Machine Learning in Atrial Fibrillation

NCT05371405 | Recruiting

• 1 other identifier: 54679
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 1

Brief 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).

Conditions

Atrial Fibrillation; Arrhythmias, Cardiac

Keywords

machine learning; ablation; atrial fibrillation

Outcome Measures

Primary Outcomes (1)

• Machine Learning Prediction of Ablation Outcome

  To compare success of AF ablation in each patient at 1 year (defined as absence of AF or atrial tachycardia on outpatient monitoring) to predicted success by the machine learning algorithm developed in this project. The outcome compares observed success at 1 year (Yes, No) to (a) a binary predictor and (b) a continuous variable of success from the algorithm. The machine learning algorithm is trained on clinical and electrophysiological data to predict if certain lesion sets will or will not be successful.

  1 year.

Secondary Outcomes (1)

• Machine Learning to Identify Ablation targets

  1 year

Eligibility Criteria

• Age: 22 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Subjects will be men and women of any ethnicity aged 22-80 years 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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Stanford University: lead

Study Sites (1)

Stanford University

Stanford, California, 94305, United States

RECRUITING

MeSH Terms

Conditions

Atrial Fibrillation; Arrhythmias, Cardiac

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Sanjiv Narayan, MD

CONTACT

650-724-1850; sanjiv1@stanford.edu

Kathleen Mills, BA

CONTACT

kmills2@stanford.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Medicine

Study Record Dates

• First Submitted: April 22, 2022
• First Posted: May 12, 2022
• Study Start: February 12, 2020
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2027
• Last Updated: November 14, 2025

Record last verified: 2025-11

Locations

US (1)