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Updated: Aug 31, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram to predict kidney survival at baseline in patients with C3 glomerulopathy
Fernando Caravaca-Fontán1, Marta Rivero2, Teresa Cavero2
1Instituto de Investigación Hospital 12 de Octubre, Madrid, Spain.
Insights
This study developed a new tool to predict kidney survival in C3 glomerulopathy patients. The prognostic nomogram accurately forecasts the risk of kidney failure, aiding clinical decision-making for this rare complement-driven disease.
Area of Science:
- Nephrology
- Complement System Biology
- Biostatistics
Background:
- C3 glomerulopathy is a rare, heterogeneous, complement-driven kidney disease.
- Predicting individual kidney prognosis in C3 glomerulopathy is clinically challenging.
- Accurate baseline prognosis is crucial for managing long-term kidney survival.
Purpose of the Study:
- To develop and validate a prognostic nomogram for predicting long-term kidney survival in C3 glomerulopathy patients.
- To identify key predictors of kidney outcome in this patient cohort.
- To provide a tool for improved clinical risk assessment.
Main Methods:
- Retrospective, multicenter observational cohort study involving 115 C3 glomerulopathy patients.
- Dataset split into training (n=87) and validation (n=28) groups.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression used to build a nomogram based on eGFR, proteinuria, and biopsy chronicity score.
Main Results:
- 40% of patients reached kidney failure within a median follow-up of 49 months.
- The developed nomogram included eGFR, proteinuria, and total chronicity score as predictors.
- The nomogram demonstrated strong predictive accuracy with a C-index of 0.860 and excellent calibration.
Conclusions:
- A practical and validated nomogram can accurately predict kidney failure risk in C3 glomerulopathy patients.
- The nomogram provides reliable predictions at 1, 2, 5, and 10 years post-diagnosis.
- This tool can significantly aid clinicians in assessing long-term prognosis and guiding patient management.
Background:
C3 glomerulopathy is a rare and heterogeneous complement-driven disease. It is often challenging to accurately predict in clinical practice the individual kidney prognosis at baseline. We herein sought to develop and validate a prognostic nomogram to predict long-term kidney survival.
Methods:
We conducted a retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases. The dataset was randomly divided into a training group (n = 87) and a validation group (n = 28). The least absolute shrinkage and selection operator (LASSO) regression was used to screen the main predictors of kidney outcome and to build the nomogram. The accuracy of the nomogram was assessed by discrimination and risk calibration in the training and validation sets.
Results:
The study group comprised 115 patients, of whom 46 (40%) reached kidney failure in a median follow-up of 49 months (range 24-112). No significant differences were observed in baseline estimated glomerular filtration rate (eGFR), proteinuria or total chronicity score of kidney biopsies, between patients in the training versus those in the validation set. The selected variables by LASSO were eGFR, proteinuria and total chronicity score. Based on a Cox model, a nomogram was developed for the prediction of kidney survival at 1, 2, 5 and 10 years from diagnosis. The C-index of the nomogram was 0.860 (95% confidence interval 0.834-0.887) and calibration plots showed optimal agreement between predicted and observed outcomes.
Conclusions:
We constructed and validated a practical nomogram with good discrimination and calibration to predict the risk of kidney failure in C3 glomerulopathy patients at 1, 2, 5 and 10 years.
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