhard Salzsieder; Petra Augstein; Lutz Vogt; Klaus-Dieter Kohnert; Peter Heinke; Ernst-Joachim Freyse; Abdel Azim Ahmed; Zakia Metwali; Iman Salman; Omer Attef
Journal:  J Diabetes Sci Technol       Date:  2007-07

2.  Continuous glucose monitoring in gastroparesis.

Authors:  Zeeshan Ramzan; Frank Duffy; Javier Gomez; Robert S Fisher; Henry P Parkman
Journal:  Dig Dis Sci       Date:  2011-07-07       Impact factor: 3.199

3.  Description and preliminary evaluation of a diabetes technology simulation course.

Authors:  Rebecca D Wilson; Marilyn Bailey; Mary E Boyle; Karen M Seifert; Karla Y Cortez; Leslie J Baker; Michael J Hovan; Jan Stepanek; Curtiss B Cook
Journal:  J Diabetes Sci Technol       Date:  2013-11-01

4.  Can technology improve adherence to long-term therapies?

Authors:  Gérard Reach
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

5.  Postprandial plasma glucose response and gastrointestinal symptom severity in patients with diabetic gastroparesis.

Authors:  Eva A Olausson; Håkan Grundin; Mats Isaksson; Christina Brock; Asbjørn M Drewes; Stig Attvall; Magnus Simrén
Journal:  J Diabetes Sci Technol       Date:  2014-05-06

6.  A novel algorithm for prediction and detection of hypoglycemia based on continuous glucose monitoring and heart rate variability in patients with type 1 diabetes.

Authors:  Simon Lebech Cichosz; Jan Frystyk; Ole K Hejlesen; Lise Tarnow; Jesper Fleischer
Journal:  J Diabetes Sci Technol       Date:  2014-03-31

Review 7.  Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel.

Authors:  Sanjay Kalra; Shehla Shaikh; Gagan Priya; Manas P Baruah; Abhyudaya Verma; Ashok K Das; Mona Shah; Sambit Das; Deepak Khandelwal; Debmalya Sanyal; Sujoy Ghosh; Banshi Saboo; Ganapathi Bantwal; Usha Ayyagari; Daphne Gardner; Cecilia Jimeno; Nancy E Barbary; Khadijah A Hafidh; Jyoti Bhattarai; Tania T Minulj; Hendra Zufry; Uditha Bulugahapitiya; Moosa Murad; Alexander Tan; Selim Shahjada; Mijinyawa B Bello; Prasad Katulanda; Gracjan Podgorski; Wajeeha I AbuHelaiqa; Rima Tan; Ali Latheef; Sedeshan Govender; Samir H Assaad-Khalil; Cecilia Kootin-Sanwu; Ansumali Joshi; Faruque Pathan; Diana A Nkansah
Journal:  Diabetes Ther       Date:  2020-12-26       Impact factor: 2.945

8.  A machine learning-based on-demand sweat glucose reporting platform.

Authors:  Devangsingh Sankhala; Abha Umesh Sardesai; Madhavi Pali; Kai-Chun Lin; Badrinath Jagannath; Sriram Muthukumar; Shalini Prasad
Journal:  Sci Rep       Date:  2022-02-14       Impact factor: 4.379

9.  Continuous glucose monitoring.

Authors:  Kaushik Pandit
Journal:  Indian J Endocrinol Metab       Date:  2012-12

Review 10.  Continuous Glucose Monitoring Systems: A Review.

Authors:  Sandeep Kumar Vashist
Journal:  Diagnostics (Basel)       Date:  2013-10-29
  10 in total

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