ECG-based artificial enhancement for risk stratification of thromboembolic and major adverse cardiac events in atrial fibrillation compared with CHA2DS2-VA scoring

Background The CHA2DS2-VA score is commonly utilized to evaluate thromboembolic risk in atrial fibrillation (AF) patients, guiding anticoagulation therapy decisions. However, its predictive accuracy has limitations, necessitating more refined risk assessment methods. Recent advancements in computational techniques allow electrocardiogram (ECG)-based modeling, which may enhance the precision of predicting thromboembolic events and major adverse cardiac events (MACE). This study examines an artificially enhanced ECG-based method and compares its effectiveness to the traditional CHA2DS2-VA scoring system. Objective To assess whether an artificially enhanced ECG-based risk stratification model provides superior predictive performance for thromboembolic and major adverse cardiac events in atrial fibrillation patients compared to the CHA2DS2-VA scoring system. Methods Using data from 10,181 AF patients, we developed and validated an AI-driven ECG system employing transformer deep neural networks. This system processed 60,413 ECGs from a university hospital, recorded between 2006 and 2021, alongside clinical records including CHA2DS2-VA scores and longitudinal follow-up data. Clinical analysis was meticulously validated by a team of seven experts, including cardiologists, to ensure robustness beyond traditional data warehouses. Results The AI-driven ECG system effectively differentiated between low and high thromboembolic risks, demonstrating a significant difference in AI scores (35.22 ± 10.17 vs. 80.30 ± 8.77, p < 0.001) and achieving high predictive accuracy with an AUROC of 0.923 (95% CI 0.918 – 0.928). Performance metrics varied significantly across CHA2DS2-VA scores, with notable precision and recall differences between scores 0 and 2-8. Kaplan-Meier curves illustrated a marked survival difference between the risk groups in terms of MACE and related hospitalizations. Conclusion The application of computationally enhanced ECG analysis provides a more precise risk stratification for thromboembolic and major adverse cardiac events in atrial fibrillation patients. This method offers a promising alternative to conventional scoring systems, facilitating more informed clinical decision-making regarding anticoagulation therapy.