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Study of total error specifications of lymphocyte subsets enumeration using China National EQAS data and Biological Variation Data Critical Appraisal Checklist (BIVAC)-compliant publications

  • Chenbin Li ORCID logo , Yu Wang , Hong Lu , Zhongli Du , Chengshan Xu and Mingting Peng EMAIL logo
Published/Copyright: July 22, 2020

Abstract

Objectives

It is important to select proper quality specifications for laboratories and external quality assessment (EQA) providers for their quality control and assessment. The aim of this study is to produce new total error (TE) specifications for lymphocyte subset enumeration by analyzing the allowable TE using EQAS data and comparing them with that based on reliable biological variation (BV).

Methods

A total of 54,400 results from 1,716 laboratories were collected from China National EQAS for lymphocyte subset enumeration during the period 2017–2019. The EQA data were grouped according to lower limits of reference intervals for establishing concentration-dependent specifications. The TE value that 80% of laboratories can achieve were considered as TE specifications based on state of the art. The BV studies compliant with Biological Variation Data Critical Appraisal Checklist (BIVAC) were used to calculate the three levels of TE specifications. Then these TE specifications were compared for determining the recommended TE specifications.

Results

Four parameters whose quality specifications could achieve the optimum criteria were as follows: the percentages of CD3+, CD3+CD4+ (high concentration) and CD3–CD16/56+ cells, and the absolute count of CD3–CD16/56+ cells. Only the TE specifications of CD3–CD19+ cells could achieve the minimum criteria. The TE specifications of remaining parameters should reach the desirable criteria.

Conclusions

New TE specifications were established by combining the EQA data and reliable BV data, which could help laboratories to apply proper criteria for continuous improvement of quality control, and EQA providers to use robust acceptance limits for better evaluation of EQAS results.


Corresponding author: Prof. Mingting Peng, National Center for Clinical Laboratories, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P.R. China; and Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, P.R. China, E-mail:
Chenbin Li and Yu Wang: contributed equally to this work.

Award Identifier / Grant number: 2013FY113800

Award Identifier / Grant number: 2017YFC0910003

Award Identifier / Grant number: 81772254

  1. Research funding: The work was supported by the research fund of National Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (Grant No. 2013FY113800), National key research and development program (Grant No. 2017YFC0910003) and National Natural Science Foundation of China (Grant No. 81772254).

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

  3. Competing interests: Authors state no conflict of interest.

References

1. Bock, P, James, A, Nikuze, A, Peton, N, Sabapathy, K, Mills, E, et al. Baseline CD4 count and adherence to antiretroviral therapy: a systematic review and meta-analysis. J Acquir Immune Defic Syndr 2016;73:514–21. https://doi.org/10.1097/qai.0000000000001092.Search in Google Scholar

2. Wang, F, Nie, J, Wang, H, Zhao, Q, Xiong, Y, Deng, L, et al. Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia. J Infect Dis 2020;221:1762–169. https://doi.org/10.1093/infdis/jiaa150.Search in Google Scholar

3. 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

4. Bratescu, A, Teodorescu, M. Circannual variations in the B cell/T cell ratio in normal human peripheral blood. J Allergy Clin Immunol 1981;68:273–80. https://doi.org/10.1016/0091-6749(81)90151-2.Search in Google Scholar

5. Abo, T, Miller, CA, Cloud, GA, Blach, CM. Annual stability in the levels of lymphocyte subpopulations identified by monoclonal antibodies in blood of healthy individuals. J Clin Immunol 1985;5:13–20. https://doi.org/10.1007/bf00915163.Search in Google Scholar

6. Ritchie, AW, Oswald, I, Micklem, HS, Boyd, JE, Elton, RA, Jazwinska, E, et al. Circadian variation of lymphocyte subpopulations: a study with monoclonal antibodies. Br Med J (Clin Res Ed) 1983;286:1773–5. https://doi.org/10.1136/bmj.286.6380.1773.Search in Google Scholar PubMed PubMed Central

7. Backteman, K, Ledent, E, Berlin, G, Ernerudh, J. A rapid and reliable flow cytometric routine method for counting leucocytes in leucocyte-depleted platelet concentrates. Vox Sang 2002;83:29–34. https://doi.org/10.1046/j.1423-0410.2002.00196.x.Search in Google Scholar PubMed

8. Tosato, F, Bernardi, D, Sanzari, MC, Pantano, G, Plebani, M. Biological variability of lymphocyte subsets of human adults’ blood. Clin Chim Acta 2013;424:159–63. https://doi.org/10.1016/j.cca.2013.06.001.Search in Google Scholar PubMed

9. Backteman, K, Ernerudh, J. Biological and methodological variation of lymphocyte subsets in blood of human adults. J Immunol Methods 2007;322:20–7. https://doi.org/10.1016/j.jim.2007.01.021.Search in Google Scholar PubMed

10. Huang, C, Li, W, Wu, W, Chen, Q, Guo, Y, Zhang, Y, et al. Intra-day and inter-day biological variations of peripheral blood lymphocytes. Clin Chim Acta 2015;438:166–70. https://doi.org/10.1016/j.cca.2014.08.009.Search in Google Scholar PubMed

11. Aziz, N, Detels, R, Quint, JJ, Gjertson, D, Ryner, T, Butch, AW. Biological variation of immunological blood biomarkers in healthy individuals and quality goals for biomarker tests. BMC Immunol 2019;20:33. https://doi.org/10.1186/s12865-019-0313-0.Search in Google Scholar

12. Falay, M, Senes, M, Korkmaz, S, Zararsiz, G, Turhan, T, Okay, M, et al. Biological variation of peripheral blood T-lymphocytes. J Immunol Methods 2019;470:1–5. https://doi.org/10.1016/j.jim.2019.04.002.Search in Google Scholar

13. Molls, RR, Ahluwalia, N, Fick, T, Mastro, AM, Wagstaff, D, Handte, G, et al. Inter- and intra-individual variation in tests of cell-mediated immunity in young and old women. Mech Ageing Dev 2003;124:619–27. https://doi.org/10.1016/s0047-6374(03)00062-9.Search in Google Scholar

14. Molina, A, Guinon, L, Perez, A, Segurana, A, Bedini, JL, Reverter, JC, et al. State of the art vs biological variability: comparison on hematology parameters using Spanish EQAS data. Int J Lab Hematol 2018;40:284–91. https://doi.org/10.1111/ijlh.12783.Search in Google Scholar PubMed

15. Jiao, Y, Qiu, Z, Xie, J, Li, D, Li, T. Reference ranges and age-related changes of peripheral blood lymphocyte subsets in Chinese healthy adults. Sci China C Life Sci 2009;52:643–50. https://doi.org/10.1007/s11427-009-0086-4.Search in Google Scholar PubMed

16. Choi, YH, Shim, H, Park, CJ, Han, SH, Hwang, K, Jang, S, Chi, HS. Flow cytometric assays for lymphocyte subset enumeration: CD45 is inevitable for lymphocyte gating and CD16 is essential for NK cells. Lab Med Online 2013;3:79–87. https://doi.org/10.3343/lmo.2013.3.2.79.Search in Google Scholar

17. CLSI. Enumeration of immunologically defined cell populations by flow cytometry; Approved guideline – Second Edition. CLSI document H42-A2. 940 West Valley Road, Suite 1400, Wayne, Pennsylvania: Clinical and Laboratory Standards Institute; 2007.Search in Google Scholar

18. CLSI. Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guidelines – Third Edition, CLSI document EP28-A3c. 940 West Valley Road, Suite 1400, Wayne, Pennsylvania: Clinical and Laboratory Standards Institute; 2008.Search in Google Scholar

19. Coucke, W, Soumali, MR. Demystifying EQA statistics and reports. Biochem Med (Zagreb) 2017;27:37–48. https://doi.org/10.11613/bm.2017.006.Search in Google Scholar

20. International Organization for Standardization. Statistical methods for use in proficiency testing by interlaboratory comparisons. Geneva: ISO; 2015.Search in Google Scholar

21. Morancho, JFE. Diagrams of the state of art extracted from the external quality assessment scheme. Use for the selection of quality specifications. An Clin 2002:101–34. URL: http://www.aefa.es/wp-content/uploads/2014/04/Graficas-del-Estado-del-arte-extraidas-del-PSEC-2002_entero.pdf.Search in Google Scholar

22. Aarsand, AK, Roraas, T, Fernandez-Calle, P, Ricos, C, Diaz-Garzon, 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

23. Fraser, CG, Hyltoft Petersen, P, Libeer, JC, Ricos, C. Proposals for setting generally applicable quality goals solely based on biology. Ann Clin Biochem 1997;34:8–12. https://doi.org/10.1177/000456329703400103.Search in Google Scholar PubMed

24. Wong, WS, Lo, AW, Siu, LP, Leung, JN, Tu, SP, Tai, SW, et al. Reference ranges for lymphocyte subsets among healthy Hong Kong Chinese adults by single-platform flow cytometry. Clin Vaccine Immunol 2013;20:602–6. https://doi.org/10.1128/cvi.00476-12.Search in Google Scholar PubMed PubMed Central

25. Zhang, K, Wang, F, Zhang, M, Cao, X, Yang, S, Jia, S, et al. Reference ranges of lymphocyte subsets balanced for age and gender from a population of healthy adults in Chongqing District of China. Cytometry B Clin Cytom 2016;90:538–42. https://doi.org/10.1002/cyto.b.21323.Search in Google Scholar PubMed

26. Chng, WJ, Tan, GB, Kuperan, P. Establishment of adult peripheral blood lymphocyte subset reference range for an Asian population by single-platform flow cytometry: influence of age, sex, and race and comparison with other published studies. Clin Diagn Lab Immunol 2004;11:168–73. https://doi.org/10.1128/cdli.11.1.168-173.2004.Search in Google Scholar PubMed PubMed Central

27. Kallner, A, McQueen, M, Heuck, C. The Stockholm Consensus Conference on quality specifications in laboratory medicine, 25–26 April 1999. Scand J Clin Lab Invest 1999;59:475–6. https://doi.org/10.1080/00365519950185175.Search in Google Scholar PubMed

28. Fraser, CG. The 1999 Stockholm Consensus Conference on quality specifications in laboratory medicine. Clin Chem Lab Med 2015;53:837–40. https://doi.org/10.1515/cclm-2014-0914.Search in Google Scholar PubMed

29. Perich, C, Minchinela, J, Ricos, C, Fernandez-Calle, P, Alvarez, V, Domenech, MV, et al. Biological variation database: structure and criteria used for generation and update. Clin Chem Lab Med 2015;53:299–305. https://doi.org/10.1515/cclm-2014-0739.Search in Google Scholar PubMed

30. Bartlett, WA, Braga, F, Carobene, A, Coskun, 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

31. Salas, A, Ricos, C, Prada, E, Ramon, F, Morancho, J, Jou, JM, et al. State-of-the-art approach to goal setting. Clin Lab Med 2017;37:73–84. https://doi.org/10.1016/j.cll.2016.09.007.Search in Google Scholar PubMed

32. Vis, JY, Huisman, A. Verification and quality control of routine hematology analyzers. Int J Lab Hematol 2016;38:100–9. https://doi.org/10.1111/ijlh.12503.Search in Google Scholar PubMed

33. Buttarello, M, Plebani, M. Automated blood cell counts: state of the art. Am J Clin Pathol 2008;130:104–16. https://doi.org/10.1309/ek3c7ctdknvpxvtn.Search in Google Scholar

34. Jones, GR, Sikaris, K, Gill, J. ‘Allowable limits of performance’ for external quality assurance programs – an approach to application of the Stockholm criteria by the RCPA Quality Assurance Programs. Clin Biochem Rev 2012;33:133–9. PMID: 23267245; PMCID:PMC3529550.Search in Google Scholar

35. Li, C, Peng, M, Xu, D, Lu, H, Zhou, W, Liu, Y, et al. Commutability assessment of reference materials for the enumeration of lymphocyte subsets. Clin Chem Lab Med 2019;57:697–706. https://doi.org/10.1515/cclm-2018-0915.Search in Google Scholar PubMed


Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2020-0741).


Received: 2020-05-17
Accepted: 2020-06-19
Published Online: 2020-07-22
Published in Print: 2021-01-26

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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