Comparative evaluation of routine coagulation testing on Stago sthemO 301 and Werfen ACL TOP 750
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Simone Denitto
,
Gian Luca Salvagno
Abstract
Objectives
We performed a comparative evaluation of analytical performance between the novel bench-top Stago sthemO 301 hemostasis analyzer and the Werfen ACL TOP 750 coagulometer using routine plasma samples.
Methods
A minimum of 100 fresh plasma samples per test were analyzed using both analytical systems. The tests included prothrombin time (PT) expressed as international normalized ratio (INR) and seconds (s), activated partial thromboplastin time (APTT) in ratio and seconds, fibrinogen, antithrombin and D-dimer. Clotting assays were performed using mechanical clot detection on sthemO and optical detection on ACL TOP. The comparative evaluation included the calculation of Spearman’s correlation, Passing–Bablok regression and Bland–Altman plots.
Results
Correlation coefficients ranged between 0.76 for APTT to 0.98 for PT-INR and D-dimer, indicating a generally acceptable/good agreement. The regression slopes varied from 0.82 for D-dimer to 1.17 for APTT-s. A significant bias was observed for all tests except antithrombin, with differences for sthemO ranging between −31 % for D-dimer and 13.7 % for PT-s.
Conclusions
SthemO demonstrates acceptable global comparability with ACL TOP for routine coagulation testing. Nevertheless, reagent- and method-dependent bias has been observed, which highlight the need for additional harmonization efforts.
Acknowledgments
The authors are thankful to Barbara Cortivo, Livia Tretti, Francesco Russo and Chiara Zecchetto for their skilled technical assistance. The manufacturers had no role in directing the study or influencing the interpretation of the study results.
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Research ethics: This study was conducted in accordance with the Declaration of Helsinki. The protocol was approved by the local Ethics Committee (approval number 971CESC; July 20, 2016).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Data will be available from the corresponding author upon reasonable request.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/dx-2025-0018).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
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