Elderly patients with primary membranous nephropathy (PMN) exhibit significant prognostic heterogeneity and poor tolerance to immunotherapy. However, there is a lack of early prognostic prediction tools specifically for this population. This study aimed to develop a prognostic prediction model applicable to elderly PMN patients.

This study retrospectively included elderly patients with PMN confirmed by renal biopsy. The primary endpoint was a adverse composite outcome including end-stage renal disease (ESRD), a ≥50% decline in estimated glomerular filtration rate (eGFR), or all-cause death. Patients were randomly divided into a training cohort and a validation cohort at a ratio of 7∶3. Key prognostic features were identified using least absolute shrinkage and selection operator (LASSO) regression combined with random survival forest, and a predictive model was constructed based on penalized Cox regression. Model performance was evaluated using the concordance index (C-index), time-dependent area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis. The SurvSHAP (t) method was employed for interpretability analysis of the model.

A total of 309 elderly patients with PMN were included in this study, with a median age of 65.00 years (IQR, 62.00-68.00) and a male predominance 61.2%(189/309).During a median follow-up of 47.00 months (IQR, 25.00-89.00), 38.2%(118/309) reached the endpoint event. The final model included nine key features, including eGFR, total protein (TP), glomerular capsular adhesion, urine glucose, segmental glomerulosclerosis proportion, fibrinogen, urea, age, and activated partial thromboplastin time (APTT). In the validation cohort, the model demonstrated good discrimination, with a C-index of 0.731(95% CI: 0.652-0.797). The time-dependent AUROCs for predicting adverse outcomes at 3, 5, and 10 years were 0.758(95% CI: 0.614-0.901), 0.781(95% CI: 0.646-0.916), and 0.866(95% CI: 0.740-0.993), respectively. Calibration curves demonstrated a high degree of concordance between predicted probabilities and actual event rates. Decision curve analysis confirmed the net clinical benefit of the model.SurvSHAP (t) analysis showed that eGFR, TP, glomerular capsular adhesion, urine glucose, and the proportion of segmental glomerular sclerosis were the top five variables contributing to the model.

This prognostic model effectively predicts the risk of adverse outcomes in elderly patients with PMN in the internal validation cohort, offering a potential scientific basis for individualized risk stratification and treatment decision-making in this population.