Machine Learning in Atrial Fibrillation
Stanford University
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 for AF is currently suboptimal, in part because AF represents several disease states of which few have been delineated or used to successfully guide management. This study seeks to clarify this delineation of AF types using machine learning (ML).
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 analyses to identify electrical, structural and clinical determinants of ablation outcome in individual patients to guide personalized therapy". We address this hypothesis using a combined computational/clinical approach. The project will recruit 120 patients to address 3 Specific Aims. Aim 1. To identify components of AF electrograms that indicate depolarization, repolarization or other mechanisms a…
Eligibility
- Age range
- 22–80 years
- Sex
- All
- Healthy volunteers
- No
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 (result…
Location
- Stanford UniversityStanford, California