Status:
RECRUITING
Machine Learning in Atrial Fibrillation
Lead Sponsor:
Stanford University
Conditions:
Atrial Fibrillation
Arrhythmias, Cardiac
Eligibility:
All Genders
22-80 years
Brief Summary
Atrial fibrillation is a serious public health issue that affects over 5 million Americans (Miyazaka, Circulation 2006) in whom it may cause skipped beats, dizziness, stroke and even death. Therapy fo...
Detailed Description
This project tests the novel hypothesis that "Machine learning (ML) in AF patients can integrate physiological data across biological scales stratified by labeled outcomes, and use explainability anal...
Eligibility Criteria
Inclusion
- 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
- 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
Key Trial Info
Start Date :
February 12 2020
Trial Type :
OBSERVATIONAL
Allocation :
ESTIMATED
End Date :
December 1 2027
Estimated Enrollment :
120 Patients enrolled
Trial Details
Trial ID
NCT05371405
Start Date
February 12 2020
End Date
December 1 2027
Last Update
November 14 2025
Active Locations (1)
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1
Stanford University
Stanford, California, United States, 94305