Precise Phenotyping Classification in Atrial Fibrillation

This study aims to integrate multi-omics data (genomics, imaging, serology, etc.) to achieve precise phenotyping classification for atrial fibrillation (AF) patients and explore personalized rhythm control strategies. By enrolling over 1,000 AF patients, we will collect genomic data including GWAS and single-cell sequencing, combined with cardiac MRI, CT, echocardiography imaging, and serum biomarkers, to uncover AF pathological mechanisms and recurrence risks at molecular and structural levels. Machine learning and AI algorithms will be employed to develop AF phenotypic classification models, which will be validated across multiple centers to assess their accuracy and reliability in predicting AF recurrence and therapeutic responses. Furthermore, clinical trials will evaluate the efficacy of oral dronedarone hydrochloride tablets and intravenous nicorandil hydrochloride in preventing post-catheter ablation recurrence and their safety/effectiveness in early cardioversion. Finally, based on advanced phenotyping results, we will establish personalized rhythm control strategies integrating pharmacotherapy, catheter ablation, and lifestyle interventions to optimize AF management.