Machine Learning in Atrial Fibrillation

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).

Eligibility

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

Conditions3

Locations1 site

Browse More Trials