Sorting Atrial Fibrillation Patients into Groups Using Multi-Omics and AI Data
Studies whether combined genetic, imaging and blood data can classify atrial fibrillation patients.
Plain-language title generated from the registry record
Registry title: Precise Phenotyping Classification in Atrial Fibrillation
Key facts
- Where
- Beijing, ChinaSee the site
Where in China
- Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, ChinaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- China National Center for Cardiovascular Diseases
- Trial code
- NCT07044011
- Record updated
- 29 June 2025
- Original description
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.Read full original descriptionShow less
Study on 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.
Inclusion: Patients diagnosed with atrial fibrillation; Voluntarily participated in the study and signed the informed consent form.