Machine Learning in Atrial Fibrillation
Recruiting · NCT05371405 · Observational (researchers observe without assigning treatment) · Lead sponsor: Stanford University
View the official record on ClinicalTrials.gov →What this trial is about
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).
Who can take part
Age range
22 Years to 80 Years
Sex
All (male and female)
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)
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 it is running
1 location listed across 1 US state.
- Stanford University - Stanford, California, United States