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논문 기본 정보

자료유형
학술저널
저자정보
Cho Hanwool (Department of Laboratory Medicine Seoul St. Mary’s Hospital College of Medicine The Catholic Univer) Yoo Jaeeun (Department of Laboratory Medicine Incheon St. Mary’s Hospital College of Medicine The Catholic Univ) Kim Hyunjung (Department of Laboratory Medicine Uijeongbu St. Mary’s Hospital College of Medicine The Catholic Un) Jang Hyunsik (Department of Laboratory Medicine Seoul St. Mary’s Hospital College of Medicine The Catholic Univer) Kim Yonggoo (Department of Laboratory Medicine Seoul St. Mary’s Hospital College of Medicine The Catholic Univer) Chae Hyojin (Department of Laboratory Medicine Seoul St. Mary’s Hospital College of Medicine The Catholic Univer)
저널정보
대한진단검사의학회 Annals of Laboratory Medicine Annals of Laboratory Medicine 제42권 제2호
발행연도
2022.3
수록면
160 - 168 (9page)
DOI
10.3343/alm.2022.42.2.160

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Background: Automated urine sediment analysis has been developed to address the limitations of microscopic examination of dysmorphic red blood cells (RBCs). We evaluated the urinary RBC distribution (URD) parameter of a recently launched automated urinary flow cytometry analyzer, UF-5000 (Sysmex, Kobe, Japan), to differentiate glomerular hematuria (GH) from non-GH (NGH). Methods: Samples submitted for urine sediment analysis from patients with hematuria (>20 RBCs/μL) were divided into derivation (N=156; 101 GH, 55 NGH) and validation cohorts (N=107; 60 GH, 47 NGH). The clinical diagnosis of GH or NGH was established based on clinical data review. Differences in UF-5000 parameters (URD, small RBC, lysed RBC, RBC-P70FSC, RBC-SF-FSC-W, mean forward-scattered light, and mean side-scattered light) between GH and NGH, and areas under the ROC curves (AUC) were analyzed in the derivation cohort. The derived ideal cut-off value was evaluated in the validation cohort. We applied the Kitasato criteria to compare the diagnostic performance. Results: URD (%), differed significantly between GH and NGH (P<0.001) in the two cohorts. The AUC of URD was 0.814 and 0.806 in the derivation and validation cohorts, respectively. Using a cut-off of >20.1%, the sensitivity was 99.0%/89.4% and the specificity was 50.9%/63.3% in the derivation/validation cohort. When the Kitasato criteria were applied, the sensitivity and specificity were 80.2% and 52.7%, respectively. Conclusions: URD is a rapid, objective, and quantitative measure that can be used to differentiate GH and NGH.

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