Clin2
NCT05371405Possibly a fitRecruiting

Using computer learning to improve care for atrial fibrillation patients

Atrial FibrillationArrhythmias, Cardiac

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.

Phase
N/A
Enrollment
120 people
Ages
22 years to 80 years
Study type
Observational

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

View the official record on ClinicalTrials.gov

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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