Ng, G. André

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Abstract Aims For patients with refractory atrial fibrillation (AF) undergoing atrioventricular nodal ablation (AVNA), initial single-chamber right ventricular (RV)-only pacing is standard. Given the deleterious effects of chronic RV-only pacing, the impact of an initial biventricular (BiV) pacing strategy post-ablation is of interest. Methods and results We conducted a meta-analysis to determine the effect of BiV vs. RV-only pacing in patients undergoing AVNA for refractory atrial fibrillation. A search of multiple electronic databases identified 921 reports, which included four randomized controlled trials (n = 534). Mean New York Heart Association (NYHA) class was 2.3 and mean left ventricular ejection fraction (LVEF) was 44%. When compared with RV-only pacing, BiV pacing was not associated with reduced mortality [risk ratio 0.85, 95% confidence interval (CI) 0.40–1.82, P = 0.68]. In three studies comprised of patients with left ventricular systolic dysfunction (mean EF 41 ± 3%), BiV pacing demonstrated a non-significant reduction in cardiac mortality (risk ratio 0.59, 95% CI 0.25–1.39; P = 0.23). Compared with RV-only pacing, BiV pacing was associated with significant improvement in symptoms [Minnesota Living with Heart Failure Questionnaire (MLWHFQ) 2.72 points fewer, 95% CI 1.45–3.99] and increased LVEF (+2.6%, 95% CI 1.69–3.44), but no significant change in 6 min walk distance (6MWD) (5.02 ms more, 95% CI −1.56 to 11.59; P = 0.13). Conclusions In patients with refractory AF undergoing AVNA, BiV pacing was not associated with significantly improved survival when compared with RV-only pacing. A modest, but significant improvement in structural and functional response to BiV pacing was observed.