Machine Learning in Atrial Fibrillation
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age22-80
SponsorStanford University
About this trial
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).
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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
Trial 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.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
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.
Time frame
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.
Time frame
1 yearSponsors and contacts
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Source ClinicalTrials.gov