Machine Learning in Atrial Fibrillation

Part of paid clinical trials in Stanford, California.

Sponsor
Stanford University
Study ID
NCT05371405
Status
Recruiting
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Conditions

Eligibility Criteria

Sex
ALL
Age
22 Years - 80 Years
Healthy Volunteers
Not accepted

Study Details

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).

Key Dates

Start date
Feb 12, 2020
Status verified
Nov 2025
Primary completion
Dec 31, 2026
Completion
Dec 31, 2027

Study Design

Enrollment
120 participants (estimated)

Primary Outcome Measure

Machine Learning Prediction of Ablation Outcome [ Time Frame: 1 year. ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Stanford UniversityStanfordCalifornia94305
Sanjiv Narayan, MD
[email protected]
(650) 724-1850
Kathleen Mills, BA
[email protected]

Find similar trials in Stanford, CA

By condition
Atrial fibrillation
By specialty
CardiologyHeart disease
By research site
Stanford University· Stanford, CA

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