Literature DB >> 27527571

Practical application of biological variation and Sigma metrics quality models to evaluate 20 chemistry analytes on the Beckman Coulter AU680.

Mai Thi Chi Tran1, KienTrung Hoang2, Ronda F Greaves3.   

Abstract

OBJECTIVES: This study aimed to evaluate the imprecision and bias data generated for 20 routine chemistry analytes against both the biological variation fitness for purpose (FFP) and Sigma metrics (SM) criteria. DESIGN AND
METHOD: Twenty serum/plasma analytes were evaluated on the Beckman Coulter AU680. Third party commercial lyophilized internal quality control samples of human origin were used for day-to-day imprecision calculations. Commercial external quality assurance (EQA) samples were used to determine the systematic error between the test method result and the instrument group mean result from the EQA program for each analyte. Biological variation data was used to calculate the minimum, desirable and optimal imprecision and bias for determination of FFP. The desirable total allowable error was determined from biological variation data and applied to the SM calculation. The outcomes of both quality approaches were then compared.
RESULTS: The day-to-day imprecision of most tested analytes (except sodium and chloride) were smaller than the allowable imprecision (ranging from minimum to optimum). Most analytes achieved at least minimum bias. The SM varied with analyte concentration with six analytes producing low Sigma values. Comparing the quality processes eleven analytes produced a green light for both FFP and SM. There was some difference seen in interpretation for the other nine analytes.
CONCLUSIONS: The individual interpretation of bias and imprecision using FFP criteria allowed for the clear determination of the major source of error. Whereas, SM provided a summative evaluation of method performance. But the selection of total allowable error (TEa) is fundamental to this interpretation and harmonisation of the TEa calculation is needed.
Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Analytical method evaluation; Bias; Biological variation; Fitness for purpose; Imprecision; Sigma metrics

Mesh:

Year:  2016        PMID: 27527571     DOI: 10.1016/j.clinbiochem.2016.08.008

Source DB:  PubMed          Journal:  Clin Biochem        ISSN: 0009-9120            Impact factor:   3.281


  4 in total

1.  Quality specifications of routine clinical chemistry methods based on sigma metrics in performance evaluation.

Authors:  Jun Xia; Su-Feng Chen; Fei Xu; Yong-Lie Zhou
Journal:  J Clin Lab Anal       Date:  2017-06-23       Impact factor: 2.352

2.  The application of sigma metrics in the laboratory to assess quality control processes in South Africa.

Authors:  Marli van Heerden; Jaya A George; Siyabonga Khoza
Journal:  Afr J Lab Med       Date:  2022-06-22

3.  Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches: Electronic supplementary material available online for this article.

Authors:  Xiuzhi Guo; Tianjiao Zhang; Xuehui Gao; Pengchang Li; Tingting You; Qiong Wu; Jie Wu; Fang Zhao; Liangyu Xia; Ermu Xu; Ling Qiu; Xinqi Cheng
Journal:  Biochem Med (Zagreb)       Date:  2018-06-15       Impact factor: 2.313

4.  Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing-based approach and an internal quality control data inter-laboratory comparison-based approach.

Authors:  Runqing Li; Tengjiao Wang; Lijun Gong; Peng Peng; Song Yang; Haibin Zhao; Pan Xiong
Journal:  J Clin Lab Anal       Date:  2019-08-06       Impact factor: 2.352

  4 in total

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