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Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths
Rine Nakanishi1, Piotr J Slomka2, Richard Rios3
1Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan; Los Angeles BioMedical Research Institute at Harbor UCLA Medical Center, Torrance, California, USA.
Insights
Machine learning (ML) integrating computed tomographic (CT) and clinical data significantly improves prediction of cardiovascular disease (CVD) and coronary heart disease (CHD) deaths. This comprehensive ML model outperforms traditional risk scores and CT-only models for better risk assessment.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Coronary artery calcium (CAC) scoring predicts cardiovascular disease (CVD) risk but current methods don’t integrate all available data.
- Comprehensive risk assessment requires integrating diverse clinical and imaging variables.
Purpose of the Study:
- To evaluate if machine learning (ML) using noncontrast computed tomographic (CT) and clinical variables enhances prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths.
- To compare ML model performance against traditional coronary artery calcium (CAC) Agatston scoring and clinical data.
Main Methods:
- A cohort of 66,636 asymptomatic subjects without established ASCVD was analyzed.
- An ensemble boosting ML approach incorporated 77 variables, including CAC score, plaque characteristics, and extracoronary scores.
- Model performance was assessed using 10-fold cross-validation and area under the curve (AUC), comparing against ASCVD risk score, CAC score, ML clinical, and ML CT models.
Main Results:
- The comprehensive ML model (ML all) achieved superior AUC for predicting CVD death (0.845) versus ASCVD risk (0.821), CAC score (0.781), and ML CT (0.804).
- Similarly, for CHD death prediction, ML all (0.860) outperformed ASCVD risk (0.835), CAC score (0.816), and ML CT (0.827).
- All comparisons showed statistically significant improvements (p < 0.001).
Conclusions:
- A comprehensive ML model integrating CT and clinical data significantly improves the prediction of CVD and CHD death.
- This advanced ML approach demonstrates superior prognostic utility compared to traditional risk stratification methods.
- The findings support the use of integrated ML models for more accurate cardiovascular risk assessment.
Objectives:
The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data.
Background:
The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment.
Methods:
The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT).
Results:
The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001).
Conclusions:
The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.
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