Abstract
Objectives
Westgard Sigma Rules is a statistical tool available for quality control. Biological variation (BV) can be used to set analytical performance specifications (APS). The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) regularly updates BV data. However, few studies have used robust BV data to determine quality goals and design a quality control strategy for tumor markers. The aim of this study was to derive APS for tumor markers from EFLM BV data and apply Westgard Sigma Rules to establish internal quality control (IQC) rules.
Methods
Precision was calculated from IQC data, and bias was obtained from the relative deviation of the External quality assurance scheme (EQAS) group mean values and laboratory-measured values. Total allowable error (TEa) was derived using EFLM BV data. After calculating sigma metrics, the IQC strategy for each tumor marker was determined according to Westgard Sigma Rules.
Results
Sigma metrics achieved for each analyte varied with the level of TEa. Most of these tumor markers except neuron-specific enolase reached 3σ or better based on TEamin. With TEades and TEaopt set as the quality goals, almost all analytes had sigma values below 3. Set TEamin as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values.
Conclusions
Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.
Funding source: Health Commission of Henan Province http://dx.doi.org/10.13039/100018925
Award Identifier / Grant number: LHGJ20200202
-
Research funding: This study was supported by Health Commission of Henan Province, grant number: LHGJ20200202.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Not applicable.
-
Ethical approval: Not applicable.
References
1. Matson, PL. Internal quality control and external quality assurance in the IVF laboratory. Hum Reprod 1998;13:156–65. https://doi.org/10.1093/humrep/13.suppl_4.156.Search in Google Scholar PubMed
2. Gras, JM, Philippe, M. Application of the Six Sigma concept in clinical laboratories: a review. Clin Chem Lab Med 2007;45:789–96. https://doi.org/10.1515/CCLM.2007.135.Search in Google Scholar PubMed
3. 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
4. Westgard, JO. Useful measures and models for analytical quality management in medical laboratories. Clin Chem Lab Med 2016;54:223–33. https://doi.org/10.1515/cclm-2015-0710.Search in Google Scholar PubMed
5. Westgard, JO. A total quality-control plan with right-sized statistical quality-control. Clin Lab Med 2017;37:137–50. https://doi.org/10.1016/j.cll.2016.09.011.Search in Google Scholar PubMed
6. People’s Republic of China Health Industry Standard (WS/T641-2018), 2018. Available from: http://www.nhc.gov.cn/wjw/wsbzxx/wsbz.shtml [Accessed 10 Jun 2022].Search in Google Scholar
7. Westgard, JO, Westgard, SA. Basic quality management systems (Chapter 12). In: Designing statistical QC procedures. Madison, WI: Westgard QC; 2014:171–88.Search in Google Scholar
8. Montévil, M, Mossio, M, Pocheville, A, Longo, G. Theoretical principles for biology: Variation. Prog Biophys Mol Biol 2016;122:36–50. https://doi.org/10.1016/j.pbiomolbio.2016.08.005.Search in Google Scholar PubMed
9. Ricós, C, Álvarez, V, Minchinela, J, Fernández-Calle, P, Perich, C, Boned, B, et al.. Biologic variation approach to daily laboratory. Clin Lab Med 2017;37:47–56.10.1016/j.cll.2016.09.005Search in Google Scholar PubMed
10. Sandberg, S, Fraser, CG, Horvath, AR, Jansen, R, Jones, G, Oosterhuis, W, et al.. Defining analytical performance specifications: consensus statement from the 1st strategic conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2015;53:833–5. https://doi.org/10.1515/cclm-2015-0067.Search in Google Scholar PubMed
11. Westgard, QC. Desirable specifications for total error, imprecision and bias, derived from intra- and inter-individual biologic variation. Available from: http://www.westgard.com/biodatabase1.htm [Accessed 10 Jun 2022].Search in Google Scholar
12. Ricós, C, García-Victoria, M, de la Fuente, B. Quality indicators and specifications for the extra-analytical phases in clinical laboratory management. Clin Chem Lab Med 2004;42:578–82.10.1515/CCLM.2004.100Search in Google Scholar PubMed
13. Cattozzo, G, Albeni, C, Calonaci, A, De Luca, G. Evaluation of the analytical performance of the Beckman Coulter AU680 automated analytical system based on quality specifications for allowable performance derived from biological variation. Clin Chem Lab Med 2011;49:1563–7. https://doi.org/10.1515/CCLM.2011.629.Search in Google Scholar PubMed
14. Aarsand, AK, Røraas, T, Sandberg, S. Biological variation – reliable data is essential. Clin Chem Lab Med 2015;53:153–4. https://doi.org/10.1515/cclm-2014-1141.Search in Google Scholar PubMed
15. Aarsand, AK, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, et al.. EFLM biological variation database. Available from: https://biologicalvariation.eu/ [Accessed 2 Jul 2022].Search in Google Scholar
16. Faria, SC, Sagebiel, T, Patnana, M, Cox, V, Viswanathan, C, Lall, C, et al.. Tumor markers: myths and facts unfolded. Abdom Radiol (NY) 2019;44:1575–600. https://doi.org/10.1007/s00261-018-1845-0.Search in Google Scholar PubMed
17. International Organization for Standardization. Statistical methods for use in proficiency testing by interlaboratory comparison (ISO 13528:2015). Geneva: International Organization for Standardization (ISO); 2015.Search in Google Scholar
18. Marques-Garcia, F, Boned, B, González-Lao, E, Braga, F, Carobene, A, Coskun, A, et al.. Critical review and meta-analysis of biological variation estimates for tumor markers. Clin Chem Lab Med 2022;60:494–504. https://doi.org/10.1515/cclm-2021-0725.Search in Google Scholar PubMed
19. Stöckl, D, Baadenhuijsen, H, Fraser, CG, Libeer, JC, Petersen, PH, Ricós, C. Desirable routine analytical goals for quantities assayed in serum. Eur J Clin Chem Clin Biochem 1995;33:157–69.Search in Google Scholar
20. Gowans, EM, Hyltoft Petersen, P, Blaabjerg, O, Hørder, M. Analytical goals for the acceptance of common reference intervals for laboratories throughout a geographical area. Scand J Clin Lab Invest 1988;48:757–64. https://doi.org/10.3109/00365518809088757.Search in Google Scholar PubMed
21. Fraser, CG, Harris, EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409–37. https://doi.org/10.3109/10408368909106595.Search in Google Scholar PubMed
22. Westgard, JO, Westgard, SA. Available from: https://www.westgard.com/westgard-sigma-rules.htm [Accessed 12 Jun 2022].Search in Google Scholar
23. Carobene, A. The European Biological Variation Study (EuBIVAS): delivery of updated biological variation estimates, a project by the Working Group on Biological Variation in the European Federation of Clinical Chemistry and Laboratory Medicine. J Lab Precis Med 2017;2:70. https://doi.org/10.21037/jlpm.2017.08.13.Search in Google Scholar
24. European Federation of Clinical Chemistry and Laboratory Medicine Task and Finish Group Biological Variation Database. Available from: https://www.eflm.eu/site/page/a/1084 [Accessed 10 June 2022].Search in Google Scholar
25. Aarsand, AK, Røraas, T, Bartlett, WA, Coşkun, A, Carobene, A, Fernandez-Calle, P, et al.. Harmonization initiatives in the generation, reporting and application of biological variation data. Clin Chem Lab Med 2018;56:1629–36. https://doi.org/10.1515/cclm-2018-0058.Search in Google Scholar PubMed
26. Bartlett, WA, Braga, F, Carobene, A, Coşkun, A, Prusa, R, Fernandez-Calle, P, et al.. A checklist for critical appraisal of studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127.Search in Google Scholar PubMed
27. Aarsand, AK, Røraas, T, Fernandez-Calle, P, Ricos, C, Díaz-Garzón, J, Jonker, N, et al.. The Biological Variation Data Critical Appraisal Checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808.Search in Google Scholar PubMed
28. Carobene, A, Aarsand, AK, Bartlett, WA, Coskun, A, Diaz-Garzon, J, Fernandez-Calle, P, et al.. The European Biological Variation Study (EuBIVAS): a summary report. Clin Chem Lab Med 2021;60:505–17. https://doi.org/10.1515/cclm-2021-0370.Search in Google Scholar PubMed
29. Carobene, A, Guerra, E, Locatelli, M, Ceriotti, F, Sandberg, S, Fernandez-Calle, P, et al.. Providing correct estimates of biological variation-not an easy task. The example of S100-β protein and neuron-specific enolase. Clin Chem 2018;64:1537–9. https://doi.org/10.1373/clinchem.2018.292169.Search in Google Scholar PubMed
30. Carobene, A, Guerra, E, Locatelli, M, Cucchiara, V, Briganti, A, Aarsand, AK, et al.. Biological variation estimates for prostate specific antigen from the European Biological Variation Study; consequences for diagnosis and monitoring of prostate cancer. Clin Chim Acta 2018;486:185–91. https://doi.org/10.1016/j.cca.2018.07.043.Search in Google Scholar PubMed
31. Coşkun, A, Aarsand, AK, Sandberg, S, Guerra, E, Locatelli, M, Díaz-Garzón, J, et al.. Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study. Clin Chem Lab Med 2021;60:543–52. https://doi.org/10.1515/cclm-2021-0283.Search in Google Scholar PubMed
32. Duffy, MJ, Sturgeon, CM, Sölétormos, G, Barak, V, Molina, R, Hayes, DF, et al.. Validation of new cancer biomarkers: a position statement from the European group on tumor markers. Clin Chem 2015;61:809–20. https://doi.org/10.1373/clinchem.2015.239863.Search in Google Scholar PubMed
33. Carter, HB, Albertsen, PC, Barry, MJ, Etzioni, R, Freedland, SJ, Greene, KL, et al.. Early detection of prostate cancer: AUA guideline. J Urol 2013;190:419–26. https://doi.org/10.1016/j.juro.2013.04.119.Search in Google Scholar PubMed PubMed Central
34. Galle, PR, Foerster, F, Kudo, M, Chan, SL, Llovet, JM, Qin, S, et al.. Biology and significance of alpha-fetoprotein in hepatocellular carcinoma. Liver Int 2019;39:2214–29. https://doi.org/10.1111/liv.14223.Search in Google Scholar PubMed
35. Colloca, G, Venturino, A, Vitucci, P. Pre-treatment carcinoembryonic antigen and outcome of patients with rectal cancer receiving neo-adjuvant chemo-radiation and surgical resection: a systematic review and meta-analysis. Med Oncol 2017;34:177. https://doi.org/10.1007/s12032-017-1037-8.Search in Google Scholar PubMed
36. Badrick, T, Punyalack, W, Graham, P. Commutability and traceability in EQA programs. Clin Biochem 2018;56:102–4. https://doi.org/10.1016/j.clinbiochem.2018.04.018.Search in Google Scholar PubMed
37. Miller, WG, Jones, GR, Horowitz, GL, Weykamp, C. Proficiency testing/external quality assessment: current challenges and future directions. Clin Chem 2011;57:1670–80. https://doi.org/10.1373/clinchem.2011.168641.Search in Google Scholar PubMed
38. Bureau Internationale des Poids et Mesures. Available from: https://www.bipm.org/jctlm/home.do [Accessed 10 Jun 2022].Search in Google Scholar
39. Ferrero, CA, Carobene, A, Ceriotti, F, Modenese, A, Arcelloni, C. Behavior of frozen serum pools and lyophilized sera in an external quality-assessment scheme. Clin Chem 1995;41:575–80. https://doi.org/10.1093/clinchem/41.4.575.Search in Google Scholar
40. Fraser, CG, Petersen, PH. Quality goals in external quality assessment are best based on biology. Scand J Clin Lab Invest Suppl 1993;212:8–9. https://doi.org/10.3109/00365519309085446.Search in Google Scholar
41. Oosterhuis, WP. Gross overestimation of total allowable error based on biological variation. Clin Chem 2011;57:1334–6. https://doi.org/10.1373/clinchem.2011.165308.Search in Google Scholar PubMed
© 2022 Walter de Gruyter GmbH, Berlin/Boston