European Heart Journal最新文献

Background and aims: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.

Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed.

Results: The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group.

Conclusions: The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

Atrial fibrillation (AF) has become the pre-dominant arrhythmia worldwide and is associated with high morbidity and mortality. Its pathogenesis is intricately linked to the deleterious impact of cardiovascular risk factors, emphasizing the pivotal imperative for early detection and mitigation strategies targeting these factors for the prevention of primary AF. While traditional risk factors are well recognized, an increasing number of novel risk factors have been identified in recent decades. This review explores the emerging non-traditional risk factors for the primary prevention of AF, including unhealthy lifestyle factors in current society (sleep, night shift work, and diet), biomarkers (gut microbiota, hyperuricaemia, and homocysteine), adverse conditions or diseases (depression, epilepsy, clonal haematopoiesis of indeterminate potential, infections, and asthma), and environmental factors (acoustic pollution and other environmental factors). Unlike traditional risk factors, individuals have limited control over many of these non-traditional risk factors, posing challenges to conventional prevention strategies. The purpose of this review is to outline the current evidence on the associations of non-traditional risk factors with new-onset AF and the potential mechanisms related to these risk factors. Furthermore, this review aims to explore potential interventions targeting these risk factors at both the individual and societal levels to mitigate the growing burden of AF, suggesting guideline updates for primary AF prevention.

Recent advances have given rise to a spectrum of digital health technologies that have the potential to revolutionize the design and conduct of cardiovascular clinical trials. Advances in domain tasks such as automated diagnosis and classification, synthesis of high-volume data and latent data from adjacent modalities, patient discovery, telemedicine, remote monitoring, augmented reality, and in silico modelling have the potential to enhance the efficiency, accuracy, and cost-effectiveness of cardiovascular clinical trials. However, early experience with these tools has also exposed important issues, including regulatory barriers, clinical validation and acceptance, technological literacy, integration with care models, and health equity concerns. This narrative review summarizes the landscape of digital tools at each stage of clinical trial planning and execution and outlines roadblocks and opportunities for successful implementation of digital tools in cardiovascular clinical trials.