Literature DB >> 25737535

Investigation of 2 models to set and evaluate quality targets for hb a1c: biological variation and sigma-metrics.

Cas Weykamp1, Garry John2, Philippe Gillery3, Emma English4, Linong Ji5, Erna Lenters-Westra6, Randie R Little7, Gojka Roglic8, David B Sacks9, Izumi Takei10.   

Abstract

BACKGROUND: A major objective of the IFCC Task Force on Implementation of HbA1c Standardization is to develop a model to define quality targets for glycated hemoglobin (Hb A1c).
METHODS: Two generic models, biological variation and sigma-metrics, are investigated. We selected variables in the models for Hb A1c and used data of external quality assurance/proficiency testing programs to evaluate the suitability of the models to set and evaluate quality targets within and between laboratories.
RESULTS: In the biological variation model, 48% of individual laboratories and none of the 26 instrument groups met the minimum performance criterion. In the sigma-metrics model, with a total allowable error (TAE) set at 5 mmol/mol (0.46% NGSP), 77% of the individual laboratories and 12 of 26 instrument groups met the 2σ criterion.
CONCLUSIONS: The biological variation and sigma-metrics models were demonstrated to be suitable for setting and evaluating quality targets within and between laboratories. The sigma-metrics model is more flexible, as both the TAE and the risk of failure can be adjusted to the situation-for example, requirements related to diagnosis/monitoring or international authorities. With the aim of reaching (inter)national consensus on advice regarding quality targets for Hb A1c, the Task Force suggests the sigma-metrics model as the model of choice, with default values of 5 mmol/mol (0.46%) for TAE and risk levels of 2σ and 4σ for routine laboratories and laboratories performing clinical trials, respectively. These goals should serve as a starting point for discussion with international stakeholders in the field of diabetes.
© 2015 American Association for Clinical Chemistry.

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Year:  2015        PMID: 25737535      PMCID: PMC4946649          DOI: 10.1373/clinchem.2014.235333

Source DB:  PubMed          Journal:  Clin Chem        ISSN: 0009-9147            Impact factor:   8.327


  14 in total

1.  Within-subject biological variation of glucose and HbA(1c) in healthy persons and in type 1 diabetes patients.

Authors:  Siri Carlsen; Per Hyltoft Petersen; Svein Skeie; Øyvind Skadberg; Sverre Sandberg
Journal:  Clin Chem Lab Med       Date:  2011-06-02       Impact factor: 3.694

2.  Standards of medical care in diabetes--2011.

Authors: 
Journal:  Diabetes Care       Date:  2011-01       Impact factor: 19.112

3.  Influence of analytical bias and imprecision on the number of false positive results using Guideline-Driven Medical Decision Limits.

Authors:  Per Hyltoft Petersen; George G Klee
Journal:  Clin Chim Acta       Date:  2013-12-18       Impact factor: 3.786

4.  The analytical goals for hemoglobin A(1c) measurement in IFCC units and National Glycohemoglobin Standardization Program Units are different.

Authors:  Cas W Weykamp; Andrea Mosca; Philippe Gillery; Mauro Panteghini
Journal:  Clin Chem       Date:  2011-05-13       Impact factor: 8.327

5.  Biological variation – reliable data is essential.

Authors:  Aasne K Aarsand; Thomas Røraas; Sverre Sandberg
Journal:  Clin Chem Lab Med       Date:  2015-02       Impact factor: 3.694

6.  Defining acceptable limits for the metrological traceability of specific measurands.

Authors:  Renze Bais; Dave Armbruster; Rob T P Jansen; George Klee; Mauro Panteghini; Joseph Passarelli; Ken A Sikaris
Journal:  Clin Chem Lab Med       Date:  2013-05       Impact factor: 3.694

7.  Changing from glucose to HbA1c for diabetes diagnosis: predictive values of one test and importance of analytical bias and imprecision.

Authors:  Aneta Aleksandra Nielsen; Per Hyltoft Petersen; Anders Green; Cramer Christensen; Henry Christensen; Ivan Brandslund
Journal:  Clin Chem Lab Med       Date:  2014-07       Impact factor: 3.694

8.  Confidence intervals and power calculations for within-person biological variation: effect of analytical imprecision, number of replicates, number of samples, and number of individuals.

Authors:  Thomas Røraas; Per H Petersen; Sverre Sandberg
Journal:  Clin Chem       Date:  2012-07-03       Impact factor: 8.327

9.  Revaluation of biological variation of glycated hemoglobin (HbA(1c)) using an accurately designed protocol and an assay traceable to the IFCC reference system.

Authors:  Federica Braga; Alberto Dolci; Martina Montagnana; Franca Pagani; Renata Paleari; Gian Cesare Guidi; Andrea Mosca; Mauro Panteghini
Journal:  Clin Chim Acta       Date:  2011-04-17       Impact factor: 3.786

10.  Consensus statement on the worldwide standardization of the hemoglobin A1C measurement: the American Diabetes Association, European Association for the Study of Diabetes, International Federation of Clinical Chemistry and Laboratory Medicine, and the International Diabetes Federation.

Authors: 
Journal:  Diabetes Care       Date:  2007-09       Impact factor: 19.112

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  17 in total

1.  Evaluation of Four HbA1c Point-of-Care Devices Using International Quality Targets: Are They Fit for the Purpose?

Authors:  Erna Lenters-Westra; Emma English
Journal:  J Diabetes Sci Technol       Date:  2018-06-19

Review 2.  Evaluation of Performance of Laboratories and Manufacturers Within the Framework of the IFCC model for Quality Targets of HbA1c.

Authors:  Cas Weykamp; Carla Siebelder
Journal:  J Diabetes Sci Technol       Date:  2017-11-17

3.  Accuracy of HbA1c as Monitored by External Quality Assessment and Compared With Patient Mean Values.

Authors:  Gunnar Nordin
Journal:  J Diabetes Sci Technol       Date:  2018-06-19

4.  Assessing quality from an accuracy-based HbA1c proficiency survey.

Authors:  Randie R Little; Curt L Rohlfing
Journal:  Clin Chem Lab Med       Date:  2016-03       Impact factor: 3.694

Review 5.  Methods, units and quality requirements for the analysis of haemoglobin A1c in diabetes mellitus.

Authors:  Ilkka Penttilä; Karri Penttilä; Päivi Holm; Harri Laitinen; Päivi Ranta; Jukka Törrönen; Rainer Rauramaa
Journal:  World J Methodol       Date:  2016-06-26

6.  Glycated Hemoglobin Measurement: Comparison of Three Methods Versus High Performance Liquid Chromatography.

Authors:  María Zulema Chaila; Matías Viniegra; Juan José Gagliardino; Alfredo Martínez; María Gabriela Simesen de Bielke; Mauro Frusti; Luis Monaco; Pablo Salgado; Carlos Buso; Claudio Daniel Gonzalez; Víctor Francisco Commendatore
Journal:  J Diabetes Sci Technol       Date:  2021-03-09

7.  Discordance in glycemic categories and regression to normality at baseline in 10,000 people in a Type 2 diabetes prevention trial.

Authors:  Mike Sampson; Tim Elwell-Sutton; Max O Bachmann; Allan Clark; Ketan K Dhatariya; Clare Ferns; Amanda Howe; W Garry John; Gerry Rayman; Leyla Swafe; Jeremy Turner; Melanie Pascale
Journal:  Sci Rep       Date:  2018-04-19       Impact factor: 4.379

8.  Comparison between Sigma metrics in four accredited Egyptian medical laboratories in some biochemical tests: an initiative towards sigma calculation harmonization.

Authors:  Rania El Sharkawy; Sten Westgard; Ahmed M Awad; AbdelKarem Omneya I Ahmed; El Hadidi Iman; Ahmed Gaballah; Eman Shaheen
Journal:  Biochem Med (Zagreb)       Date:  2018-06-15       Impact factor: 2.313

9.  Application of sigma metrics for the assessment of quality assurance using the MQ-2000 PT HbA1c analyzer.

Authors:  Kağan Huysal; Yasemin U Budak
Journal:  Biochem Med (Zagreb)       Date:  2015-10-15       Impact factor: 2.313

10.  Analytical verification and quality assessment of the Tosoh HLC-723GX HbA1c analyzer.

Authors:  Marko Ris; Sandra Božičević; Vanja Radišić Biljak; Marijana Vučić Lovrenčić
Journal:  Pract Lab Med       Date:  2016-12-09
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