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Updated: Aug 2, 2025
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular
Peter D Farjo1, Naveena Yanamala1,2, Nobuyuki Kagiyama1,3,4
1Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA.
Insights
Machine learning models accurately predict coronary artery calcium score categories using electrocardiograms and clinical data. This approach aids cardiovascular risk stratification without radiation exposure.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Coronary artery calcium (CAC) scoring is vital for cardiovascular risk stratification but faces limitations in availability and radiation concerns.
- Current methods necessitate specialized imaging, hindering widespread clinical adoption.
Purpose of the Study:
- To investigate the efficacy of machine learning (ML) in predicting CAC score categories using only clinical features and surface electrocardiograms (ECGs).
- To assess ML's potential to overcome CAC limitations for improved cardiovascular risk assessment.
Main Methods:
- A substudy of 534 subjects utilized ECG and clinical data, split into training (80%) and testing (20%) sets.
- Two binary logistic regression ML models were developed to predict CAC scores of 0 and ≥400.
- The CAC ≥400 model was further validated in a cohort of 87 subjects undergoing coronary angiography.
Main Results:
- The ML models achieved high performance, with Area Under the Curve (AUC) values of 84% for CAC=0 and 87% for CAC≥400.
- Predictive accuracy for CAC=0 was 75% (sensitivity 92%, specificity 70%), and for CAC≥400 was 81% (sensitivity 91%, specificity 75%).
- In the validation cohort, the CAC ≥400 model successfully predicted significant coronary artery stenosis, need for revascularization, and major adverse cardiovascular events over 2 years.
Conclusions:
- Machine learning models can effectively predict CAC score categories from readily available clinical information and ECGs.
- This ML-driven approach offers a promising, radiation-free alternative for cardiovascular risk stratification in patients with suspected coronary artery disease.
Aims:
Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.
Methods And Results:
In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.
Conclusion:
ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
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