Background/Objectives: Chronological age (CA) is commonly used in clinical decision-making, yet it may not accurately reflect biological aging. Recent advances in artificial intelligence (AI) allow estimation of electrocardiogram (ECG)-derived heart age, which may serve as a non-invasive biomarker for physiological aging. This study aimed to develop and validate a deep learning model to predict ECG-heart age in individuals with no structural heart disease. Methods: We trained a convolutional neural network (DenseNet-121) using 12-lead ECGs from 292,484 individuals (mean age: 51.4 ± 13.8 years; 42.3% male) without significant cardiac disease. Exclusion criteria included missing age data, age 90 years, and structural abnormalities. CA was used as the target variable. Model performance was evaluated using the coefficient of determination (R²), Pearson correlation coefficient (PCC), mean absolute error (MAE), and root mean square error (RMSE). External validation was conducted using 1191 independent ECGs. Results: The model demonstrated strong predictive performance (R² = 0.783, PCC = 0.885, MAE = 5.023 years, RMSE = 6.389 years). ECG-heart age tended to be overestimated in younger adults (≤30 years) and underestimated in older adults (≥70 years). External validation showed consistent performance (R² = 0.703, PCC = 0.846, MAE = 5.582 years, RMSE = 7.316 years). Conclusions: The proposed AI-based model accurately estimates ECG-heart age in individuals with structurally normal hearts. ECG-derived heart age may serve as a reliable biomarker of biological aging and support future risk stratification strategies.

Kim MJ, et al. Korean Circ J. 2025 Feb;55:e45. https://doi.org/10.4070/kcj.2024.0310

Background/Objectives: Catheter ablation is an effective treatment for atrial fibrillation (AF) but is associated with femoral vascular complications. While anticoagulation therapy has been linked to these complications, specific risk factors remain unclear. This study assessed the incidence and predictors of vascular complications after catheter ablation using systematic duplex ultrasound (DUS) as well as their outcomes. Methods: A single-center observational study was conducted with 404 consecutive AF patients who underwent catheter ablation between March 2023 and February 2024. DUS was performed systematically post-procedure in all patients to identify complications; these were primarily treated with DUS-guided manual compression. Results: Vascular complications were observed in 6.4% of patients, higher than reported in previous studies. Hematomas (3.5%) and arteriovenous fistulas (AVFs, 2.0%) were the most common such complications. Multivariate analysis identified repeat ablation (odds ratio [OR] 3.09, 95% confidence interval [CI] 1.10–8.64, p = 0.03) and months of experience <6 months (OR 3.42, 95% CI 1.36–8.63, p = 0.01) as significant predictors of complications. DUS-guided compression managed most complications successfully, with three pseudoaneurysms resolved through compression and one through embolization. However, AVFs were relatively resistant to conservative management, often necessitating prolonged observation or intervention. Conclusions: Systematic DUS following catheter ablation revealed a higher-than-expected incidence of vascular complications. Repeat ablation and months of experience <6 months are potential predictors of femoral vascular complications. DUS-guided compression is effective for most complications, although AVFs present greater treatment challenges.

Kim MJ, et al. Int J Arrhythm. 2026 ;27:e9. https://doi.org/10.18501/ija.2026.27.e9