Pil-Sung Yang

Kim Y, et al. Korean Circ J. 2027 ;57:e2. https://doi.org/10.4070/kcj.2025.0259

The 2026 Asia Pacific Heart Rhythm Society (APHRS) Guidelines represent the first comprehensive APHRS guidelines dedicated to the management of atrial fibrillation (AF) and comprise four parts covering epidemiology, detection/screening and integrated care; stroke prevention; rate and rhythm control; and lifestyle risk factor and comorbidity management. Part 1 focuses on epidemiology and integrated care management, integrating contemporary global evidence with data and clinical considerations specific to the Asia‐Pacific region. It provides evidence‐based and practical recommendations for clinical epidemiology in the region, as well as introduces the need for holistic or integrated care management.

Background: Accurate prediction of atrial fibrillation (AF) is essential for prevention. The CHARGE‐AF score, based on routinely available clinical factors, provides a practical tool for estimating AF risk but has limited predictive accuracy. This study aimed to improve AF risk prediction by integrating artificial intelligence (AI)–based electrocardiogram (ECG) analysis of age and sex with genetic information in addition to established clinical risk factors. Methods: We analyzed 39 478 UK Biobank participants without prior AF. A polygenic risk score for AF (AF‐PRS), the AI‐ECG age gap (AI‐ECG–predicted age minus chronological age), and AI‐ECG–predicted sex mismatch were evaluated on top of the CHARGE‐AF model. Model performance was compared across sequential prediction models. Results: Over a median follow‐up of 2.7 years (interquartile range, 1.7–4.2; maximum, 6.7), 533 participants (1.3%) developed AF. AF‐PRS (HR 1.61, 95% CI: 1.48–1.76) and the AI‐ECG age gap (HR 1.37, 95% CI: 1.24–1.51) were independently associated with incident AF. Compared with CHARGE‐AF alone (C‐index 0.708, 95% CI: 0.686–0.730), adding AF‐PRS improved discrimination (C‐index 0.743; Δ0.035, 95% CI: 0.021–0.049; p < 0.001). Adding traditional ECG parameters did not enhance performance, whereas incorporating AI‐ECG features (age gap and sex mismatch) into the combined clinical and genetic model provided additional gain in discrimination (C‐index 0.754; Δ0.045, 95% CI: 0.028–0.062), reclassification (net reclassification improvement 0.408), and discrimination improvement (0.007; all p < 0.001). Conclusions: Integrating genetic risk and AI‐ECG–derived features enhances AF prediction beyond an established clinical model, supporting the use of combined digital and genetic biomarkers for risk stratification.

Introduction: Patient characteristics and predictors of early (ERAT) and late (LRAT) recurrence of atrial tachyarrhythmia by atrial fibrillation (AF) type after cryoballoon ablation (CBA) remain poorly defined. Methods: Using the Korean Heart Rhythm Cryoballoon Registry (12 centers, 2018–2022), 2051 patients undergoing CBA for drug‐refractory AF were stratified by AF type. ERAT was defined as atrial tachyarrhythmia within the 3‐month blanking period and LRAT as recurrence beyond 3 months. Independent predictors of ERAT (all patients) and LRAT (patients without ERAT) were identified. Results: ERAT occurred in 631 patients (30.8%; 17.6% in paroxysmal and 40.4% in persistent AF). Among 1420 patients without ERAT, LRAT occurred in 311 (21.9%; 16.7% paroxysmal, 27.2% persistent). In paroxysmal AF, coronary artery disease (HR 1.60; 95% CI, 1.00–2.56; p = 0.048) and LA enlargement (HR 1.07; 95% CI, 1.04–1.10; p < 0.001) predicted ERAT, whereas hypertrophic cardiomyopathy (HR 5.01; 95% CI, 2.20–11.42; p < 0.001) and LA enlargement (HR 1.04; 95% CI, 1.01–1.08; p = 0.019) predicted LRAT. In persistent AF, AF duration (HR 1.04; 95% CI, 1.02–1.06; p < 0.001) and LA enlargement (HR 1.03; 95% CI, 1.02–1.04; p < 0.001) predicted ERAT, and LA enlargement (HR 1.03; 95% CI, 1.01–1.05; p = 0.010) independently predicted LRAT. Conclusion: Predictors of ERAT and LRAT differed between patients with paroxysmal and those with persistent AF. LA enlargement was the most consistently shared predictor across recurrence phenotypes.

Background: Sinoatrial node dysfunction and atrial fibrillation (AF) often coexist, and each potentially initiates or perpetuates the other. Consequently, genetic risk factors for AF may be associated with sick sinus syndrome (SSS). This study evaluated clinical, sociodemographic, laboratory and genetic factors in predicting the SSS incidence by using a polygenic risk score (PRS) for AF, given the absence of a dedicated PRS for SSS. Methods: A total of 502 421 individuals aged 38–73 years across 22 centres from 2006 to 2010 were enrolled in the UK Biobank. After those with incomplete data, prior AF or valvular disease were excluded, 405 869 participants (median age 58.0 (IQR, 50.0–64.0) years; 213 684 (45.2%) male) were analysed. AF PRS categorised the participants into low (first quintile), intermediate (middle three quintiles) and high (fifth quintile) risks. SSS incidence was recorded, and associations were evaluated using Cox regression with sociodemographic, clinical, laboratory and genetic variables. Results: During the median follow-up of 11.9 years, 769 SSS events occurred. Their incidence increased with age (HR 1.52, 95% CI 1.43 to 1.62 per 5 years), male sex (HR 1.39, 95% CI 1.03 to 1.88), prior myocardial infarction (HR 1.54, 95% CI 1.08 to 2.21) and hypertension (HR 1.21, 95% CI 1.02 to 1.42). The high-PRS and intermediate-PRS groups were related to 82% (HR 1.82, 95% CI 1.45 to 2.29) and 32% (HR 1.32, 95% CI 1.07 to 1.62) higher SSS risks, respectively. Cystatin C (HR 1.71, 95% CI 1.33 to 2.22) was also associated with the increased risk. The model’s C-index was 0.752 with sociodemographic and clinical variables, but it increased to 0.760 with genetic and biomarker data. Conclusion: In addition to sociodemographic and clinical risk factors, AF PRS and cystatin C were linked to the SSS incidence. Although their inclusion modestly improved prediction, these findings highlighted the potential of integrating genetic and biomarker information for comprehensive SSS risk assessments.

Lee JM, et al. Korean Circ J. 2026 Jan;56:e39. https://doi.org/10.4070/kcj.2025.0352

Kim M, et al. Korean Circ J. 2025 Dec;56:e23. https://doi.org/10.4070/kcj.2025.0242