Literature DB >> 12828820

Analysis of continuous glucose monitoring data from non-diabetic and diabetic children: a tale of two algorithms.

Stuart A Weinzimer1, Maria C DeLucia, Elizabeth A Boland, Amy Steffen, William V Tamborlane.   

Abstract

Use of the Medtronic MiniMed Continuous Glucose Monitoring System (CGMS) in non-diabetic children has revealed many low and high sensor glucose (SG) values, suggesting that the original analytical algorithm (Solutions 2.0) might be overreading glycemic excursions. A revised algorithm (Solutions 3.0) was introduced in 2001. Our aim was to compare analyses of the same sensor profiles using both programs. Twenty-five lean, non-diabetic subjects (mean age 14 +/- 4 years) underwent continuous glucose monitoring with CGMS for up to 72 h. Sensor tracings were analyzed with both algorithms and compared. Separate analyses were performed for nocturnal readings (12-6 a.m.). Mean SG values were similar (103 +/- 24 mg/dL for version 2.0 vs. 100 +/- 14 for version 3.0), but the distribution was significantly different: 13.8% of total SG were <70 mg/dL by version 2.0 versus 8.2% by version 3.0 (p < 0.001), and 7.7% of total SG were >150 mg/dL by version 2.0 versus 4.7% by version 3.0 (p = 0.02). Of nocturnal SG values, 25.8% were <70 mg/dL by version 2.0 compared with 17.9% by version 3.0, and 9.4% were >150 mg/dL by version 2.0 compared with 4.0% by version 3.0. In lean non-diabetic children, Solutions 2.0 identified significantly more hypoglycemia and hyperglycemia than Solutions 3.0. Similar analyses in 40 children with type 1 diabetes revealed no significant differences. Solutions 3.0 may be a more useful algorithm for preventing over-reading of low and high SG readings in non-diabetic children, whereas both algorithms give similar results in children with diabetes.

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Year:  2003        PMID: 12828820     DOI: 10.1089/152091503765691866

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


  6 in total

1.  Real-time glucose estimation algorithm for continuous glucose monitoring using autoregressive models.

Authors:  Yenny Leal; Winston Garcia-Gabin; Jorge Bondia; Eduardo Esteve; Wifredo Ricart; Jose-Manuel Fernández-Real; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2010-03-01

2.  Continuous glucose monitoring in non-insulin-using individuals with type 2 diabetes: acceptability, feasibility, and teaching opportunities.

Authors:  Nancy A Allen; James A Fain; Barry Braun; Stuart R Chipkin
Journal:  Diabetes Technol Ther       Date:  2009-03       Impact factor: 6.118

3.  Lack of accuracy of continuous glucose sensors in healthy, nondiabetic children: results of the Diabetes Research in Children Network (DirecNet) accuracy study.

Authors:  Nelly Mauras; Roy W Beck; Katrina J Ruedy; Craig Kollman; William V Tamborlane; H Peter Chase; Bruce A Buckingham; Eva Tsalikian; Stuart Weinzimer; Andrea D Booth; Dongyuan Xing
Journal:  J Pediatr       Date:  2004-06       Impact factor: 4.406

4.  Impact of glucose measurement processing delays on clinical accuracy and relevance.

Authors:  Sujit R Jangam; Gary Hayter; Timothy C Dunn
Journal:  J Diabetes Sci Technol       Date:  2013-05-01

Review 5.  Continuous glucose monitoring in type 1 diabetes.

Authors:  Stuart A Weinzimer; William V Tamborlane; H Peter Chase; Satish K Garg
Journal:  Curr Diab Rep       Date:  2004-04       Impact factor: 4.810

6.  Accuracy of the GlucoWatch G2 Biographer and the continuous glucose monitoring system during hypoglycemia: experience of the Diabetes Research in Children Network.

Authors: 
Journal:  Diabetes Care       Date:  2004-03       Impact factor: 19.112

  6 in total

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