Literature DB >> 25316713

The chronic injury glucose error grid: a tool to reduce diabetes complications.

Jan S Krouwer1, George S Cembrowski2.   

Abstract

Traditional glucose error grids provide error limits for glucose meters. These criteria help to assess the meter's suitability to prevent acute injury. We present a rationale for an error grid that provides a different set of error limits to help prevent chronic injury in diabetes. For example, glucose values in the no treatment zone of a traditional error grid could be harmful in diabetic retinopathy. The same method comparison data informs both the acute and chronic injury error grids. All of the data are used in an acute injury error grid, whereas only long-term biases populate a chronic injury error grid. These biases can be due to reagent lots and patient specific interferences. An example of a chronic injury glucose error grid is provided using simulated data.
© 2014 Diabetes Technology Society.

Entities:  

Keywords:  diabetes complications; error grid; long-term bias; surveillance

Mesh:

Substances:

Year:  2014        PMID: 25316713      PMCID: PMC4495547          DOI: 10.1177/1932296814554415

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


  8 in total

1.  Setting performance goals and evaluating total analytical error for diagnostic assays.

Authors:  Jan S Krouwer
Journal:  Clin Chem       Date:  2002-06       Impact factor: 8.327

2.  Towards more complete specifications for acceptable analytical performance - a plea for error grid analysis.

Authors:  Jan S Krouwer; George S Cembrowski
Journal:  Clin Chem Lab Med       Date:  2011-05-27       Impact factor: 3.694

3.  Performance variability of seven commonly used self-monitoring of blood glucose systems: clinical considerations for patients and providers.

Authors:  Ronald L Brazg; Leslie J Klaff; Christopher G Parkin
Journal:  J Diabetes Sci Technol       Date:  2013-01-01

4.  The surveillance error grid.

Authors:  David C Klonoff; Courtney Lias; Robert Vigersky; William Clarke; Joan Lee Parkes; David B Sacks; M Sue Kirkman; Boris Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2014-06-13

5.  Analytic bias specifications based on the analysis of effects on performance of medical guidelines.

Authors:  G G Klee; P G Schryver; R M Kisabeth
Journal:  Scand J Clin Lab Invest       Date:  1999-11       Impact factor: 1.713

6.  The Danger of Using Total Error Models to Compare Glucose Meter Performance.

Authors:  Jan S Krouwer
Journal:  J Diabetes Sci Technol       Date:  2014-02-05

7.  Association of A1C and fasting plasma glucose levels with diabetic retinopathy prevalence in the U.S. population: Implications for diabetes diagnostic thresholds.

Authors:  Yiling J Cheng; Edward W Gregg; Linda S Geiss; Giuseppina Imperatore; Desmond E Williams; Xinzhi Zhang; Ann L Albright; Catherine C Cowie; Ronald Klein; Jinan B Saaddine
Journal:  Diabetes Care       Date:  2009-11       Impact factor: 17.152

8.  Translating the A1C assay into estimated average glucose values.

Authors:  David M Nathan; Judith Kuenen; Rikke Borg; Hui Zheng; David Schoenfeld; Robert J Heine
Journal:  Diabetes Care       Date:  2008-06-07       Impact factor: 19.112

  8 in total
  3 in total

1.  Why the Details of Glucose Meter Evaluations Matters.

Authors:  Jan S Krouwer
Journal:  J Diabetes Sci Technol       Date:  2018-10-17

2.  Analysis of "Seven Year Surveillance of the Clinical Performance of a Blood Glucose Test-Strip Product".

Authors:  Jan S Krouwer
Journal:  J Diabetes Sci Technol       Date:  2017-09-14

3.  Getting More Information From Glucose Meter Evaluations.

Authors:  Jan S Krouwer; Patricia E Garrett
Journal:  J Diabetes Sci Technol       Date:  2019-05-07
  3 in total

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