Atrial FibrillationNCT05371405
Machine Learning in Atrial Fibrillation
An observational study of Atrial Fibrillation and Arrhythmias, Cardiac, run by Stanford University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 22 to 80 years
- Conditions the study is for
- Atrial Fibrillation, Arrhythmias, Cardiac
- How many people
- 120 participants
- Where
- 1 siteUnited States
- Study dates
- 12 Feb 2020to Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 22 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Kathleen Mills, BA contact
United States1 site, 1 recruiting
- Recruiting
Stanford University
Stanford, California, 94305
Sanjiv Narayan, MD, (650) 724-1850, sanjiv1@stanford.edu
Kathleen Mills, BA, kmills2@stanford.edu
About this study
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).
Read the 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.
- Conditions
- Atrial Fibrillation, Arrhythmias, Cardiac
- Keywords
- machine learning, ablation, atrial fibrillation
- Medical subject headings
- Atrial Fibrillation, Arrhythmias, Cardiac
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 year.
Secondary outcomes (1)
Machine Learning to Identify Ablation targets
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.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2022
- Study start12 Feb 2020
- Primary completionDec 2026 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial FibrillationStudies of the same condition, open and under way.
- More trials run by Stanford UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Stanford University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sanjiv Narayan, MD, PhD, Stanford University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05371405
- Sponsor's protocol ID
- 54679
- Status last verified
- Nov 2025
- First submitted
- 22 Apr 2022
- Last updated
- 14 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning in Atrial Fibrillation
- ClinicalTrials.gov ID
- NCT05371405
- Acronym / sponsor ID
- 54679
- Conditions
- Atrial Fibrillation, Arrhythmias, Cardiac
- Keywords
- machine learning, ablation, atrial fibrillation
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Stanford UniversityUniversity, hospital or other
- Study start
- 12 Feb 2020 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 12 May 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 22 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (6 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Machine Learning Prediction of Ablation Outcome
Time frame: 1 year.
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 secondary outcome
Machine Learning to Identify Ablation targets
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Nov 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Kathleen Mills, BA contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atrial Fibrillation and Arrhythmias, Cardiac, run by Stanford University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sanjiv Narayan, MD (650-724-1850, sanjiv1@stanford.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine Learning Prediction of Ablation Outcome (time frame: 1 year.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Nov 2025.