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Machine Learning Model for Atherosclerosis Evaluation and Cardiovascular Risk Prediction Based on Coronary CT
Ying Song1, Na Xu1, Jianan Zheng2
1Department of Cardiology (Y.S., N.X., C.C., Y.Z., L.G., Z.G., J.C., L.S., J.Y.), Fuwai Hospital, National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
A new machine learning model using coronary computed tomography angiography (CCTA) significantly improves prediction of major adverse cardiac events compared to traditional methods. This advanced CCTA model offers better risk assessment for atherosclerotic cardiovascular disease.
Area of Science:
- Cardiovascular Imaging
- Machine Learning in Medicine
- Predictive Analytics
Background:
- Traditional risk prediction tools for atherosclerotic cardiovascular disease (ASCVD) have limitations.
- Coronary artery calcium (CAC) scoring is a common risk assessment tool but has limitations.
Purpose of the Study:
- To develop and validate a machine learning model using coronary computed tomography angiography (CCTA) data for improved ASCVD risk prediction.
- To compare the performance of the CCTA-based machine learning model against traditional risk factors and CAC scoring.
Main Methods:
- The CREATION study included 8431 patients with suspected coronary artery disease undergoing CCTA.
- Six machine learning survival models were trained using 48 CCTA parameters; XGBoost was selected for model development.
- The primary outcome was major adverse cardiac events (MACE): all-cause death, myocardial infarction, revascularization, or stroke.
Main Results:
- The XGBoost CCTA model significantly outperformed traditional risk factors and CAC scoring in both training (AUC 0.903 vs 0.830) and testing (AUC 0.899 vs 0.753) cohorts.
- Key predictors included diameter stenosis, lipid plaque burden, total plaque volume, high-risk plaque, and vessel volume.
- Lipid plaque burden showed a strong association with MACE (aHR 2.524 per 5% increase).
Conclusions:
- A machine learning model integrating CCTA plaque quantification, characterization, and stenosis assessment significantly enhances MACE prediction.
- This CCTA-based model provides direct visualization of coronary atherosclerosis and outperforms current clinical practice models.
- The findings highlight the potential of advanced CCTA analysis for more accurate cardiovascular risk stratification.
Background:
Current atherosclerotic cardiovascular disease risk prediction tools based on traditional risk factors and the coronary artery calcium score have limitations.
Methods:
The CREATION study includes patients with suspected coronary artery disease who underwent coronary computed tomography angiography (CCTA) at Fuwai Hospital between 2016 and 2019. The primary outcome was major adverse cardiac events defined as a composite end point of all-cause death, acute myocardial infarction, coronary revascularization, or stroke. Six machine learning survival models were used to create an atherosclerotic cardiovascular disease prediction model.
Results:
Overall, 8431 participants with analyzable CCTA data were included with a median follow-up of 3.68 years, and 319 major adverse cardiac events (3.8%) occurred (mean age: 54.73±10.21 years, 48.2% were male, 50.9% with symptomatic chest pain). Among 6 machine learning models trained with 48 CCTA parameters, XGBoost showed the best performance and was selected for model development. In the training cohort (n=5901, 70%), the XGBoost model significantly outperformed the clinical risk factors and coronary artery calcium score model (area under the curve, 0.903 versus 0.830; P<0.001). Testing cohort showed similar performance (area under the curve, 0.899 versus 0.753; P<0.001). The CCTA model demonstrates consistent predictive performance across sex (female or male), onset-age (early onset or late-onset), and symptom (asymptomatic or symptomatic) subgroup analysis. The final CCTA model included diameter stenosis, lipid plaque burden and volume, total plaque volume, high-risk plaque, and vessel volume as the most important features. Lipid plaque burden was most strongly associated with major adverse cardiac event (adjusted hazard ratio per 5% increase: 2.524 [95% CI, 2.157-2.996]; P<0.001). The incremental value of machine learning CCTA features was consistent across different time points throughout the 1- to 5-year follow-up period. The findings remained unchanged when restricted to a secondary composite end point (death, myocardial infarction, or stroke).
Conclusions:
The machine learning model incorporating CCTA plaque quantification, characterization, and stenosis assessment significantly enhanced the predictive capacity for major adverse cardiac events. It provides direct visualization of coronary atherosclerosis and outperforms the traditional risk factors and the coronary artery calcium score model recommended in clinical practice.
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