Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation
Unique patient phenoclusters of AR are described using a machine learning approach leveraging comprehensive CMR and clinical characterization. This approach may be an opportunity for a precision medicine approach to enhance risk stratification of patients with AR. Female patients with AR pose a unique phenotype with high mortality, which deserves greater attention.
Authors
- Yodying Kaolawanich (ORCID: https://orcid.org/0000-0003-1262-9212)
- Maan Malahfji (ORCID: https://orcid.org/0000-0002-2701-8783)
- Mujtaba Saeed (ORCID: https://orcid.org/0000-0003-1876-0238)
- Xin Tan (ORCID: https://orcid.org/0000-0002-3370-1415)
- Dipan J. Shah (ORCID: https://orcid.org/0000-0002-6179-2393)
- William A. Zoghbi (ORCID: https://orcid.org/0000-0003-0802-0455)
- Venkateshwar Polsani (ORCID: https://orcid.org/0009-0004-6576-1297)
- Michael Elliott (ORCID: https://orcid.org/0000-0002-2519-4871)
- Michael J. Reardon (ORCID: https://orcid.org/0000-0002-2880-6132)
- Raymond Kim (ORCID: https://orcid.org/0000-0001-5054-3810)
- Meng Li (ORCID: https://orcid.org/0000-0002-3602-3429)
- A C Guta (ORCID: https://orcid.org/0000-0001-9520-2841)
Institutions
- Houston Methodist (US)
- Duke University (US)
- Clinical Emergency Hospital Bucharest (RO)
- Piedmont Atlanta Hospital (US)
- Rice University (US)
Publication Details
- Journal
- JACC. Cardiovascular imaging
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1016/j.jcmg.2025.01.006
- Citations
- 6
- Primary Topic
- Cardiac and Coronary Surgery Techniques
- Type
- article
- Field-Weighted Citation Impact
- 12.04
Funders
- Rice University
- Houston Methodist Hospital
- National Institutes of Health