Using machine learning to improve treatment for atrial fibrillation
Machine Learning in Atrial Fibrillation
This study is testing if using machine learning can help doctors better understand and treat atrial fibrillation by looking at patient data from those who have had ablation.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 120 (estimated) |
| Ages | 22 Years to 80 Years |
| Sex | All |
| Sponsor | Stanford University Academic / other |
| Locations | 1 site (Stanford, California) |
| Trial ID | NCT05371405 on ClinicalTrials.gov |
What this trial studies
This project investigates how machine learning can enhance the understanding and treatment of atrial fibrillation (AF) by analyzing physiological data and outcomes from patients undergoing ablation. The study aims to identify specific electrical and structural features that influence the success of AF ablation, utilizing a combination of computational techniques and clinical data. By recruiting 120 patients, the research will focus on three main objectives: analyzing electrograms to predict outcomes, assessing the acute response to ablation, and determining long-term success rates of the procedure. The findings could lead to more personalized treatment strategies for AF patients.
Who should consider this trial
Good fit: Ideal candidates are patients undergoing ablation for paroxysmal or persistent atrial fibrillation who have failed or are intolerant to at least one anti-arrhythmic drug.
Not a fit: Patients with active coronary ischemia, decompensated heart failure, or other specified exclusion criteria may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to more effective and personalized treatment options for patients with atrial fibrillation.
How similar studies have performed: Other studies have shown promise in using machine learning for similar applications in cardiac care, suggesting potential for success in this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * undergoing ablation at Stanford of (a) paroxysmal AF (self-terminates \< 7 days), or (b) persistent AF (requires cardioversion to terminate). * Per our clinical practice and guidelines (Calkins et al, Heart Rhythm 2012), patients will have failed or be intolerant of ≥ 1 anti-arrhythmic drug. Exclusion Criteria: * active coronary ischemia or decompensated heart failure * atrial or ventricular clot on trans-esophageal echocardiography * pregnancy (to minimize fluoroscopic exposure) * inability or unwillingness to provide informed consent * rheumatic valve disease (results in a unique AF phenotype) * thrombotic disease or venous filters
Where this trial is running
Stanford, California
- Stanford University — Stanford, California, United States (Recruiting)
Study contacts
- Study coordinator: Sanjiv Narayan, MD
- Email: sanjiv1@stanford.edu
- Phone: 650-724-1850
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.