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Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study.
Philip Hempel1,2, Antônio H Ribeiro3, Marcus Vollmer4,5
1Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany. philip.hempel@med.uni-goettingen.de.
NPJ Digital Medicine
|January 13, 2025
Summary
Artificial intelligence-electrocardiogram (AI-ECG) models can predict cardiovascular disease risk using aging effects from longitudinal electrocardiograms (ECGs). This approach enhances early patient risk identification and improves cardiovascular health outcomes.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomarkers
Background:
- Aging impacts the 12-lead electrocardiogram (ECG), correlating with cardiovascular disease (CVD).
- AI-ECG models estimate aging effects as a novel biomarker but typically use single ECGs, neglecting longitudinal data.
- Validation of AI-ECG models on diverse populations and longitudinal data is crucial for clinical application.
Purpose of the Study:
- To validate an AI-ECG model trained on Brazilian data using a German cohort with over 20 years of follow-up.
- To assess the utility of longitudinal ECG data in AI-ECG models for cardiovascular risk prediction.
- To investigate the association of AI-ECG-derived aging effects with specific cardiovascular outcomes and mortality.
Main Methods:
- Validation of a pre-trained AI-ECG model on a German cohort with longitudinal ECG data (over 20 years).
- Comparison of model performance using single ECGs versus longitudinal ECGs.
- Application of explainable AI (XAI) methods to interpret model predictions.
- Statistical analysis of hazard ratios and odds ratios for cardiovascular events and mortality.
Main Results:
- The AI-ECG model demonstrated similar performance (r² = 0.70) in the German cohort compared to the original Brazilian study (r² = 0.71).
- Incorporating longitudinal ECG data significantly strengthened the association with cardiovascular risk, increasing the hazard ratio for mortality from 1.43 to 1.65.
- AI-ECG-derived aging effects were significantly associated with increased odds of atrial fibrillation, heart failure, and mortality.
- Explainable AI confirmed that the model focuses on clinically relevant ECG features associated with aging.
Conclusions:
- Longitudinal AI-ECG analysis of aging effects is a robust method for cardiovascular risk assessment.
- This approach can serve as a novel population-level biomarker for early identification of at-risk individuals.
- The validated AI-ECG model shows promise for improving early detection and management of cardiovascular disease.
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