Literature DB >> 12610005

Clinical performance of CGMS in type 1 diabetic patients treated by continuous subcutaneous insulin infusion using insulin analogs.

Bruno Guerci1, Michèle Floriot, Philip Böhme, Danielle Durain, Muriel Benichou, Stéphanie Jellimann, Pierre Drouin.   

Abstract

OBJECTIVE: Performance criteria have been established for in vitro blood glucose monitoring, particularly for the self-monitoring of blood glucose using glucose meters. Devices intended for use in the future, such as the continuous glucose monitoring system (CGMS), should satisfy similar criteria, particularly in diabetic patients under intensive therapy. RESEARCH DESIGN AND METHODS: The analysis was conducted on 18 type 1 diabetic patients (not controlled, HbA(1c) >7.5%) treated by external pump using insulin analogs. Each patient received a glucose sensor for 3 days during his/her hospitalization and was instructed in its operation. Medtronic criteria were used to determine the accuracy of the CGMS. In addition, the data were analyzed according to American Diabetes Association (ADA) criteria, Clarke Error Grid analysis, and method of residuals, with the glucose oxidase method using a Beckman analyzer used as the reference method. Specificity and sensitivity were evaluated from the viewpoint of accuracy in the detection of hypoglycemia. For nine patients, two glucose sensors were simultaneously inserted into an abdominal site to determine the reproducibility of the system. RESULTS-Among the 33 glucose sensors inserted, 6 (18%) were nonfunctional. The mean duration of CGMS recording was 63 +/- 12 h. From all of the 692 sets of data that paired glucose readings and CGMS, the coefficients of correlation ranged from 0.87 to 0.92 and the mean absolute error ranged from 12.8 to 15.7%. The time experienced in hypoglycemia (<55 mg/dl) was reported at 86 +/- 62 min/day. Only 39% of the CGMS values satisfied the ADA precision criteria to within +/-10%, and 19% of these values satisfied the future ADA precision criteria of accuracy to within +/-5%. The means of difference method showed that the CGMS slightly underestimated the plasma glucose values (mean = -12 mg/dl). Error grid analysis showed only 77% of the glucose sensor values were in zone A, and 98.9% were in zones A and B. Two values fell in zone C and a single value fell in zone D. The sensitivity and specificity of the CGMS to detect hypoglycemia were 33 and 96%, respectively. A total of 6666 paired sensor values were recorded with a coefficient of correlation of 0.84 with a coefficient of variation of 8.25%.
CONCLUSIONS: CGMS could be useful in routine clinical practice to provide much more information on the glucose profile than intermittent self-monitoring of blood glucose (SMBG). However, CGMS cannot be used as a replacement for glucose meters because it does not satisfy the conventional performance goals set down for in vitro glucose measurements and could therefore lead to clinically incorrect treatment decisions.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12610005     DOI: 10.2337/diacare.26.3.582

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


  24 in total

1.  Effect of short-term use of a continuous glucose monitoring system with a real-time glucose display and a low glucose alarm on incidence and duration of hypoglycemia in a home setting in type 1 diabetes mellitus.

Authors:  Raymond J Davey; Timothy W Jones; Paul A Fournier
Journal:  J Diabetes Sci Technol       Date:  2010-11-01

2.  A human pilot study of the fluorescence affinity sensor for continuous glucose monitoring in diabetes.

Authors:  Ralph Dutt-Ballerstadt; Colton Evans; Arun P Pillai; Eric Orzeck; Rafal Drabek; Ashok Gowda; Roger McNichols
Journal:  J Diabetes Sci Technol       Date:  2012-03-01

3.  Real-time continuous glucose monitoring in the clinical setting: the good, the bad, and the practical.

Authors:  Irene Mamkin; Svetlana Ten; Sonal Bhandari; Neesha Ramchandani
Journal:  J Diabetes Sci Technol       Date:  2008-09

4.  Using support vector machines to detect therapeutically incorrect measurements by the MiniMed CGMS.

Authors:  Jorge Bondia; Cristina Tarín; Winston García-Gabin; Eduardo Esteve; José Manuel Fernández-Real; Wifredo Ricart; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2008-07

5.  Continuous noninvasive glucose monitoring technology based on "occlusion spectroscopy".

Authors:  Orna Amir; Daphna Weinstein; Silviu Zilberman; Malka Less; Daniele Perl-Treves; Harel Primack; Aharon Weinstein; Efi Gabis; Boris Fikhte; Avraham Karasik
Journal:  J Diabetes Sci Technol       Date:  2007-07

6.  Development of a clinical type 1 diabetes metabolic system model and in silico simulation tool.

Authors:  Xing-Wei Wong; J Geoffrey Chase; Christopher E Hann; Thomas F Lotz; Jessica Lin; Aaron J Le; Geoffrey M Shaw
Journal:  J Diabetes Sci Technol       Date:  2008-05

7.  In silico simulation of long-term type 1 diabetes glycemic control treatment outcomes.

Authors:  Xing-Wei Wong; J Geoffrey Chase; Christopher E Hann; Thomas F Lotz; Jessica Lin; Aaron J Le Compte; Geoffrey M Shaw
Journal:  J Diabetes Sci Technol       Date:  2008-05

8.  Impact of islet transplantation on glycemic control as evidenced by a continuous glucose monitoring system.

Authors:  Lisa Gorn; Raquel N Faradji; Shari Messinger; Kathy Monroy; David A Baidal; Tatiana Froud; John Mastrototaro; Camillo Ricordi; Rodolfo Alejandro
Journal:  J Diabetes Sci Technol       Date:  2008-03

9.  The accuracy of the CGMS in children with type 1 diabetes: results of the diabetes research in children network (DirecNet) accuracy study.

Authors: 
Journal:  Diabetes Technol Ther       Date:  2003       Impact factor: 6.118

10.  The impact of parameter identification methods on drug therapy control in an intensive care unit.

Authors:  Christopher E Hann; J Geoffrey Chase; Michael F Ypma; Jos Elfring; Noorhafiz Mohd Nor; Piers Lawrence; Geoffrey M Shaw
Journal:  Open Med Inform J       Date:  2008-05-27
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.