A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

Abstract

Objective

To explore the predictive factors and establish a nomogram model for predicting relapse risk in primary membranous nephropathy (PMN).

Methods

The clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy were collected in the Affiliated Hospital of Qingdao University. A total of 400 PMN patients who achieved remission were assigned to the development group (n = 280) and validation group (n = 120) randomly. Cox regression analysis was performed in the development cohort to determine the predictive factors of relapse in PMN patients, a nomogram model was established based on the multivariate Cox regression analysis and validated in the validation group. C-index and calibration plots were used to evaluate the discrimination and calibration performance of the model respectively.

Result

Hyperuricemia (HR = 2.938, 95% CI 1.875–4.605, p < 0.001), high C-reactive protein (CRP) (HR = 1.147, 95% CI 1.086–1.211, p < 0.001), and treatment with calcineurin inhibitors with or without glucocorticoids (HR = 2.845, 95%CI 1.361–5.946, p = 0.005) were independent risk factors, while complete remission (HR = 0.420, 95%CI 0.270–0.655, p < 0.001) was a protective factor for relapse of PMN according to multivariate Cox regression analysis, then a nomogram model for predicting relapse of PMN was established combining the above indicators. The C-indices of this model were 0.777 (95%CI 0.729–0.825) and 0.778 (95%CI 0.704–0.853) in the development group and validation group respectively. The calibration plots showed that the predicted relapse probabilities of the model were consistent with the actual probabilities at 1, 2 and 3 years, which indicated favorable performance of this model in predicting the relapse probability of PMN.

Conclusions

Hyperuricemia, remission status, CRP and therapeutic regimen were predictive factors for relapse of PMN. A novel nomogram model with good discrimination and calibration was constructed to predict relapse risk in patients with PMN early.

Keywords:

• Primary membranous nephropathy
• relapse
• predictive factors
• nomogram model

Author contributions

Min Li: design of research, collection and analysis of data, and drafting of the manuscript. Huifang Wang and Xiaoying Lai: drafting and revision of the manuscript. Zixuan Fu and Chunhui Jiang: collection and collation of the data. Xuemei Liu: conception and design of the study and critical revision of the paper. All authors contributed to the article and approved the submitted version.

Ethics statement

This study was conducted according to the principles of the Declaration of Helsinki; informed consent was obtained from all subjects. This study was approved by the Medical Ethics Committee of the Affiliated Hospital of Qingdao University (the ethics approval number is QYFY WZLL 27505).

Disclosure statement of interest

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported by the Project of Science and Technology of Qingdao People’s Livelihood under Grant number 19-6-1-18-nsh.