Abstract
INTRODUCTION: Rituximab (RTX) has been shown to be effective in inducing immunological remission in patients with membranous nephropathy (MN). Some patients required more than one course of RTX to achieve immunological remission. Identifying patients who need more courses of RTX to achieve immunological remission is beneficial for better physician-patient communication, the assessment of treatment course, and the evaluation of medical costs. This study aims to establish a practical model to predict the probability of immunological remission after receiving one cycle of RTX. METHODS: This study enrolled 106 patients from the First Affiliated Hospital of Zhengzhou University in the modeling group and 30 patients from Henan Provincial Hospital of Traditional Chinese Medicine in the external validation group. Patients in the modeling group were divided into responders or nonresponders according to whether they achieved immunological remission or not after following up for 6 months. A nomogram was established based on the results of logistic regression analysis. The predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCAs). RESULTS: In the modeling group, 75 (70.8%) patients achieved immunological remission within 6 months after receiving one cycle of RTX. Significant differences were observed between nonresponders and responders. Risk factors used in nomogram included PLA2R antibody, hemoglobin, and gender. The AUC value of nomogram was 0.797 (95% CI 0.701-0.894, P<0.001). The calibration curves demonstrated acceptable agreement between the predicted outcomes by the nomogram and the actual values. DCA curves showed good positive net benefits in the predictive model. The external validation also demonstrated the reliability of the prediction nomogram. CONCLUSION: A predictive nomogram including PLA2R antibody, hemoglobin, and gender may provide a basis to predict the doses of RTX needed in MN patients.