Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Int Urol Nephrol. 2022 Dec;54(12):3211-3219. doi: 10.1007/s11255-022-03254-w. Epub 2022 Jul 1.
The purpose of this study is to build a prediction model for accurate assessment of the risk of end-stage kidney disease (ESKD) in individuals with primary focal segmental glomerulosclerosis (FSGS) by integrating clinical and pathological features at biopsy. The prediction model was created based on a retrospective study of 99 patients with biopsy-proven primary FSGS diagnosed at our hospital between December 2012 and December 2019. We assessed discriminative ability and predictive accuracy of the model by C-index and calibration plot. Internal validation of the prediction model was performed with 1000-bootstrap procedure. Eight patients (8.1%) progressed to ESKD before 31 March 2021. Univariate analysis revealed that disease duration before biopsy, hematuria, hemoglobin, eGFR, and percentages of sclerosis and global sclerosis were associated with renal outcome. In multivariate analysis, three predictors were included in final prediction model: eGFR, hematuria, and percentage of sclerosis. The C-index of the model was 0.811 and 5-year calibration plot showed good agreement between predicted renal survival probability and actual observation. A nomogram and an online risk calculator were built on the basis of the prediction model. In conclusion, we constructed and internally validated the first prediction model for risk of ESKD in primary FSGS, which showed good discriminative ability and calibration performance. The prediction model provides an accurate and simple strategy to predict renal prognosis which may help to identify patients at high risk of ESKD and guide the management for patients with primary FSGS in clinical practice.
本研究旨在通过整合活检时的临床和病理特征，为原发性局灶节段性肾小球硬化症（FSGS）患者建立一种准确评估终末期肾病（ESKD）风险的预测模型。该预测模型是基于对 2012 年 12 月至 2019 年 12 月在我院诊断为原发性 FSGS 的 99 例经活检证实的患者的回顾性研究建立的。我们通过 C 指数和校准图评估了模型的判别能力和预测准确性。使用 1000 次引导程序对预测模型进行了内部验证。截至 2021 年 3 月 31 日，有 8 例（8.1%）患者进展为 ESKD。单因素分析显示，活检前的病程、血尿、血红蛋白、eGFR 以及硬化和全球硬化的百分比与肾脏结局有关。多因素分析中，最终预测模型纳入了 3 个预测因素：eGFR、血尿和硬化百分比。模型的 C 指数为 0.811，5 年校准图显示预测的肾脏生存概率与实际观察之间具有良好的一致性。在此基础上建立了列线图和在线风险计算器。总之，我们构建并内部验证了首个原发性 FSGS 患者 ESKD 风险的预测模型，该模型具有良好的判别能力和校准性能。该预测模型提供了一种准确且简单的策略来预测肾脏预后，这可能有助于识别 ESKD 风险较高的患者，并指导原发性 FSGS 患者的临床管理。
