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Licensed Unlicensed Requires Authentication Published by De Gruyter April 10, 2014

Evaluation of the automated coagulation analyzer CS-5100 and its utility in high throughput laboratories

Franz Ratzinger, Klaus G. Schmetterer, Helmuth Haslacher, Thomas Perkmann, Sabine Belik and Peter Quehenberger

Abstract

Background: Automated analyzers are an important component of modern laboratories. As a representative of the newest generation of coagulation analyzers, the CS-5100 features several technical refinements including a pre-analytical assessment unit as well as multi-wavelength optical detection units. Therefore, the CS-5100 is supposed to rapidly and accurately perform a broad panel of coagulation tests. In the current study, the CS-5100 was evaluated regarding its precision and practicability in a clinical laboratory setting.

Methods: The CS-5100 was evaluated regarding its intra- and inter-assay precision using commercially available control samples. Results of patient samples, including hemolytic, icteric and lipemic specimens, measured on the CS-5100 were compared to reference analyzers, which are used in our accredited laboratory.

Results: The coefficients of variation, assessed in the intra- and inter-assay precision analyses were below 5% representatively for most parameters. Results, obtained by the CS-5100 showed predominantly a high comparability to used reference analyzers, with correlation coefficients ranging from 0.857 to 0.990. Only minor ranged systemic or proportional differences were found in Passing-Bablok regression between the CS-5100 and reference analyzers regarding most of the tested parameters. Lipemic samples had a tendency to deteriorate correlation coefficients, but an overall effect of the sample’s triglyceride level could be ruled out. In a routine setting, the analyzer reached a sample throughput rate of 160 tests per hour.

Conclusions: The CS-5100 is able to rapidly and precisely measure patient samples. No considerable influence on test comparability was found for elevated levels of free hemoglobin, bilirubin or triglycerides.


Corresponding author: Peter Quehenberger, Department of Laboratory Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria, Phone: +43 1 40400 5383, Fax: +43 1 40400 5392, E-mail:

Acknowledgments

We are grateful that Siemens provide required reagents for measuring on the CS-5100.

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. Research support 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.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

References

1. Söderberg J, Jonsson PA, Wallin O, Grankvist K, Hultdin J. Haemolysis index – an estimate of preanalytical quality in primary health care. Clin Chem Lab Med 2009;47:940–4.10.1515/CCLM.2009.227Search in Google Scholar

2. Lippi G, Blanckaert N, Bonini P, Green S, Kitchen S, Palicka V, et al. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories. Clin Chem Lab Med 2008;46:764–72.10.1515/CCLM.2008.170Search in Google Scholar

3. Favaloro EJ, Lippi G, Adcock DM. Preanalytical and postanalytical variables: the leading causes of diagnostic error in hemostasis? Semin Thromb Hemost 2008;34:612–34.10.1055/s-0028-1104540Search in Google Scholar

4. Forsman RW. Why is the laboratory an afterthought for managed care organizations? Clin Chem 1996;42:813–6.10.1093/clinchem/42.5.813Search in Google Scholar

5. Passing H, Bablok. A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. J Clin Chem Clin Biochem 1983;21:709–20.Search in Google Scholar

6. Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinical measurment. Lancet 1986;327:307–10.10.1016/S0140-6736(86)90837-8Search in Google Scholar

7. Hanneman SK. Design, analysis, and interpretation of method comparison studies. AACN Adv Crit Care 2008;19: 223–34.Search in Google Scholar

8. Pearson ECFHOHES. Tests for rank correlation coefficients. I. Biometrika 1957;44:470–81.10.1093/biomet/44.3-4.470Search in Google Scholar

9. Richard L. Gorsuch CS. Correlation coefficients: mean bias and confidence interval distortions. J Methods Meas Soc Sci 2010;1:52–65.Search in Google Scholar

10. Fisher RA. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika 1915;10:507–21.10.2307/2331838Search in Google Scholar

11. Salvagno GL, Lippi G, Bassi A, Poli G, Guidi GC. Prevalence and type of pre-analytical problems for inpatients samples in coagulation laboratory. J Eval Clin Pract 2008;14:351–3.10.1111/j.1365-2753.2007.00875.xSearch in Google Scholar PubMed

12. Green SF. The cost of poor blood specimen quality and errors in preanalytical processes. Clin Biochem 2013;46:1175–9.10.1016/j.clinbiochem.2013.06.001Search in Google Scholar PubMed

13. Fischer F, Appert-Flory A, Jambou D, Toulon P. Evaluation of the automated coagulation analyzer Sysmex CA-7000. Thromb Res 2006;117:721–9.10.1016/j.thromres.2005.06.012Search in Google Scholar PubMed

14. Quehenberger P, Kapiotis S, Handler S, Ruzicka K, Speiser W. Evaluation of the automated coagulation analyzer SYSMEX CA 6000. Thromb Res 1999;96:65–71.10.1016/S0049-3848(99)00069-9Search in Google Scholar

15. Appert-Flory A, Fischer F, Jambou D, Toulon P. Evaluation and performance characteristics of the automated coagulation analyzer ACL TOP. Thromb Res 2007;120:733–43.10.1016/j.thromres.2006.12.002Search in Google Scholar PubMed

16. Milos M, Herak DC, Zadro R. Discrepancies between APTT results determined with different evaluation modes on automated coagulation analyzers. Int J Lab Hematol 2010;32:33–9.10.1111/j.1751-553X.2008.01111.xSearch in Google Scholar PubMed

17. Molenaar PJ, Leyte A. Pre-acquisition system assessment of the Sysmex(®) Coagulation System CS-2100i and comparison with end-user verification; a model for the regional introduction of new analysers and methods. Clin Chem Lab Med 2011;49:1479–89.Search in Google Scholar

18. de Bie P, Schornagel WJ, van den Dool EJ, Bakker B, van Dam W, Heckman M, et al. Laboratory evaluation of the Coasys® Plus C coagulation analyzer. Thromb Res 2013;131:357–62.10.1016/j.thromres.2013.02.004Search in Google Scholar PubMed

19. Mullier F, Vanpee D, Jamart J, Dubuc E, Bailly N, Douxfils J, et al. Comparison of five D-dimer reagents and application of an age-adjusted cut-off for the diagnosis of venous thromboembolism in emergency department. Blood Coagul Fibrinolysis 2013 Nov 15. [Epub ahead of print].10.1097/MBC.0000000000000020Search in Google Scholar PubMed

20. Park SJ, Chi HS, Chun SH, Jang S, Park CJ. Evaluation of performance including influence by interfering substances of the innovance D-dimer assay on the Sysmex coagulation analyzer. Ann Clin Lab Sci 2011;41:20–4.Search in Google Scholar

21. Jennings I, Woods TA, Kitchen DP, Kitchen S, Walker ID. Laboratory D-dimer measurement: improved agreement between methods through calibration. Thromb Haemost 2007;98:1127–35.10.1055/s-0037-1613742Search in Google Scholar

22. Milos M, Herak D, Kuric L, Horvat I, Zadro R. Evaluation and performance characteristics of the coagulation system: ACL TOP analyzer – HemosIL reagents. Int J Lab Hematol 2009;31:26–35.10.1111/j.1751-553X.2007.00999.xSearch in Google Scholar

23. Flanders MM, Crist R, Safapour S, Rodgers GM. Evaluation and performance characteristics of the STA-R coagulation analyzer. Clin Chem 2002;48:1622–4.10.1093/clinchem/48.9.1622Search in Google Scholar

24. Magari RT. Bias estimation in method comparison studies. J Biopharm Stat 2004;14:881–92.10.1081/BIP-200035450Search in Google Scholar PubMed

25. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–60.10.1177/096228029900800204Search in Google Scholar PubMed

26. Bilic-Zulle L. Comparison of methods: Passing and Bablok regression. Biochem Med 2011;21:49–52.10.11613/BM.2011.010Search in Google Scholar

Received: 2013-12-18
Accepted: 2014-3-13
Published Online: 2014-4-10
Published in Print: 2014-8-1

©2014 by Walter de Gruyter Berlin/Boston

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