Department of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Department of Nephrology, The Second Hospital of Longyan, Fujian 364000, China.
Int J Med Sci. 2024 May 13;21(7):1292-1301. doi: 10.7150/ijms.95321. eCollection 2024.
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). 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. 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. 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.
本研究旨在建立和验证一种实用的基于网络的动态预测模型，用于预测原发性膜性肾病（PMN）患者的肾脏进展。共纳入 2018 年 1 月至 2023 年 12 月期间来自福建医科大学附属第一医院的 359 例 PMN 患者和来自龙岩第二医院的 102 例 PMN 患者分别纳入推导队列和验证队列。肾脏进展定义为活检或终末期肾病（ESRD）发病时肾小球滤过率（eGFR）从基线测量值下降 30%或更多。采用多变量 Cox 回归分析确定独立的预后因素。建立并验证了基于网络的肾脏进展动态预测模型，并通过. 进行了性能评估。分析了接收者操作特征和决策曲线分析。在推导队列中，66 例（18.3%）患者在随访期间发生肾脏进展（37.60±7.95 个月）。肾脏进展的最终预测规则包括高尿酸血症（HR=2.20，95%CI 1.26 至 3.86）、蛋白尿（HR=2.16，95%CI 1.47 至 3.18）、血清白蛋白显著降低（HR=2.34，95%CI 1.51 至 3.68）和 eGFR（HR=1.96，95%CI 1.47 至 2.61）、年龄较大（HR=1.85，95%CI 1.28 至 2.61）和更高的 sPLA2R-ab 水平（HR=2.08，95%CI 1.43 至 3.18）。使用 Cox 模型中的回归系数计算每个变量的得分。开发的基于网络的动态预测模型可在线获取，网址为 http://imnpredictmodel1.shinyapps.io/dynnomapp，在验证队列中显示出良好的区分度（C 统计量=0.72）和校准度（Brier 评分，P=0.155）。我们开发了一种基于网络的动态预测模型，可预测 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。
