Using computer learning to improve care for atrial fibrillation patients
Part of Heart & circulation clinical trials.
This study uses machine learning (computer pattern-finding) to better understand atrial fibrillation and guide care around the time of an ablation procedure. It may help refine how doctors treat certain AF patients based on their heart patterns.
Summary written for real people, not researchers, by Clin2.
Who can take part
- You are scheduled for an atrial fibrillation ablation at Stanford
- Your AF is either paroxysmal (episodes stop on their own within 7 days) or persistent (needs electrical cardioversion to stop)
- You have tried at least one anti-arrhythmia medicine for AF, but it either didn’t work or you couldn’t tolerate it
- You do not have active blocked-heart blood flow (coronary ischemia) or unstable heart failure
- Your heart ultrasound (trans-esophageal echocardiogram) does not show a clot in the atria or ventricles
Quick eligibility check
Answer a few plain-language questions, based on this study's own requirements, to get a preliminary sense of fit.
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