Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients
k-means clustering identified two prognostically distinct phenogroups of patients who had undergone TAVR with better discriminatory power than conventional risk and frailty calculators. Our results highlight the utility of machine learning applications for clinical risk prediction and scope to improve patient surveillance.
Authors
- Farhan Mohammed (ORCID: https://orcid.org/0000-0001-6321-6472)
- Mayooran Namasivayam (ORCID: https://orcid.org/0000-0001-8985-3947)
- Jason Kovacic
- Erik Meijering (ORCID: https://orcid.org/0000-0001-8015-8358)
- Michael P. Feneley (ORCID: https://orcid.org/0000-0002-6650-8865)
- Sebastiano Barbieri (ORCID: https://orcid.org/0000-0002-5919-372X)
- David C. Muller (ORCID: https://orcid.org/0000-0002-2350-0417)
- Louisa Jorm (ORCID: https://orcid.org/0000-0003-0390-661X)
- Chris Hayward (ORCID: https://orcid.org/0000-0003-4036-1890)
- David Roy
- Thomas Meredith
- Amy Pomeroy
Institutions
- The University of Queensland (AU)
- Victor Chang Cardiac Research Institute (AU)
- UNSW Sydney (AU)
- St Vincent's Hospital Sydney (AU)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-02-05
- DOI
- https://doi.org/10.3389/fcvm.2025.1444658
- Citations
- 4
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.55