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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">MRP</journal-id><journal-title-group><journal-title>Medical Research and Practice</journal-title></journal-title-group><issn>2993-9690</issn><eissn>2993-9704</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/MRP.2026090013</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于肾活检库IgA 肾病高危病理无创预测模型</title><url>https://artdesignp.com/journal/MRP/4/9/10.61369/MRP.2026090013</url><author>阿卜力孜穆妮热,韩素英,艾尔肯图尔逊姑丽,周姗姗</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>9</issue><history><date date-type="pub"><published-time>2026-09-20</published-time></date></history><abstract>目的：构建IgA 肾病高危活动性病理术前无创预测模型，分析肾活检数据库临床病理特征。方法：回顾2021&amp;mdash;2024年肾活检资料，352 例患者用于整体特征分析；79例IgA 肾病以Oxford 分型C &amp;ge; 1和/ 或E1定义高危病理终点，经单、多因素Logistic 回归建模，采用ROC、Bootstrap、Brier 评分、Hosmer-Lemeshow 检验评估模型效能。结果：352例不同病理亚组年龄、24h 尿蛋白、eGFR 等指标差异有统计学意义；79例IgA 肾病高危组54例，收缩压、24h尿蛋白更高，白蛋白、C3更低。收缩压、24h 尿蛋白、C3为独立影响因素；模型AUC=0.792，校正AUC=0.758，拟合良好。结论：肾活检数据库可体现术前临床异质性；该三项指标构建的无创模型区分度尚可，可于肾活检前识别高危病理风险。</abstract><keywords>肾活检数据库,IgA 肾病,高危活动性病理,无创预测模型</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Lai KN, Tang SCW, Schena FP, Novak J, Tomino Y, Fogo AB, Glassock RJ.IgA nephropathy. Nat Rev Dis Primers. 2016;2:16001.DOI: https://doi.org/10.1038/nr dp.2016.1[2] Working Group of the International IgA Nephropathy Network and the Renal Pathology Society; Cattran DC, Coppo R, Cook HT, Feehally J, Roberts ISD, TroyanovS, et al. The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification. Kidney Int. 2009;76(5):534-545.DOI: https://doi.org/10.1038/ki.2009.243[3] Trimarchi H, Barratt J, Cattran DC, Cook HT, Coppo R, Haas M, Liu ZH, et al. Oxford Classification of IgA nephropathy 2016: an update from the IgA Nephropathy Classification Working Group. Kidney Int. 2017;91(5):1014-1021.DOI: https://doi.org/10.1016/j.kint.2017.02.003[4] Barbour SJ, Coppo R, Zhang H, Liu ZH, Suzuki Y, Matsuzaki K, et al. Evaluating a New International Risk-Prediction Tool in IgA Nephropathy. JAMA Intern Med. 2019;179(7):942-952.DOI: https://doi.org/10.1001/jamainternmed.2019.0600[5] 刘春花, 杨定平. 伴不同比例新月体IgA 肾病患者的临床病理特征及预后分析[J]. 临床肾脏病杂志,2024,24(1):51-60.DOI:10.3969/j.issn.1671-2390.2024.01.008.[6] Poggio ED, McClelland RL, Blank KN, Hansen S, Bansal S, Bomback AS, et al. Systematic Review and Meta-Analysis of Native Kidney Biopsy Complications. Clin J Am Soc Nephrol. 2020;15(11):1595-1602.DOI: https://doi.org/10.2215/CJN.04710420.[7] Stamellou E, Seikrit C, Tang SCW, Boor P, Tesař V, Floege J, Barratt J, Kramann R. IgA nephropathy. Nat Rev Dis Primers. 2023;9(1):67.DOI: https://doi. org/10.1038/s415 72-023- 00476-9.[8] 彭思琪, 卢文, 江肖, 徐兴欣, 吴永贵.742例原发性IgA 肾病患者的临床病理特征及其预后分析[J]. 中华肾脏病杂志,2021,37(2):87-94.DOI:10.3760/cma.j.cn441217-20200407-00016.[9] Tang C, Chen P, Si FL, Lv JC, Shi SF, Zhou XJ, Liu LJ, Zhang H. Time-Varying Proteinuria and Progression of IgA Nephropathy: A Cohort Study. Am J Kidney Dis. 2024;84(2):170-178.e1.DOI: https://doi.org/10.1053/j.ajkd.2023.12.01</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
