Machine Learning‐Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II
Utilizing machine learning, we identified a high-risk cluster for procedural failure in catheter ablation of persistent atrial fibrillation within the DECAAF II trial population. The primary differentiating factors of this high-risk cluster include older age, high left atrial fibrosis, elevated BMI, and increased left atrial volume.
Authors
- Aneesh Dhore
- Abdel Hadi El Hajjar
- Nassir F. Marrouche (ORCID: https://orcid.org/0000-0003-0454-0541)
- Hadi Younes (ORCID: https://orcid.org/0000-0002-7202-1164)
- Charbel Noujaim (ORCID: https://orcid.org/0000-0002-3179-4398)
- Ghaith Shamaileh
- Mario Mekhael (ORCID: https://orcid.org/0000-0001-8809-5419)
- Chao Huang (ORCID: https://orcid.org/0000-0003-4215-3630)
- Omar Kreidieh (ORCID: https://orcid.org/0000-0001-6189-2821)
- Ala Assaf (ORCID: https://orcid.org/0000-0001-6443-798X)
- Eoin Donnellan (ORCID: https://orcid.org/0000-0001-8440-9900)
- Ghassan Bidaoui (ORCID: https://orcid.org/0000-0002-3562-7386)
- Han Feng (ORCID: https://orcid.org/0000-0003-3163-8931)
- Nour Chouman (ORCID: https://orcid.org/0000-0002-6624-1590)
- Chanho Lim (ORCID: https://orcid.org/0000-0001-9998-5601)
- Dan Li (ORCID: https://orcid.org/0000-0003-2230-9300)
- Daniel W. Nelson (ORCID: https://orcid.org/0000-0003-0493-6650)
Institutions
- Tulane University (US)
Publication Details
- Journal
- Journal of Cardiovascular Electrophysiology
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1111/jce.16554
- Citations
- 1
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Medtronic