Using AI to predict atrial fibrillation recurrence
Development and Validation of an Atrial Fibrillation Recurrence Model Based on Large Language Models: A Retrospective, Multicenter Study
This study is testing a new way to use AI to help doctors predict if atrial fibrillation will come back after treatment, so they can better manage their patients.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 3000 (estimated) |
| Ages | 18 Years to 90 Years |
| Sex | All |
| Sponsor | The Fourth Affiliated Hospital of Zhejiang University School of Medicine Academic / other |
| Locations | 5 sites (Kunming, Yunnan and 4 other locations) |
| Trial ID | NCT06977516 on ClinicalTrials.gov |
What this trial studies
This study aims to develop a risk prediction model for atrial fibrillation (AF) recurrence by utilizing large language model (LLM) technology to analyze various textual data, including clinical characteristics and procedural records. By leveraging deep learning capabilities, the model seeks to provide actionable insights for electrophysiologists managing patients who have undergone catheter ablation for AF. The research will retrospectively collect data from multiple treatment centers to enhance the predictive accuracy of AF recurrence outcomes.
Who should consider this trial
Good fit: Ideal candidates for this study are patients diagnosed with atrial fibrillation who have undergone their first catheter ablation between January 2016 and December 2023.
Not a fit: Patients who may not benefit from this study include those undergoing repeated AF ablation procedures or those with incomplete follow-up data.
Why it matters
Potential benefit: If successful, this model could significantly improve the management and treatment outcomes for patients with atrial fibrillation by providing tailored risk assessments.
How similar studies have performed: While the application of large language models in medical risk prediction is novel, other studies have shown success in using advanced data analysis techniques for similar purposes.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: We plan to retrospectively collect data from five atrial fibrillation treatment centers, including The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Taizhou Hospital of Zhejiang Province, The Affiliated Hospital of Yunnan University, Jinhua People's Hospital, and Beilun District People's Hospital, for patients diagnosed with atrial fibrillation who underwent their first catheter ablation between January 2016 and December 2023. Exclusion Criteria: 1. Patients undergoing repeated AF ablation procedures; 2. AF patients with incomplete follow-up data; 3. AF patients lacking preoperative laboratory tests or key textual modality results (echocardiography, ambulatory ECG); 4. Patients with comorbid conditions including acute myocardial infarction, valvular heart disease, malignant tumors, or hyperthyroidism, and those with AF recurrence within 3 months post-ablation.
Where this trial is running
Kunming, Yunnan and 4 other locations
- The Affiliated Hospital of Yunnan University — Kunming, Yunnan, China (Recruiting)
- Jinhua People's Hospital — Jinhua, Zhejiang, China (Recruiting)
- Beilun District People's Hospital — Ningbo, Zhejiang, China (Recruiting)
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University — Taizhou, Zhejiang, China (Recruiting)
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine — Yiwu, Zhejiang, China (Recruiting)
Study contacts
- Study coordinator: Shudong Xia, M.D.
- Email: shystone@zju.edu.cn
- Phone: +86-13989897610
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.