Prediction model for treatment response of primary membranous nephropathy with nephrotic syndrome.

Objective: To investigate the predictors and establish a nomogram model for the prediction of the response to treatment in primary membranous nephropathy (PMN) with nephrotic syndrome (NS).

Methods: The clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy at the Affiliated Hospital of Qingdao University were collected. A total of 373 patients were randomly assigned into development group (n = 262) and validation group (n = 111). Logistic regression analysis was performed in the development group to determine the predictors of treatment response. A nomogram model was established based on the multivariate logistic regression analysis and validated in the validation group. The C-index and calibration plots were used for the evaluation of the discrimination and calibration performance, respectively.

Results: Serum albumin levels (OR = 1.151, 95% CI 1.078-1.229, P < 0.001) and glomerular C3 deposition (OR = 0.407, 95% CI 0.213-0.775, P = 0.004) were identified as independent predictive factors for treatment response in PMN with NS, then a nomogram was established combining the above indicators and treatment regimen. The C-indices of this model were 0.718 (95% CI 0.654-0.782) and 0.789 (95% CI 0.705-0.873) in the development and validation groups, respectively. The calibration plots showed that the predicted probabilities of the model were consistent with the actual probabilities (P > 0.05), which indicated favorable performance of this model in predicting the treatment response probability.

Conclusions: Serum albumin levels and glomerular C3 deposition were predictors for treatment response of PMN with NS. A novel nomogram model with good discrimination and calibration was constructed to predict treatment response probability at an early stage.