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Licensed Unlicensed Requires Authentication Published by De Gruyter January 13, 2015

Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-Dyn Sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000

Mathias Bruegel EMAIL logo , Dorothea Nagel , Manuela Funk , Petra Fuhrmann , Johannes Zander and Daniel Teupser


Background: Various types of automated hematology analyzers are used in clinical laboratories. Here, we performed a side-by-side comparison of five current top of the range routine hematology analyzers in the setting of a university hospital central laboratory.

Methods: Complete blood counts (CBC), differentials, reticulocyte and nucleated red blood cell (NRBC) counts of 349 patient samples, randomly taken out of routine diagnostics, were analyzed with Cell-Dyn Sapphire (Abbott), DxH 800 (Beckman Coulter), Advia 2120i (Siemens), XE-5000 and XN-2000 (Sysmex). Inter-instrument comparison of CBCs including reticulocyte and NRBC counts and investigation of flagging quality in relation to microscopy were performed with the complete set of samples. Inter-instrument comparison of five-part differential was performed using samples without atypical cells in blood smear (n=292). Automated five-part differentials and NRBCs were additionally compared with microscopy.

Results: The five analyzers showed a good concordance for basic blood count parameters. Correlations between instruments were less well for reticulocyte counts, NRBCs, and differentials. The poorest concordance for NRBCs with microscopy was observed for Advia 2120i (Kendall’s τb=0.37). The highest flagging sensitivity for blasts was observed for XN-2000 (97% compared to 65%–76% for other analyzers), whereas overall specificity was comparable between different instruments.

Conclusions: To the best of our knowledge, this is the most comprehensive side-by-side comparison of five current top of the range routine hematology analyzers. Variable analyzer quality and parameter specific limitations must be considered in defining laboratory algorithms in clinical practice.

Corresponding author: Mathias Bruegel, Institute of Laboratory Medicine, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, 81377 Munich, Germany, Phone: +49 89 4400 73209, Fax: +49 89 4400 78888, E-mail:


We thank Abbott, Beckman Coulter, Siemens and Sysmex for their support of the study by providing their instruments for evaluation free of charge.

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

Financial support: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(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.


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Supplemental Material

The online version of this article (DOI: 10.1515/cclm-2014-0495) offers supplementary material, available to authorized users.

Received: 2014-9-24
Accepted: 2014-12-7
Published Online: 2015-1-13
Published in Print: 2015-6-1

©2015 by De Gruyter

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