- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05371405
Machine Learning in Atrial Fibrillation
Study Overview
Status
Conditions
Detailed Description
This project tests the novel hypothesis that "Machine learning (ML) in AF patients can integrate physiological data across biological scales stratified by labeled outcomes, and use explainability analyses to identify electrical, structural and clinical determinants of ablation outcome in individual patients to guide personalized therapy". We address this hypothesis using a combined computational/clinical approach. The project will recruit 120 patients to address 3 Specific Aims.
Aim 1. To identify components of AF electrograms that indicate depolarization, repolarization or other mechanisms at the tissue level, using ML trained to monophasic action potentials (MAP). For this prospective protocol, we will collect electrograms using a MAP catheter at multiple atrial sites in patients undergoing AF ablation. We will then test if our algorithms developed previously from our registry, can predict MAP timings from AF electrograms.
Aim 2. To identify electrical and structural features of the acute response of AF to ablation near and remote from PVs at the individual heart level using machine learning and biostatistical approaches. For this prospective protocol, we will recruit patients undergoing their standard-of-care ablation and test if an ML classifier developed previously in a registry dataset prospectively predicts acute response to specific ablation strategies.
Aim 3. To identify patients in whom ablation is unsuccessful or successful long-term using ML and biostatistics. For this prospective protocol, we will recruit patients undergoing their standard-of-care ablation and test if an ML classifier developed previously in a registry dataset prospectively predicts 1 year freedom from atrial arrhythmias.
This project is significant because it will establish a deeper understanding of AF and might reveal novel mechanisms of AF maintenance. Our results can be translated directly to practice and may enable the development of better treatment options.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Sanjiv Narayan, MD
- Phone Number: 650-724-1850
- Email: sanjiv1@stanford.edu
Study Contact Backup
- Name: Kathleen Mills, BA
- Email: kmills2@stanford.edu
Study Locations
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California
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Stanford, California, United States, 94305
- Recruiting
- Stanford University
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Contact:
- Sanjiv Narayan, MD
- Phone Number: 650-724-1850
- Email: sanjiv1@stanford.edu
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Contact:
- Kathleen Mills, BA
- Email: kmills2@stanford.edu
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Machine Learning Prediction of Ablation Outcome
Time Frame: 1 year.
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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.
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Machine Learning to Identify Ablation targets
Time Frame: 1 year
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To determine if AF ablation success at 1 year (defined as absence of AF or atrial tachycardia on outpatient monitoring) correlates with the ablation of regions predicted by the machine learning algorithm in this project to be successful ablation targets.
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1 year
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 54679
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Atrial Fibrillation
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Ablacon, Inc.CompletedArrhythmias, Cardiac | Atrial Fibrillation, Persistent | Persistent Atrial Fibrillation | Longstanding Persistent Atrial FibrillationGermany
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Ablacon, Inc.RecruitingAtrial Fibrillation | Arrhythmias, Cardiac | Arrhythmia | Atrial Flutter | Atrial Fibrillation, Persistent | Atrial Tachycardia | Atrial Arrhythmia | Atrial Fibrillation Paroxysmal | Atrial Fibrillation, Paroxysmal or PersistentUnited States, Belgium, Netherlands, Czechia
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Barts & The London NHS TrustAtriCure, Inc.Not yet recruitingAtrial Fibrillation, Persistent | Persistent Atrial Fibrillation | Atrial Arrhythmia | Atrium; FibrillationUnited Kingdom
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AtriCure, Inc.Active, not recruitingPersistent Atrial Fibrillation | Atrial Fibrillation (AF) | Longstanding Persistent Atrial FibrillationUnited States
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Maastricht University Medical CenterRWTH Aachen UniversityUnknownAtrial Fibrillation (Paroxysmal) | Atrial Fibrillation Recurrent | Atrial Fibrillation Common Gene VariantsNetherlands
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Adagio MedicalRecruitingAtrial Fibrillation | Atrial Flutter | Paroxysmal Atrial Fibrillation | Persistent Atrial FibrillationNetherlands, Germany, Belgium
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Vivek ReddyEnrolling by invitationAtrial Fibrillation and Flutter | Atrial Flutter Typical | Atrial Fibrillation, Paroxysmal or PersistentUnited States
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Fundació Institut de Recerca de l'Hospital de la...RecruitingAtrial Arrhythmia | Atrial Fibrillation and Flutter | Atrial Fibrillation RecurrentSpain
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Abbott Medical DevicesRecruitingAtrial Fibrillation | Paroxysmal Atrial Fibrillation | Persistent Atrial Fibrillation | Atrial ArrhythmiaUnited States, Spain, Belgium, Australia, Germany, Netherlands, France, Austria, Canada, Czechia, Italy, United Kingdom
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St. George's Hospital, LondonRecruitingAtrial Fibrillation | Atrial Fibrillation, Persistent | Persistent Atrial Fibrillation | Atrial ArrhythmiaUnited Kingdom