Actively Recruiting
Machine Learning in Atrial Fibrillation
Led by Stanford University · Updated on 2025-11-14
120
Participants Needed
1
Research Sites
407 weeks
Total Duration
On this page
AI-Summary
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).
CONDITIONS
Official Title
Machine Learning in Atrial Fibrillation
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Undergoing ablation at Stanford for (a) paroxysmal AF (self-terminates in less than 7 days) or (b) persistent AF (requires cardioversion to terminate)
- Have failed or are intolerant of at least one anti-arrhythmic drug
You will not qualify if you...
- Active coronary ischemia or decompensated heart failure
- Presence of atrial or ventricular clot detected by trans-esophageal echocardiography
- Pregnancy
- Inability or unwillingness to provide informed consent
- Rheumatic valve disease
- Thrombotic disease or presence of venous filters
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Stanford University
Stanford, California, United States, 94305
Actively Recruiting
Research Team
S
Sanjiv Narayan, MD
CONTACT
K
Kathleen Mills, BA
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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