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Evaluation of AI ECG age in the prediction of cardiovascular diseases and risk factors: Exploratory data analysis
Kellen Sumwiza1, Antonio H Ribeiro2, Gerard Rushingabigwi3
1African Centre of Excellence in the Internet of Things, College of Science and Technology, University of Rwanda, KN 67 Street, Kigali, Rwanda.
Insights
Artificial intelligence (AI)-predicted electrocardiogram (ECG) age is a novel biomarker for cardiovascular disease (CVD) risk. This AI-derived age improves CVD risk prediction compared to chronological age alone, enhancing personalized medicine.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomarker Discovery
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality, necessitating improved risk stratification methods.
- Current risk assessment tools may not fully capture individual disease vulnerability.
- Novel biomarkers are needed for more accurate CVD risk prediction.
Purpose of the Study:
- To evaluate the efficacy of artificial intelligence (AI)-predicted electrocardiogram (ECG) age as a novel biomarker for cardiovascular disease (CVD) risk.
- To compare the predictive performance of AI-predicted ECG age against chronological age in assessing cardiovascular outcomes.
- To explore the association of AI-predicted ECG age with specific cardiovascular conditions and sex-specific risk patterns.
Main Methods:
- Utilized the CODE-15 dataset, comprising over 344,000 ECG records with clinical annotations.
- Developed a logistic regression model incorporating AI-predicted ECG age, demographic data, and comorbidities.
- Assessed model performance using receiver operating characteristic curve area (AUROC) and correlation coefficients.
Main Results:
- The logistic regression model incorporating AI-predicted ECG age achieved a 92% AUROC for atrial fibrillation (AF) detection, outperforming chronological age (88%).
- AI-predicted ECG age demonstrated stronger correlations with AF (r=0.17), bundle branch blocks (r=0.14), and Chagas disease (r=0.074) than chronological age.
- Observed sex-specific patterns, with male sex showing increased sinus bradycardia risk (OR: 3.090) and female sex lower left bundle branch block risk (OR: 0.885) when using AI-predicted ECG age.
Conclusions:
- AI-predicted ECG age serves as a biologically meaningful indicator for cardiovascular risk assessment.
- This AI-driven approach offers a cost-effective and scalable method for CVD detection, particularly beneficial for personalized medicine.
- The findings suggest potential for improved CVD screening, especially in resource-limited settings lacking conventional biomarkers.
Abstract:
Cardiovascular diseases (CVDs) are the most widespread cause of death across the world, and this aspect requires a better risk stratification method. Effectively, this paper tests the potential of the artificial intelligence (AI)-predicted electrocardiogram (ECG) age as a novel CVD risk predictor biomarker. We used the CODE-15 % data (over 344,000 ECG records with clinical annotation) to create a logistic regression model that incorporated age predicted from ECG, as well as demographic and comorbidity variables, to evaluate cardiovascular outcomes. The model performed better with ECG-predicted age than using chronological age alone, with a 92 % receiver operating characteristic curve area (AUROC) for atrial fibrillation (AF) detection, compared to 88 % when chronological age was used alone. From the results, the ECG-predicted age showed a stronger correlation with AF (r = 0.17), bundle branch blocks (r = 0.14), and Chagas disease (r = 0.074) than chronological age. Sex-specific patterns were also observed; male sex had an increased risk of sinus bradycardia (odds ratio [OR]: 3.090), while female sex had a lower risk of having a left bundle branch block (LBBB) OR: 0.885) with the variable of ECG-predicted age. These results demonstrate how ECG-predicted age may be used as a biologically meaningful indicator for cardiovascular risk assessment. This study advances personalized medicine by offering a cost-effective and scalable method for detecting CVD, particularly in under-resourced regions that lack conventional biomarkers.
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