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July 7, 2019Clinical Chemistry and Laboratory Medicine (CCLM)51 citationsOpen Access

Comparison of five automated urine sediment analyzers with manual microscopy for accurate identification of urine sediment

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JCJooyoung ChoYonsei UniversityKOKyeong Jin OhYonsei UniversityBJBeom Chan JeonYonsei University

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Abstract

Background While the introduction of automated urine analyzers is expected to reduce the labor involved, turnaround time and potential assay variations, microscopic examination remains the "gold standard" for the analysis of urine sediments. In this study, we evaluated the analytical and diagnostic performance of five recently introduced automated urine sediment analyzers. Methods A total of 1016 samples were examined using five automated urine sediment analyzers and manual microscopy. Concordance of results from each automated analyzer and manual microscopy were evaluated. In addition, image and microscopic review rates of each system were investigated. Results The proportional bias for red blood cells (RBCs), white blood cells (WBCs) and squamous epithelial cells in the automated urine sediment analyzers were within ±20% of values obtained using the manual microscope, except in the cases of RBCs and WBCs analyzed using URiSCAN PlusScope and Iris iQ200SPRINT, respectively. The sensitivities of Roche Cobas® u 701 and Siemens UAS800 for pathologic casts (73.6% and 81.1%, respectively) and crystals (62.2% and 49.5%, respectively) were high, along with high image review rates (24.6% and 25.2%, respectively). The detection rates for crystals, casts and review rates can be changed for the Sysmex UF-5000 platform according to cut-off thresholds. Conclusions Each automated urine sediment analyzer has certain distinct features, in addition to the common advantages of reducing the burden of manual processing. Therefore, laboratory physicians are encouraged to understand these features, and to utilize each system in appropriate ways, considering clinical algorithms and laboratory workflow.

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Cho et al. (2019) studied this question.

synapsesocial.com/papers/6a29f46a50486b5801f94cfchttps://doi.org/10.1515/cclm-2019-0211
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