{Reference Type}: Journal Article
{Title}: A Dynamic Prediction Model for Renal Progression in Primary Membranous Nephropathy.
{Author}: Liang Y;Li Q;Zou Z;Huang B;Zhong N;Li C;Wang A;Chen Y;Tu S;Wan J;
{Journal}: Int J Med Sci
{Volume}: 21
{Issue}: 7
{Year}: 2024
{Factor}: 3.642
{DOI}: 10.7150/ijms.95321
{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.