Machine Learning in Atrial Fibrillation
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age22-80
SponsorStanford University
About this trial
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).
Eligibility criteria
Qualifiers
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.
Disqualifiers
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
Trial design
Treatments tested in this trial
- Not listed
Trial groups
No trial groups listed
Sponsors and collaborators
Source ClinicalTrials.gov