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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Greaves, Ronda / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter


IMPACT FACTOR 2018: 3.638

CiteScore 2018: 2.44

SCImago Journal Rank (SJR) 2018: 1.191
Source Normalized Impact per Paper (SNIP) 2018: 1.205

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1437-4331
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Volume 57, Issue 8

Issues

Determination of sigma score based on biological variation for haemostasis assays: fit-for-purpose for daily practice?

Martine J. Hollestelle
  • Corresponding author
  • ECAT Foundation (External quality Control for Assays and Tests), P.O. Box 107, 2250AC Voorschoten, The Netherlands
  • Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands, Phone: +31.71.3030910, Fax: +31.71.3030919
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Janneke Ruinemans-Koerts / René N. Idema
  • Laboratory for Clinical Chemistry and Hematology, Amphia Hospital, Breda, Noord Brabant, The Netherlands
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Piet Meijer / Moniek P.M. de Maat
Published Online: 2019-01-10 | DOI: https://doi.org/10.1515/cclm-2018-0934

Abstract

Background

Internal quality control (QC) rules for laboratory tests can be derived from analytical performance specifications (APS) using the six-sigma method. We tested the applicability of this paradigm to routine haemostasis measurements.

Methods

Three laboratories using different instruments and reagents calculated sigma scores for their prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen and antithrombin (AT) measurements. Sigma scores were calculated using biological variation (BV) data from the literature in combination with internal and external QC data.

Results

Wide ranges in sigma scores for the PT (0.1–6.8), APTT (0.0–4.3), fibrinogen (1.5–8.3) and AT (0.1–2.4) were observed when QC data was combined with the minimum, median and maximum value of BV data, due in particular to a large variation in within-subject and between-subjects coefficients of variation. When the median BV values were applied, most sigma scores were below 3.0, for internal QC data; 75% and for external QC data; 92%.

Conclusions

Our findings demonstrate that: (1) The sigma scores for common haemostasis parameters are relatively low, and (2) The application of the six-sigma method to BV-derived APS is hampered by the large variation in published BV data. As the six-sigma concept is based on requirements for monitoring, and many haemostasis tests are only designed for diagnostic purposes, a fit-for-purpose APS is needed to achieve clinically relevant quality goals.

Keywords: biological variation; haemostasis; quality control data; sigma score

References

  • 1.

    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.PubMedWeb of ScienceGoogle Scholar

  • 2.

    Oosterhuis WP, Bayat H, Armbruster D, Coskun A, Freeman KP, Kallner A, et al. The use of error and uncertainty methods in the medical laboratory. Clin Chem Lab Med 2018;56:209–19.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 3.

    Fraser CG, Harris EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409–37.PubMedCrossrefGoogle Scholar

  • 4.

    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.CrossrefPubMedGoogle Scholar

  • 5.

    Harris EK. Proposed goals for analytical precision and accuracy in single-point diagnostic testing. Theoretical basis and comparison with data from College of American Pathologists proficiency surveys. Arch Pathol Lab Med 1988;112: 416–20.PubMedGoogle Scholar

  • 6.

    Oosterhuis WP. Analytical performance specification in clinical chemistry: the holy grail? J Lab Precis Med 2017;02:78.CrossrefGoogle Scholar

  • 7.

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

  • 8.

    Westgard JO. Useful measures and models for analytical quality management in medical laboratories. Clin Chem Lab Med 2016;54:223–33.Web of SciencePubMedGoogle Scholar

  • 9.

    Westgard S, Bayat H, Westgard JO. Analytical Sigma metrics: a review of Six Sigma implementation tools for medical laboratories. Biochem Med (Zagreb) 2018;28:020502.PubMedCrossrefGoogle Scholar

  • 10.

    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.Web of SciencePubMedGoogle Scholar

  • 11.

    Schoenmakers CH, Naus AJ, Vermeer HJ, van Loon D, Steen G. Practical application of Sigma Metrics QC procedures in clinical chemistry. Clin Chem Lab Med 2011;49:1837–43.PubMedWeb of ScienceGoogle Scholar

  • 12.

    Tran MT, Hoang K, Greaves RF. Practical application of biological variation and Sigma metrics quality models to evaluate 20 chemistry analytes on the Beckman Coulter AU680. Clin Biochem 2016;49:1259–66.Web of ScienceCrossrefPubMedGoogle Scholar

  • 13.

    El Sharkawy R, Westgard S, Awad AM, Ahmed AO, Iman EH, Gaballah A, et al. Comparison between Sigma metrics in four accredited Egyptian medical laboratories in some biochemical tests: an initiative towards sigma calculation harmonization. Biochem Med (Zagreb) 2018;28:020711.CrossrefPubMedGoogle Scholar

  • 14.

    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.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 15.

    Thelen MH, Jansen RT, Weykamp CW, Steigstra H, Meijer R, Cobbaert CM. Expressing analytical performance from multi-sample evaluation in laboratory EQA. Clin Chem Lab Med 2017;55:1509–16.PubMedWeb of ScienceGoogle Scholar

  • 16.

    Libeer JC, Baadenhuijsen H, Fraser CG, Petersen PH, Ricos C, Stockl D, et al. Characterization and classification of external quality assessment schemes (EQA) according to objectives such as evaluation of method and participant bias and standard deviation. External quality assessment (EQA) Working group A on analytical goals in laboratory medicine. Eur J Clin Chem Clin Biochem 1996;34:665–78.Google Scholar

  • 17.

    Gowans EM, Hyltoft Petersen P, Blaabjerg O, Horder 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.CrossrefPubMedGoogle Scholar

  • 18.

    Meijer P, de Maat MP, Kluft C, Haverkate F, van Houwelingen HC. Long-term analytical performance of hemostasis field methods as assessed by evaluation of the results of an external quality assessment program for antithrombin. Clin Chem 2002;48:1011–5.PubMedGoogle Scholar

  • 19.

    Chen Q, Shou W, Wu W, Guo Y, Zhang Y, Huang C, et al. Biological and analytical variations of 16 parameters related to coagulation screening tests and the activity of coagulation factors. Semin Thromb Hemost 2015;41:336–41.PubMedWeb of ScienceCrossrefGoogle Scholar

  • 20.

    Shou W, Chen Q, Wu W, Cui W. Biological variations of lupus anticoagulant, antithrombin, protein C, protein S, and von Willebrand factor assays. Semin Thromb Hemost 2016;42:87–92.PubMedWeb of ScienceGoogle Scholar

  • 21.

    Riese H, Vrijkotte TG, Meijer P, Kluft C, De Geus EJ. Covariance of metabolic and haemostatic risk indicators in men and women. Fibrinol Proteol 2001;14:1–12.Google Scholar

  • 22.

    Thompson SG, Martin JC, Meade TW. Sources of variability in coagulation factor assays. Thromb Haemost 1987;58:1073–7.CrossrefPubMedGoogle Scholar

  • 23.

    Salomaa V, Rasi V, Stengard J, Vahtera E, Pekkanen J, Vartiainen E, et al. Intra- and interindividual variability of hemostatic factors and traditional cardiovascular risk factors in a 3-year follow-up. Thromb Haemost 1998;79:969–74.CrossrefGoogle Scholar

  • 24.

    Blomback M, Eneroth P, Landgren BM, Lagerstrom M, Anderson O. On the intraindividual and gender variability of haemostatic components. Thromb Haemost 1992;67:70–5.PubMedCrossrefGoogle Scholar

  • 25.

    de Maat MP, van Schie M, Kluft C, Leebeek FW, Meijer P. Biological variation of hemostasis variables in thrombosis and bleeding: consequences for performance specifications. Clin Chem 2016;62:1639–46.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 26.

    Dot D, Miro J, Fuentes-Arderiu X. Within-subject and between-subject biological variation of prothrombin time and activated partial thromboplastin time. Ann Clin Biochem 1992;29(Pt 4):422–5.CrossrefPubMedGoogle Scholar

  • 27.

    Wada Y, Kurihara M, Toyofuku M, Kawamura M, Iida H, Kayamori Y, et al. Analytical goals for coagulation tests based on biological variation. Clin Chem Lab Med 2004;42:79–83.PubMedGoogle Scholar

  • 28.

    Costongs GM, Bas BM, Janson PC, Hermans J, Brombacher PJ, van Wersch JW. Short-term and long-term intra-individual variations and critical differences of coagulation parameters. J Clin Chem Clin Biochem 1985;23:405–10.PubMedGoogle Scholar

  • 29.

    Rudez G, Meijer P, Spronk HM, Leebeek FW, ten Cate H, Kluft C, et al. Biological variation in inflammatory and hemostatic markers. J Thromb Haemost 2009;7:1247–55.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 30.

    Chambless LE, McMahon R, Wu K, Folsom A, Finch A, Shen YL. Short-term intraindividual variability in hemostasis factors. The ARIC Study. Atherosclerosis Risk in Communities Intraindividual Variability Study. Ann Epidemiol 1992;2:723–33.CrossrefPubMedGoogle Scholar

  • 31.

    Sakkinen PA, Macy EM, Callas PW, Cornell ES, Hayes TE, Kuller LH, et al. Analytical and biologic variability in measures of hemostasis, fibrinolysis, and inflammation: assessment and implications for epidemiology. Am J Epidemiol 1999;149:261–7.CrossrefPubMedGoogle Scholar

  • 32.

    Marckmann P, Sandstrom B, Jespersen J. The variability of and associations between measures of blood coagulation, fibrinolysis and blood lipids. Atherosclerosis 1992;96:235–44.CrossrefPubMedGoogle Scholar

  • 33.

    de Maat MP, de Bart AC, Hennis BC, Meijer P, Havelaar AC, Mulder PG, et al. Interindividual and intraindividual variability in plasma fibrinogen, TPA antigen, PAI activity, and CRP in healthy, young volunteers and patients with angina pectoris. Arterioscler Thromb Vasc Biol 1996;16:1156–62.CrossrefPubMedGoogle Scholar

  • 34.

    Carobene A. Reliability of biological variation data available in an online database: need for improvement. Clin Chem Lab Med 2015;53:871–7.Web of ScienceGoogle Scholar

  • 35.

    Harrison HH, Jones JB. Using Sigma quality control to verify and monitor performance in a multi-instrument, multisite integrated health care network. Clin Lab Med 2017;37: 207–41.CrossrefWeb of ScienceGoogle Scholar

  • 36.

    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.Web of ScienceCrossrefPubMedGoogle Scholar

  • 37.

    Badrick T, Graham P. Can a combination of average of normals and “real time” external quality assurance replace internal quality control? Clin Chem Lab Med 2018;56:549–53.PubMedWeb of ScienceCrossrefGoogle Scholar

  • 38.

    Westgard JO, Westgard SA. The quality of laboratory testing today: an assessment of sigma metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance. Am J Clin Pathol 2006;125:343–54.CrossrefPubMedGoogle Scholar

  • 39.

    Meijer P, Kluft C, Haverkate F, De Maat MP. The long-term within- and between-laboratory variability for assay of antithrombin, and proteins C and S: results derived from the external quality assessment program for thrombophilia screening of the ECAT Foundation. J Thromb Haemost 2003;1:748–53.CrossrefGoogle Scholar

About the article

Received: 2018-08-28

Accepted: 2018-11-14

Published Online: 2019-01-10

Published in Print: 2019-07-26


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

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organisation(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 57, Issue 8, Pages 1235–1241, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2018-0934.

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