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Abstract：
Objective: This study aimed to build and validate a practical web-based dynamic prediction model for predicting renal progression in patients with primary membranous nephropathy (PMN). Method: A total of 359 PMN patients from The First Affiliated Hospital of Fujian Medical University and 102 patients with PMN from The Second Hospital of Longyan between January 2018 to December 2023 were included in the derivation and validation cohorts, respectively. Renal progression was delineated as a decrease in eGFR of 30% or more from the baseline measurement at biopsy or the onset of End-Stage Renal Disease (ESRD). Multivariable Cox regression analysis was employed to identify independent prognostic factors. A web-based dynamic prediction model for renal progression was built and validated, and the performance was assessed using. An analysis of the receiver operating characteristic and the decision curve analysis. Results: In the derivation cohort, 66 (18.3%) patients experienced renal progression during the follow-up period (37.60 ± 7.95 months). The final prediction rule for renal progression included hyperuricemia (HR=2.20, 95%CI 1.26 to 3.86), proteinuria (HR=2.16, 95%CI 1.47 to 3.18), significantly lower serum albumin (HR=2.34, 95%CI 1.51 to 3.68) and eGFR (HR=1.96, 95%CI 1.47 to 2.61), older age (HR=1.85, 95%CI 1.28 to 2.61), and higher sPLA2R-ab levels (HR=2.08, 95%CI 1.43 to 3.18). Scores for each variable were calculated using the regression coefficients in the Cox model. The developed web-based dynamic prediction model, available online at http://imnpredictmodel1.shinyapps.io/dynnomapp, showed good discrimination (C-statistic = 0.72) and calibration (Brier score, P = 0.155) in the validation cohort. Conclusion: We developed a web-based dynamic prediction model that can predict renal progression in patients with PMN. It may serve as a helpful tool for clinicians to identify high-risk PMN patients and tailor appropriate treatment and surveillance strategies.
摘要：
目的:建立并验证基于网络的原发性膜性肾病(PMN)肾脏进展预测模型。方法:将2018年1月至2023年12月福建医科大学附属第一医院的359例PMN患者和龙岩市第二医院的102例PMN患者纳入推导和验证队列,分别。肾脏进展描述为在活检或终末期肾病（ESRD）发作时eGFR从基线测量值降低30％或更多。采用多变量Cox回归分析确定独立的预后因素。建立并验证了基于Web的肾脏进展动态预测模型,并使用评估性能。接收机工作特性分析及决策曲线分析结果：在派生队列中，66例(18.3%)患者在随访期间(37.60±7.95个月)出现肾脏进展。肾脏进展的最终预测规则包括高尿酸血症（HR=2.20，95CI1.26至3.86），蛋白尿（HR=2.16，95CI1.47至3.18），显著降低血清白蛋白（HR=2.34，95CI1.51至3.68）和eGFR（HR=1.96，95CI1.47至2.61），年龄较大（HR=1.85，95CI1.28至2.61），和更高的sPLA2R-ab水平（HR=2.08，95CI1.43至3.18）。使用Cox模型中的回归系数计算每个变量的得分。所开发的基于Web的动态预测模型,可在http://imnpredictmodel1在线获取。shinyapps.io/dynnomapp,显示出良好的辨别力(C统计量=0.72)和校准(Brier评分，P=0.155)在验证队列中。结论：我们开发了一种基于网络的动态预测模型，可以预测PMN患者的肾脏进展。它可以作为临床医生识别高危PMN患者并制定适当的治疗和监测策略的有用工具。
