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Updated: May 9, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting advanced chronic kidney disease
Shuyu Huang1, Lun Liu2, Shuhang Huang3
1Department of Blood Transfusion, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Iscience
|May 8, 2026
Summary
A new nomogram accurately predicts advanced chronic kidney disease (CKD) risk using routine clinical data. This tool aids in personalized management strategies for kidney patients.
Area of Science:
- Nephrology
- Medical Informatics
Background:
- Chronic kidney disease (CKD) poses a significant global health burden.
- Accurate prediction of advanced CKD is crucial for timely intervention and management.
Purpose of the Study:
- To develop and validate a nomogram for predicting advanced CKD.
- To utilize routine clinical parameters for risk stratification.
Main Methods:
- Integrated internal and external datasets.
- Employed LASSO and logistic regression for predictor identification.
- Conducted Bootstrap validation and evaluated discrimination, calibration, and net clinical benefit.
Main Results:
- Identified six independent predictors: serum creatinine, age, hematocrit, hypertension, serum ALT, and serum LDH.
- The nomogram demonstrated excellent discrimination (AUC: 0.961-0.939) and good calibration (Brier score: 0.058-0.073).
- Achieved significant net clinical benefit.
Conclusions:
- The developed nomogram is a robust and clinically practical tool for predicting advanced CKD risk.
- Facilitates accurate, individualized predictions to support precision kidney disease management.
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