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pISSN 2384-2458 eISSN 2288-7261
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Original Article

J Lab Med Qual Assur 2020; 42(1): 26-32

Published online March 31, 2020

https://doi.org/10.15263/jlmqa.2020.42.1.26

Copyright © Korean Association of External Quality Assessment Service.

Comparison of Body Fluid Differential Counts Using a Manual Counting Method or an Automated Hematology Analyzer

Jiwon Lee, Kibum Jeon, Jisoo Lee, Miyoung Kim, Han-Sung Kim, Hee Jung Kang, and Young Kyung Lee

Department of Laboratory Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea

Correspondence to:Miyoung Kim
Department of Laboratory Medicine, Hallym University Sacred Heart Hospital, 22 Gwanpyeong-ro 170beongil, Dongan-gu, Anyang 14068, Korea
Tel +82-31-380-1795 Fax +82-31-380-1798 E-mail rabbit790622@gmail.com

Received: October 23, 2019; Revised: December 20, 2019; Accepted: December 26, 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Two methods of counting cells in body fluids were compared; manual counting using a Neubauer chamber, and automated cell counting using an XN-350 hematology analyzer.
Methods: Cells from 32 body fluid samples were counted by manual examination and by an automated analyzer. Total cells (TC), white blood cells (WBC), red blood cells (RBC), polymorphonuclear leukocytes (PMN), mononuclear leukocytes (MN), neutrophils, lymphocytes, monocytes, and eosinophils were each counted by both methods. The results were compared using the Pearson correlation test, Bland-Altman regression analysis, and Passing-Bablok regression analysis.
Results: The two methods showed very strong correlation in TC, WBC, RBC, PMN, and MN counts, strong correlation in % neutrophils, and % lymphocytes, and weak correlation in % monocytes and % eosinophils. Using Bland-Altman regression analysis, the mean biases for TC, WBC, and RBC were -270, -257.4, and -1,256.09, respectively, and 0.15 for PMN and MN. Research parameters were compared as well: mean biases were -1.31, -2.46, -5.16, and -3.58 for % neutrophils, % monocytes, % lymphocytes, and % eosinophils, respectively. Passing-Bablok regression equations were y=1.039x+20, y=1.037x+19, y=1.259x+0.0, y=0.983x+1.541, and y=0.983x+0.125 for TC, WBC, RBC, PMN, and MN, respectively. The equations were y=0.955x+2.194 for % neutrophils, y=0.965x+1.184 for % monocytes, y=1.003x+0.161 for % lymphocytes, and y=x+0.75 for % eosinophils.
Conclusions: WBC differential count results performed by an automated hematology analyzer generally show good correlation with our reference method, Neubauer chamber counting.

Keywords: Body fluids, Differential count, Automated hematology analyzer

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