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Updated: May 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and validation of a cardiovascular risk assessment model in patients with established coronary artery
Linda Battes1, Rogier Barendse, Ewout W Steyerberg
1Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands.
Insights
Predicting cardiovascular mortality in stable coronary artery disease patients is feasible using baseline clinical data. However, accurately predicting nonfatal events remains a significant clinical challenge.
Area of Science:
- Cardiology
- Clinical Risk Prediction
- Epidemiology
Background:
- Effective risk stratification is crucial for preventing recurrent cardiovascular events in patients with stable coronary artery disease.
- The EUROPA trial database provides a robust dataset for developing and validating risk prediction models.
Purpose of the Study:
- To develop and validate risk prediction models for cardiovascular and noncardiovascular end points.
- To assess the performance of these models in a large cohort of stable coronary artery disease patients.
Main Methods:
- Utilized Cox proportional hazards models for risk model development.
- Analyzed data from 12,218 patients in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease (EUROPA) database.
- Evaluated model performance using Nagelkerke's R², time-dependent area under the receiver operating characteristic curves (AUC), and calibration plots.
Main Results:
- A prediction model for cardiovascular mortality, incorporating factors like age, smoking, diabetes, and cholesterol, demonstrated adequate performance (AUC 0.73).
- Models for nonfatal and combined end points showed considerably poorer performance (AUC ≈ 0.6).
- Baseline clinical characteristics adequately predicted cardiovascular mortality risk over the long term.
Conclusions:
- Clinical characteristics at baseline are sufficient for adequately predicting cardiovascular mortality in stable coronary artery disease patients.
- Predicting nonfatal cardiovascular outcomes, individually or combined with fatal events, presents substantial challenges.
- Further research is needed to improve the prediction of nonfatal events in this patient population.
Abstract:
Appropriate risk stratification of patients with established, stable coronary artery disease could contribute to the prevention of recurrent cardiovascular events. The purpose of the present study was to develop and validate risk prediction models for various cardiovascular end points in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease (EUROPA) database, consisting of 12,218 patients with established coronary artery disease, with a median follow-up of 4.1 years. Cox proportional hazards models were used for model development. The end points examined were cardiovascular mortality, noncardiovascular mortality, nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, resuscitated cardiac arrest, and combinations of these end points. The performance measures included Nagelkerke's R², time-dependent area under the receiver operating characteristic curves, and calibration plots. Backward selection resulted in a prediction model for cardiovascular mortality (464 events) containing age, current smoking, diabetes mellitus, total cholesterol, body mass index, previous myocardial infarction, history of congestive heart failure, peripheral vessel disease, previous revascularization, and previous stroke. The model performance was adequate for this end point, with a Nagelkerke R² of 12%, and an area under the receiver operating characteristic curve of 0.73. However, the performance of models constructed for nonfatal and combined end points was considerably worse, with an area under the receiver operating characteristic curve of about 0.6. In conclusion, in patients with established coronary artery disease, the risk of cardiovascular mortality during longer term follow-up can be adequately predicted using the clinical characteristics available at baseline. However, the prediction of nonfatal outcomes, both separately and combined with fatal outcomes, poses major challenges for clinicians and model developers.
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