Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter March 8, 2014

Sigma metrics used to assess analytical quality of clinical chemistry assays: importance of the allowable total error (TEa) target

  • Koen Hens , Mario Berth , Dave Armbruster EMAIL logo and Sten Westgard

Abstract

Background: Six Sigma metrics were used to assess the analytical quality of automated clinical chemistry and immunoassay tests in a large Belgian clinical laboratory and to explore the importance of the source used for estimation of the allowable total error. Clinical laboratories are continually challenged to maintain analytical quality. However, it is difficult to measure assay quality objectively and quantitatively.

Methods: The Sigma metric is a single number that estimates quality based on the traditional parameters used in the clinical laboratory: allowable total error (TEa), precision and bias. In this study, Sigma metrics were calculated for 41 clinical chemistry assays for serum and urine on five ARCHITECT c16000 chemistry analyzers. Controls at two analyte concentrations were tested and Sigma metrics were calculated using three different TEa targets (Ricos biological variability, CLIA, and RiliBÄK).

Results: Sigma metrics varied with analyte concentration, the TEa target, and between/among analyzers. Sigma values identified those assays that are analytically robust and require minimal quality control rules and those that exhibit more variability and require more complex rules. The analyzer to analyzer variability was assessed on the basis of Sigma metrics.

Conclusions: Six Sigma is a more efficient way to control quality, but the lack of TEa targets for many analytes and the sometimes inconsistent TEa targets from different sources are important variables for the interpretation and the application of Sigma metrics in a routine clinical laboratory. Sigma metrics are a valuable means of comparing the analytical quality of two or more analyzers to ensure the comparability of patient test results.


Corresponding author: Dave Armbruster, Abbott Diagnostics Division, Department 09AA, Building CP1-1S, Abbott Park, IL 60064, USA, E-mail:

Acknowledgments

The technical support by Ilse Van Gysel and Patrick Rosiers was much appreciated. We also thank Frank Heyvaert for his professional cooperation.

Conflict of interest statement

Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article. Employment and fees for lecturing played no role in thestudy design; in the collection, analysis, and interpretationof data; in the writing of the report; or in the decision tosubmit the report for publication.

Research funding: None declared.

Employment or leadership: Mario Berth: has received fees from Abbott for lecturing. Dave Armbruster: is employed by Abbott Diagnostics; receives salary from and holds stock from Abbott. Sten Westgard: has received fees from Abbott for lecturing and preparing educational materials.

Honorarium: None declared.

References

1. Lasky FD, Boser RB. Designing in quality through design control: a manufacturer′s perspective. Clin Chem 1997;43:866–72.10.1093/clinchem/43.5.866Search in Google Scholar

2. Stankovic AK, Romeo P. The role of in vitro diagnostics companies in reducing laboratory error. Clin Chem Lab Med 2007;45:781–8.Search in Google Scholar

3. Gras JM, Philippe M. Application of the Six Sigma concept in clinical laboratories: a review. Clin Chem Lab Med 2007;45: 789–96.Search in Google Scholar

4. Llopis MA, Trujillo G, Llovet MI, Tarrés E, Ibarz M, Biosca C, et al. Quality indicators and specifications for key analytical- extranalytical processes in the clinical laboratory. Five years’ experience using the Six Sigma concept. Clin Chem Lab Med 2011;49:463–70.10.1515/CCLM.2011.067Search in Google Scholar PubMed

5. Kinns H, Pitkin S, Housley D, Freedman DB. Internal quality control: best practices. J Clin Pathol 2013;66:1027–32.10.1136/jclinpath-2013-201661Search in Google Scholar PubMed

6. CLSI EP5-A2. Evaluation of precision performance of quantitative measurement methods; approved guideline – 2nd ed. Wayne, PA: CLSI, 2004.Search in Google Scholar

7. Fraser CG, Petersen PH, Ricos C, Haeckel R. Proposed quality specifications for the imprecision and inaccuracy of analytical systems for clinical chemistry. Eur J Clin Chem Clin Biochem 1992;30:311–7.Search in Google Scholar

8. Ricós C, Alvarez V, Cava F, García-Lario JV, Hernández A, Jiménez CV, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491–500.10.1080/00365519950185229Search in Google Scholar PubMed

9. Westgard JO. Internal quality control: planning and implementation strategies. Ann Clin Biochem 2003;40:593–611.10.1258/000456303770367199Search in Google Scholar PubMed

10. Westgard JO, Darcy T. The truth about quality: medical usefulness and analytical reliability of laboratory tests. Clin Chim Acta 2004;346:3–11.10.1016/j.cccn.2003.12.034Search in Google Scholar PubMed

11. Gras JM, Goffinet P, Bormans F. Practical internal QC protocol based on Six Sigma and biological variation in a routine clinical chemistry laboratory. Clin Chem 2009;55(Suppl):A32.Search in Google Scholar

12. Friedecky B, Kratochiva J, Budina M. Why do different EQA schemes have apparently different limits of acceptability? Clin Chem Lab Med 2011;49:743–5.10.1515/CCLM.2011.105Search in Google Scholar PubMed

13. Armbruster DA. Accuracy controls: assessing trueness (bias). In: Westgard JO, Westgard S, editors. Clinics in laboratory medicine, quality control in the age of risk management, 1st ed. Vol 33. Philadelphia, PA: Elsevier, 2013:125–37.Search in Google Scholar

Received: 2013-12-17
Accepted: 2014-2-17
Published Online: 2014-3-8
Published in Print: 2014-7-1

©2014 by Walter de Gruyter Berlin/Boston

Downloaded on 5.3.2024 from https://www.degruyter.com/document/doi/10.1515/cclm-2013-1090/html
Scroll to top button