The microscopic examination of hematuria, a cardinal symptom of glomerulonephritis (GN), is time-consuming and labor-intensive. As an alternative, the fully automated urine particle analyzer UF-5000 can interpret the morphological information of the glomerular red blood cells (RBCs) using parameters such as UF-5000 small RBCs (UF-%sRBCs) and Lysed-RBCs.
Hematuria samples from 203 patients were analyzed using the UF-5000 and blood and urine chemistries to determine the cut-off values of RBC parameters for GN and non-glomerulonephritis (NGN) classification and confirm their sensitivity to the IgA nephropathy and non-IgA nephropathy groups.
The UF-%sRBCs and Lysed-RBCs values differed significantly between the GN and NGN groups. The cut-off value of UF-%sRBCs was >56.8% (area under the curve, 0.649; sensitivity, 94.1%; specificity, 38.1%; positive predictive value, 68.3%; and negative predictive value, 82.1%), while that for Lysed-RBC was >4.6/μL (area under the curve, 0.708; sensitivity, 82.4%; specificity, 56.0%; positive predictive value, 72.6%; and negative predictive value, 69.1%). Moreover, there was no significant difference in the sensitivity between the IgA nephropathy and non-IgA nephropathy groups (87.1 and 89.8% for UF-%sRBCs and 83.9 and 78.4% for Lysed-RBCs, respectively). In the NGN group, the cut-off values showed low sensitivity (56.0% for UF-%sRBCs and 44.0% for Lysed-RBCs).
The RBC parameters of the UF-5000, specifically UF-%sRBCs and Lysed-RBCs, showed good cut-off values for the diagnosis of GN.
Funding source: Fujita Health University Grant
We would like to thank Editage (www.editage.jp) for English language editing.
Research funding: This study was supported by a Fujita Health University Grant (M. H.).
Author contributions: Mizuno G and Hoshi M planned the study. Mizuno G, Hoshi M, Nakamoto K, Sakurai M, Nagashima K, and Fujita T performed the experiments. Mizuno G, Hoshi M, and Nakamoto K were responsible for the data integrity and analysis. Mizuno G, Hoshi M, Nakamoto K, Sakurai M, Nagashima K, Fujita T, Ito H, and Hata T discussed the results. Mizuno G, Hoshi M, and Nakamoto K wrote the manuscript. Hoshi M, Ito H, and Hata T conducted the study. Hata T assumes the primary responsibility for the final content. All authors reviewed the manuscript.
Competing interests: The authors declare no potential conflicts of interest.
Informed consent: Informed consent was obtained from patients through an opt-out form posted on the hospital wall.
Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and has been approved by the authors’ Institutional Review Board Ethics Review Committee of Fujita Health University or equivalent committee (approval no. HM19-182).
1. Horie, S, Ito, S, Okada, H, Kikuchi, H, Narita, I, Nishiyama, T, et al.. Japanese guidelines of the management of hematuria 2013. Clin Exp Nephrol 2014;18:679–89. https://doi.org/10.1007/s10157-014-1001-2.Search in Google Scholar
3. Shichiri, M, Hosoda, K, Nishio, Y, Ogura, M, Suenaga, M, Saito, H, et al.. Red-cell-volume distribution curves in diagnosis of glomerular and non-glomerular haematuria. Lancet 1988;1:908–11. https://doi.org/10.1016/s0140-6736(88)91715-1.Search in Google Scholar
4. Shichiri, M, Oowada, A, Nishio, Y, Tomita, K, Shiigai, T. Use of autoanalyser to examine urinary-red-cell morphology in the diagnosis of glomerular haematuria. Lancet 1986;2:781–2. https://doi.org/10.1016/s0140-6736(86)90302-8.Search in Google Scholar
5. Kim, H, Kim, YO, Kim, Y, Suh, JS, Cho, EJ, Lee, HK. Small red blood cell fraction on the UF-1000i urine analyzer as a screening tool to detect dysmorphic red blood cells for diagnosing glomerulonephritis. Ann Lab Med 2019;39:271–7. https://doi.org/10.3343/alm.2019.39.3.277.Search in Google Scholar
6. Hamadah, AM, Gharaibeh, K, Mara, KC, Thompson, KA, Lieske, JC, Said, S, et al.. Urinalysis for the diagnosis of glomerulonephritis: role of dysmorphic red blood cells. Nephrol Dial Transpl 2018;33:1397–403. https://doi.org/10.1093/ndt/gfx274.Search in Google Scholar PubMed
7. Ahmad, G, Segasothy, M, Morad, Z. Urinary erythrocyte morphology as a diagnostic aid in haematuria. Singap Med J 1993;34:486–8.Search in Google Scholar
8. Ito, CA, Pecoits-Filho, R, Bail, L, Wosiack, MA, Afinovicz, D, Hauser, AB. Comparative analysis of two methodologies for the identification of urinary red blood cell casts. J Bras Nefrol 2011;33:402–7. https://doi.org/10.1590/s0101-28002011000400003.Search in Google Scholar
9. Chu-Su, Y, Shukuya, K, Yokoyama, T, Lin, WC, Chiang, CK, Lin, CW. Enhancing the detection of dysmorphic red blood cells and renal tubular epithelial cells with a modified urinalysis protocol. Sci Rep 2017;7:40521. https://doi.org/10.1038/srep40521.Search in Google Scholar PubMed PubMed Central
10. Nakayama, A, Tsuburai, H, Ebina, H, Kino, F. Outline and features of UF-5000, fully automated urine particle analyzer. Sysmex J Int 2018;28:21.Search in Google Scholar
11. De Rosa, R, Grosso, S, Lorenzi, G, Bruschetta, G, Camporese, A. Evaluation of the new Sysmex UF-5000 fluorescence flow cytometry analyser for ruling out bacterial urinary tract infection and for prediction of Gram negative bacteria in urine cultures. Clin Chim Acta 2018;484:171–8. https://doi.org/10.1016/j.cca.2018.05.047.Search in Google Scholar PubMed
12. Ippoliti, R, Allievi, I, Rocchetti, A. UF-5000 flow cytometer: a new technology to support microbiologists’ interpretation of suspected urinary tract infections. Microbiologyopen 2020;9:e987. https://doi.org/10.1002/mbo3.987.Search in Google Scholar PubMed PubMed Central
13. Ren, C, Wang, X, Yang, C, Li, S, Liu, S, Cao, H. Investigation of Atyp.C using UF-5000 flow cytometer in patients with a suspected diagnosis of urothelial carcinoma: a single-center study. Diagn Pathol 2020;15:77. https://doi.org/10.1186/s13000-020-00993-1.Search in Google Scholar
14. Enko, D, Stelzer, I, Bockl, M, Derler, B, Schnedl, WJ, Anderssohn, P, et al.. Comparison of the diagnostic performance of two automated urine sediment analyzers with manual phase-contrast microscopy. Clin Chem Lab Med 2020;58:268–73. https://doi.org/10.1515/cclm-2019-0919.Search in Google Scholar
15. Lewis, G, Maxwell, AP. Timely diagnosis and treatment essential in glomerulonephritis. Practitioner 2015;259:13–7.Search in Google Scholar
16. Haubitz, M, Wittke, S, Weissinger, EM, Walden, M, Rupprecht, HD, Floege, J, et al.. Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy. Kidney Int 2005;67:2313–20. https://doi.org/10.1111/j.1523-1755.2005.00335.x.Search in Google Scholar
17. Ohisa, N, Yoshida, K, Matsuki, R, Suzuki, H, Miura, H, Ohisa, Y, et al.. A comparison of urinary albumin-total protein ratio to phase-contrast microscopic examination of urine sediment for differentiating glomerular and nonglomerular bleeding. Am J Kidney Dis 2008;52:235–41. https://doi.org/10.1053/j.ajkd.2008.04.014.Search in Google Scholar
18. Japanese Association of Medical T. Editorial Committee of the Special Issue: Urinary S. Urinary sediment examination. Jpn J Med Technol 2017;66:51–85.Search in Google Scholar
19. Matsuo, S, Imai, E, Horio, M, Yasuda, Y, Tomita, K, Nitta, K, et al.. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009;53:982–92. https://doi.org/10.1053/j.ajkd.2008.12.034.Search in Google Scholar
20. Kageyama, Y, Shukuya, K, Tanaka, M, Hisasue, T, Hisasue, N, Morita, Y, et al.. Validation of morphological analysis of urinary red blood cells by an automated urinary flow cytometer. Jpn J Clin Lab Autom 2016;41:7.Search in Google Scholar
21. Pollock, C, Liu, PL, Gyory, AZ, Grigg, R, Gallery, ED, Caterson, R, et al.. Dysmorphism of urinary red blood cells – value in diagnosis. Kidney Int 1989;36:1045–9. https://doi.org/10.1038/ki.1989.299.Search in Google Scholar
22. Crop, MJ, de Rijke, YB, Verhagen, PC, Cransberg, K, Zietse, R. Diagnostic value of urinary dysmorphic erythrocytes in clinical practice. Nephron Clin Pract 2010;115:c203–12. https://doi.org/10.1159/000313037.Search in Google Scholar
23. Soares, MFS, Roberts, ISD. Histologic classification of IgA nephropathy: past, present, and future. Semin Nephrol 2018;38:477–84. https://doi.org/10.1016/j.semnephrol.2018.05.017.Search in Google Scholar
25. Yu, GZ, Guo, L, Dong, JF, Shi, SF, Liu, LJ, Wang, JW, et al.. Persistent hematuria and kidney disease progression in IgA nephropathy: a cohort study. Am J Kidney Dis 2020;76:90–9. https://doi.org/10.1053/j.ajkd.2019.11.008.Search in Google Scholar PubMed
26. Martínez-Martínez, MU, Llamazares-Azuara, LM, Martínez-Galla, D, Mandeville, PB, Valadez-Castillo, F, Román-Acosta, S, et al.. Urinary sediment suggests lupus nephritis histology. Lupus 2017;26:580–7. https://doi.org/10.1177/0961203316669241.Search in Google Scholar PubMed
28. Duan, ZY, Cai, GY, Li, JJ, Bu, R, Chen, XM. Urinary erythrocyte-derived miRNAs: emerging role in IgA nephropathy. Kidney Blood Press Res 2017;42:738–48. https://doi.org/10.1159/000481970.Search in Google Scholar PubMed
29. Dolci, A, Giavarina, D, Pasqualetti, S, Szoke, D, Panteghini, M. Total laboratory automation: do stat tests still matter? Clin Biochem 2017;50:605–11. https://doi.org/10.1016/j.clinbiochem.2017.04.002.Search in Google Scholar PubMed
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