A nomogram prediction model for treatment failure in primary membranous nephropathy
Related Experiment Video
Updated: Jul 14, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Chanyu Geng1, Liming Huang1, Qiang Li2
1Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Primary membranous nephropathy (PMN) has a heterogeneous natural course. Immunosuppressive therapy is recommended for PMN patients at moderate or high risk of renal function deterioration. Prediction models for the treatment failure of PMN have rarely been reported.
Methods:
This study retrospectively studied patients diagnosed as PMN by renal biopsy at Sichuan Provincial People's Hospital from January 2017 to December 2020. Information on clinical characteristics, laboratory test results, pathological examination, and treatment was collected. The outcome was treatment failure, defined as the lack of complete or partial remission at the end of 12 months. Simple logistic regression was used to identify candidate predictive variables. Forced-entry stepwise multivariable logistic regression was used to develop the prediction model, and performance was evaluated using C-statistic, calibration plot, and decision curve analysis. Internal validation was performed by bootstrapping.
Results:
In total, 310 patients were recruited for this study. 116 patients achieved the outcome. Forced-entry stepwise multivariable logistic regression indicated that PLA2Rab titer (OR = 1.002, 95% CI: 1.001-1.004, p = 0.003), inflammatory cells infiltration (OR = 2.753, 95% CI: 1.468-5.370, p = 0.002) and C3 deposition on immunofluorescence (OR = 0.217, 95% CI: 0.041-0.964, p = 0.049) were the three independent risk factors for treatment failure of PMN. The final prediction model had a C-statistic (95% CI) of 0.653 (0.590-0.717) and a net benefit of 23%-77%.
Conclusions:
PLA2R antibody, renal interstitial inflammation infiltration, and C3 deposition on immunofluorescence were the three independent risk factors for treatment failure in PMN. Our prediction model might help identify patients at risk of treatment failure; however, the performance awaits improvement.
Insights
High PLA2R antibody levels, inflammatory cell infiltration, and C3 deposition predict treatment failure in primary membranous nephropathy (PMN). This model aids in identifying at-risk patients for improved management strategies.
Area of Science:
- Nephrology
- Immunology
- Internal Medicine
Background:
- Primary membranous nephropathy (PMN) exhibits variable clinical courses.
- Immunosuppressive therapy is crucial for PMN patients at risk of kidney function decline.
- Predictive models for PMN treatment failure are scarce.
Purpose of the Study:
- To develop and validate a prediction model for treatment failure in primary membranous nephropathy.
- To identify independent risk factors associated with treatment failure in PMN patients.
Main Methods:
- Retrospective analysis of 310 PMN patients diagnosed via renal biopsy.
- Logistic regression analysis to identify predictive variables for treatment failure (defined as no remission at 12 months).
- Model performance assessed using C-statistic, calibration plots, and decision curve analysis, with internal validation via bootstrapping.
Main Results:
- Three independent risk factors for PMN treatment failure were identified: PLA2R antibody titer (OR=1.002), inflammatory cell infiltration (OR=2.753), and C3 deposition on immunofluorescence (OR=0.217).
- The prediction model achieved a C-statistic of 0.653 (95% CI: 0.590-0.717).
- Decision curve analysis indicated a net benefit ranging from 23% to 77%.
Conclusions:
- PLA2R antibody levels, renal interstitial inflammation, and C3 deposition are key predictors of treatment failure in PMN.
- The developed prediction model shows potential for identifying PMN patients at risk of treatment failure.
- Further refinement is needed to enhance the model's predictive performance.
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