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Sigma Metrics misconceptions and limitations

  • Xincen Duan ORCID logo , Elvar Theodorsson , Wei Guo EMAIL logo and Tony Badrick ORCID logo EMAIL logo
Published/Copyright: December 24, 2024
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 6

Abstract

Objectives

This paper further explores the Sigma Metric (SM) and its application in clinical chemistry. It discusses the SM, assay stability, and control failure relationship.

Content

: SM is not a valid measure of assay stability or the likelihood of failure. When an out-of-control event occurs for an assay with a higher SM value, the same QC rule will have greater power to detect error than assays with a lower SM value. Thus, it is easier to prevent errors from happening for higher SM assays. This rationale encourages using more frequent QC events and more QC samples for a QC scheme of a low SM assay or simply more QC cost for low SM assays. A laboratory can have a high-precision instrument that frequently fails and a low-precision instrument that hardly ever fails. Parvin’s patient risk model presumes the bracketed continuous mode (BCM) testing workflow. If overlooked when designing QC schemes, this leads to the common misconception of the SM that one can save the cost of QC since assays with high SM require less frequent QC to ensure patient risk. There is no evidence that an assay’s precision is correlated with its failure rate. Schmidt et al., in a series of papers, showed that an assay with a higher Pf or shift in probability will have a higher expected number of unacceptable results. Incorporating Pf into the QC design process presents significant challenges despite the proactive quality control (PQC) methodology.

Summary

Unfortunately, TEa Six Sigma, as widely practiced in Clinical Chemistry, is not based on classical Six Sigma mathematical statistics. Classical Six Sigma would facilitate comparing results across activities where the principles of Six Sigma are employed.

Keywords: quality control; Sigma Metric; analytical error
Corresponding authors: Wei Guo, Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, China, E-mail: ; and Tony Badrick, Royal College of Pathologists of Australasia Quality Assurance Program, 8 Herbert Street, 2065, St Leonards, NSW, Australia, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Badrick, T, Theodorsson, E. Six Sigma – is it time to re-evaluate its value in laboratory medicine? Clin Chem Lab Med 2024;62:2398–400. https://doi.org/10.1515/cclm-2024-0377.Search in Google Scholar PubMed

2. Bayat, H, Westgard, SA, Westgard, JO. The value of sigma-metrics in laboratory medicine. Clin Chem Lab Med 2024;62:2401–4. https://doi.org/10.1515/cclm-2024-0609.Search in Google Scholar PubMed

3. Mackay, M, Hegedus, G, Badrick, T. Assay stability, the missing component of the error budget. Clin Biochem 2017;50:1136–44. https://doi.org/10.1016/j.clinbiochem.2017.07.004.Search in Google Scholar PubMed

4. Bayat, H, Westgard, SA, Westgard, JO. Planning risk-based SQC strategies: practical tools to support the new CLSI C24Ed4 guidance. App Lab Med 2017;2:211–21. https://doi.org/10.1373/jalm.2017.023192.Search in Google Scholar PubMed

5. Bayat, H, Westgard, SA, Westgard, JO. The value of sigma-metrics in laboratory medicine. Clin Chem Lab Med 2024;62:2401–4. https://doi.org/10.1515/cclm-2024-0609.Search in Google Scholar

6. Schmidt, RL, Moore, RA, Walker, BS, Rudolf, JW. Risk analysis for quality control part 3: practical application of the precision quality control model. App Lab Med 2023;8:34–40. https://doi.org/10.1093/jalm/jfac116.Search in Google Scholar PubMed

7. Moore, RA, Rudolf, JW, Schmidt, RL. Risk analysis for quality control part 2: theoretical foundations for risk analysis. App Lab Med 2023;8:23–33. https://doi.org/10.1093/jalm/jfac106.Search in Google Scholar PubMed

8. Schmidt, RL, Moore, RA, Walker, BS, Rudolf, JW. Risk analysis for quality control part 1: the impact of transition assumptions in the Parvin model. App Lab Med 2023;8:14–22. https://doi.org/10.1093/jalm/jfac117.Search in Google Scholar PubMed

9. Farnsworth, CW, Lyon, OAS. QC a risky business: the development of novel risk-based tools for assessing QC methods. App Lab Med 2023;8:3–6. https://doi.org/10.1093/jalm/jfac123.Search in Google Scholar PubMed

10. Schmidt, RL, Moore, RA, Walker, BS, Rudolf, JW. Precision quality control: a dynamic model for risk-based analysis of analytical quality. Clin Chem Lab Med 2023;61:679–87. https://doi.org/10.1515/cclm-2022-1094.Search in Google Scholar PubMed

11. Badrick, T, Loh, TP. Developing an evidence-based approach to quality control. Clin Biochem 2023;114:39–42. https://doi.org/10.1016/j.clinbiochem.2023.01.011.Search in Google Scholar PubMed

12. Hens, K, Berth, M, Armbruster, D, Westgard, S. Sigma metrics used to assess analytical quality of clinical chemistry assays: importance of the allowable total error (TEa) target. Clin Chem Lab Med 2014;52:973–80. https://doi.org/10.1515/cclm-2013-1090.Search in Google Scholar PubMed

13. Westgard, S, Petrides, V, Schneider, S, Berman, M, Herzogenrath, J, Orzechowski, A. Assessing precision, bias and sigma-metrics of 53 measurands of the Alinity ci system. Clin Biochem 2017;50:1216–21. https://doi.org/10.1016/j.clinbiochem.2017.09.005.Search in Google Scholar PubMed

14. Oosterhuis, WP, Coskun, A. Sigma metrics in laboratory medicine revisited: we are on the right road with the wrong map. Biochem Med 2018;28:186–94. https://doi.org/10.11613/bm.2018.020503.Search in Google Scholar PubMed PubMed Central

15. Low, HQ, Farrell, CJL, Loh, TP, Lim, CY. Sigma metric is more correlated with analytical imprecision than bias. Clin Chem Lab Med 2024;63:e39–43. https://doi.org/10.1515/cclm-2024-0882.Search in Google Scholar PubMed

16. Theodorsson, E. Issues in assessing analytical performance specifications in healthcare systems assembling multiple laboratories and measuring systems. Clin Chem Lab Med 2024;62:1520–30. https://doi.org/10.1515/cclm-2023-1208.Search in Google Scholar PubMed

17. Nevalainen, D, Berte, L, Kraft, C, Leigh, E, Picaso, L, Morgan, T. Evaluating laboratory performance on quality indicators with the six sigma scale. Arch Pathol Lab Med 2000;124:516–19. https://doi.org/10.5858/2000-124-0516-elpoqi.Search in Google Scholar

18. Westgard, JO, Westgard, SA. Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys. Clin Chem Lab Med 2015;53:1531–5. https://doi.org/10.1515/cclm-2014-1241.Search in Google Scholar PubMed

19. Westgard, S, Bayat, H, Westgard, JO. Analytical sigma metrics: a review of six sigma implementation tools for medical laboratories. Biochem Med 2018;28:020502. https://doi.org/10.11613/bm.2018.020502.Search in Google Scholar PubMed PubMed Central

20. De, MJ. Six sigma and competitive advantage. Total Qual Manag Bus Excel 2006;17:455–64.10.1080/14783360500528221Search in Google Scholar

21. Coskun, A, Ialongo, C. Six sigma revisited: we need evidence to include a 1.5 SD shift in the extra analytical phase of the total testing process. Biochem Med 2020;30:1–4. https://doi.org/10.11613/bm.2020.010901.Search in Google Scholar

22. Coskun, A, Oosterhuis, WP. Six sigma in laboratory medicine: the unfinished symphony. Clin Chem Lab Med 2024. Available from: https://www.degruyter.com/document/doi/10.1515/cclm-2024-1144/html.10.1515/cclm-2024-1144Search in Google Scholar PubMed

23. Coskun, A. Wrong sigma metric causes chaos. LaboratoriumMedizin 2022;46:143–5. https://doi.org/10.1515/labmed-2022-0003.Search in Google Scholar
Received: 2024-11-24
Accepted: 2024-12-15
Published Online: 2024-12-24
Published in Print: 2025-05-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2024-1380
Keywords for this article
quality control; Sigma Metric; analytical error

Articles in the same Issue

  1. Frontmatter
  2. Editorials
  3. The Friedewald formula strikes back
  4. Liquid biopsy in oncology: navigating technical hurdles and future transition for precision medicine
  5. The neglected issue of pyridoxal- 5′ phosphate
  6. Reviews
  7. Health literacy: a new challenge for laboratory medicine
  8. Clinical applications of circulating tumor cell detection: challenges and strategies
  9. Opinion Papers
  10. Pleural effusion as a sample matrix for laboratory analyses in cancer management: a perspective
  11. Interest of hair tests to discriminate a tail end of a doping regimen from a possible unpredictable source of a prohibited substance in case of challenging an anti-doping rule violation
  12. Perspectives
  13. Sigma Metrics misconceptions and limitations
  14. EN ISO 15189 revision: EFLM Committee Accreditation and ISO/CEN standards (C: A/ISO) analysis and general remarks on the changes
  15. General Clinical Chemistry and Laboratory Medicine
  16. Evaluation of current indirect methods for measuring LDL-cholesterol
  17. Verification of automated review, release and reporting of results with assessment of the risk of harm for patients: the procedure algorithm proposal for clinical laboratories
  18. Progranulin measurement with a new automated method: a step forward in the diagnostic approach to neurodegenerative disorders
  19. A comparative analysis of current С-peptide assays compared to a reference method: can we overcome inertia to standardization?
  20. Blood samples for ammonia analysis do not require transport to the laboratory on ice: a study of ammonia stability and cause of in vitro ammonia increase in samples from patients with hyperammonaemia
  21. A physio-chemical mathematical model of the effects of blood analysis delay on acid-base, metabolite and electrolyte status: evaluation in blood from critical care patients
  22. Evolution of autoimmune diagnostics over the past 10 years: lessons learned from the UK NEQAS external quality assessment EQA programs
  23. Comparison between monotest and traditional batch-based ELISA assays for therapeutic drug monitoring of infliximab and adalimumab levels and anti-drug antibodies
  24. Evaluation of pre-analytical factors impacting urine test strip and chemistry results
  25. Evaluation of AUTION EYE AI-4510 flow cell morphology analyzer for counting particles in urine
  26. Reference Values and Biological Variations
  27. Estimation of the allowable total error of the absolute CD34+ cell count by flow cytometry using data from UK NEQAS exercises 2004–2024
  28. Establishment of gender– and age–related reference intervals for serum uric acid in adults based on big data from Zhejiang Province in China
  29. Cancer Diagnostics
  30. Tumor specific protein 70 targeted tumor cell isolation technology can improve the accuracy of cytopathological examination
  31. Cardiovascular Diseases
  32. Diagnostic performance of Mindray CL1200i high sensitivity cardiac troponin I assay compared to Abbott Alinity cardiac troponin I assay for the diagnosis of type 1 and 2 acute myocardial infarction in females and males: MERITnI study
  33. Infectious Diseases
  34. Evidence-based assessment of the application of Six Sigma to infectious disease serology quality control
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  36. Evaluating the accuracy of ChatGPT in classifying normal and abnormal blood cell morphology
  37. Refining within-subject biological variation estimation using routine laboratory data: practical applications of the refineR algorithm
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  39. Biochemical evidence of vitamin B12 deficiency: a crucial issue to address supplementation in pregnant women
  40. Plasmacytoid dendritic cell proliferation and acute myeloid leukemia with minimal differentiation (AML-M0)
  41. Failing methemoglobin blood gas analyses in a sodium nitrite intoxication
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