Poor agreement of computerized calculators for mean amplitude of glycemic excursions.

Abstract Background: Glucose variability has been identified as a predictor of hypoglycemia and has been associated with mortality in critically ill patients without diabetes. A popular metric to quantify glucose variability is the mean amplitude of glycemic excursions (MAGE). The 'ruler and pencil'...

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Publicado en:Diabetes Technology & Therapeutics Vol. 16; no. 2; pp. 72 - 76
Autores principales: Sechterberger, Marjolein K, Luijf, Yoeri M, Devries, J Hans
Formato: research Journal Article
Publicado: Mary Ann Liebert, Inc. Feb2014
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2014
      vid: 16
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      pub: Mary Ann Liebert, Inc.
      place: New Rochelle, New York
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        10.1089/dia.2013.0138
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        atl: Poor agreement of computerized calculators for mean amplitude of glycemic excursions.
      aug:
        au:
          Sechterberger, Marjolein K
          Luijf, Yoeri M
          Devries, J Hans
        affil: Department of Internal Medicine, Academic Medical Centre , Amsterdam, The Netherlands .
      sug:
        subj:
          Blood Glucose Metabolism
          Blood Glucose Self-Monitoring Methods
          Glycated Hemoglobin Metabolism
          Monitoring, Physiologic Methods
          Software
          Analysis of Variance
          Data Analysis, Statistical
          Female
          Glycemic Index
          Human
          Hypoglycemia Prevention and Control
          Male
          Observer Bias
          Reference Values
          Reproducibility of Results
          Female
          Male
      ab: Abstract Background: Glucose variability has been identified as a predictor of hypoglycemia and has been associated with mortality in critically ill patients without diabetes. A popular metric to quantify glucose variability is the mean amplitude of glycemic excursions (MAGE). The 'ruler and pencil' approach to calculate MAGE is operator-dependent and time-consuming for analysis of continuous glucose monitoring data. Therefore, several computer software programs have been developed for the automated calculation of MAGE. The aim of our study was to evaluate the agreement of currently available MAGE calculators when applied to the same set of continuous glucose monitoring (CGM) traces. Materials and Methods: Four software programs for calculation of MAGE were identified and used to calculate MAGE of 21 CGM traces from seven patients with type 1 diabetes. Subsequently, the median MAGE per calculator was calculated. The correlation between the MAGE calculators was evaluated by Spearman's correlation analysis. Between-group comparison was performed using analysis of variance. Results: The median MAGE (interquartile range) per calculator was 8.7 (7.1-10.7), 6.7 (5.5-8.6), 6.7 (5.2-8.6), and 5.8 (4.3-7.1), which was statistically different overall (P<0.001). The correlation coefficients between the calculators ranged from 0.787 to 0.999. Conclusions: Available computer programs developed to calculate MAGE show varying agreement. Although software programs for the calculation of MAGE would seem attractive to assess glucose variability, their use has limitations by different outcomes, in the absence of a gold standard.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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