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NCT02997254: COMPARE_AF

COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation

Recruiting now Last updated 14 November 2025
What this trial tests

trial in Atrial Fibrillation in 1,000 participants. Currently enrolling.

Timeline
1 December 2016
Primary endpoint
31 December 2026
30 June 2027

Quick facts

Lead sponsorStanford University
StatusRecruiting now
Study typeOBSERVATIONAL
Enrollment1,000
Start date1 December 2016
Primary completion31 December 2026
Estimated completion30 June 2027
Sites1 location across United States

Conditions studied

Sponsor

Stanford University

Who can join

Adults 21 to 90, any sex, with Atrial Fibrillation or Abnormal Heart Rhythms. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

This is a cohort study to evaluate algorithms for mapping atrial fibrillation by mapping atrial structure and mapping electrical activity to detect physiologically important areas. A key innovation of the observation study is to assess tracking of physiological activity in AF, in relation to tissue activity (MAP recordings), and in relation to AF waves of activity. This observational work extends prior studies that focused on focal and rotational activity, dispersion of electrical activity, low voltage activity and others.

Publications & conference data

8 peer-reviewed publications reference this trial (live from Europe PMC):

  1. Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.
    Alhusseini MI, Abuzaid F, Rogers AJ, Zaman JAB, et al · · 2020 · cited 44× · PMID 32631100 · DOI 10.1161/circep.119.008160
  2. Interaction of Localized Drivers and Disorganized Activation in Persistent Atrial Fibrillation: Reconciling Putative Mechanisms Using Multiple Mapping Techniques.
    Kowalewski CAB, Shenasa F, Rodrigo M, Clopton P, et al · · 2018 · cited 30× · PMID 29884620 · DOI 10.1161/circep.117.005846
  3. Comparing phase and electrographic flow mapping for persistent atrial fibrillation.
    Swerdlow M, Tamboli M, Alhusseini MI, Moosvi N, et al · · 2019 · cited 22× · PMID 30882924 · DOI 10.1111/pace.13649
  4. Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.
    Rodrigo M, Alhusseini MI, Rogers AJ, Krittanawong C, et al · · 2022 · cited 13× · PMID 35429831 · DOI 10.1016/j.compbiomed.2022.105451
  5. Ablation of Atrial Fibrillation Drivers.
    Baykaner T, Zaman JAB, Wang PJ, Narayan SM. · · 2017 · cited 5× · PMID 29326835 · DOI 10.15420/2017:28:1
  6. The continuous challenge of AF ablation: From foci to rotational activity.
    Narayan SM, Vishwanathan MN, Kowalewski CAB, Baykaner T, et al · · 2017 · cited 5× · PMID 29126896 · DOI 10.1016/j.repc.2017.09.007
  7. Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.
    Feng R, Deb B, Ganesan P, Tjong FVY, et al · · 2023 · cited 4× · PMID 37849936 · DOI 10.3389/fcvm.2023.1189293
  8. Online webinar training to analyse complex atrial fibrillation maps: A randomized trial.
    Mesquita J, Maniar N, Baykaner T, Rogers AJ, et al · · 2019 · cited 4× · PMID 31269029 · DOI 10.1371/journal.pone.0217988

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Other recruiting trials for Atrial Fibrillation

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Data sources for this page

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