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Updated: Aug 14, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram predicting one-year mortality in patients undergoing percutaneous coronary
Jing-Jing Song1, Yu-Peng Liu2, Wen-Yao Wang3
1Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
This study developed a nomogram to predict one-year mortality risk in Asian patients after percutaneous coronary intervention (PCI). The tool helps clinicians assess patient risk for better decision-making.
Area of Science:
- Cardiology
- Medical Statistics
Background:
- Percutaneous coronary intervention (PCI) is a common procedure for coronary artery disease.
- Accurate prediction of post-PCI mortality is crucial for patient management.
- Existing risk stratification tools may not be optimized for Asian populations.
Purpose of the Study:
- To develop and validate a nomogram for predicting one-year all-cause mortality risk in Asian patients undergoing PCI.
- To provide a tool for clinicians to make risk-dependent treatment decisions.
Main Methods:
- A large-scale cohort of 9603 patients undergoing PCI in China was analyzed.
- Least absolute shrinkage and selection operator (LASSO) Cox regression and backward stepwise regression were used to identify risk factors.
- A nomogram was constructed using selected predictors and validated internally.
Main Results:
- Six variables were identified: age, renal insufficiency, cardiac dysfunction, previous cerebrovascular disease, previous PCI, and TIMI 0-1 flow.
- The nomogram demonstrated good predictive performance with an Area Under the Curve (AUC) of 0.792 in the derivation cohort and 0.754 in the validation cohort.
- The nomogram effectively stratified patients into low-, intermediate-, and high-risk groups.
Conclusions:
- A simple, validated nomogram for predicting one-year mortality after PCI in Asian patients was developed.
- This tool can assist clinicians in making informed, risk-stratified decisions for patients undergoing PCI.
Objective:
To formulate a nomogram to predict the risk of one-year mortality after percutaneous coronary intervention (PCI) based on a large-scale real-world Asian cohort.
Methods:
This study cohort included consecutive patients undergoing PCI in the National Center for Cardiovascular Diseases of China. The endpoint was all-cause mortality. Least absolute shrinkage and selection operator Cox regression and backward stepwise regression were used to select potential risk factors. A nomogram based on the predictors was accordingly constructed to predict one-year mortality. The performance of the nomogram was evaluated. Patients were stratified into low-, intermediate- and high-risk groups according to the tertile points in the nomogram and compared by the Kaplan-Meier analysis.
Results:
A total of 9603 individuals were included in this study and randomly divided into the derivation cohort (60%) and the validation cohort (40%). Six variables were selected to formulate the nomogram, including age, renal insufficiency, cardiac dysfunction, previous cerebrovascular disease, previous PCI, and TIMI 0-1 before PCI. The area under the curve of this nomogram regarding one-year mortality risks were 0.792 and 0.754 in the derivation cohort and validation cohort, respectively. Kaplan-Meier curve successfully stratified the patients according to three risk groups. This nomogram calibrated well and exhibited satisfactory clinical utility in the decision curve analysis.
Conclusions:
This study developed and validated a simple-to-use nomogram predicting one-year mortality risk in Asian patients undergoing PCI and could help clinicians make risk-dependent decisions.
