Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation

作者：Maan Malahfji, Xin Tan, Yodying Kaolawanich, Mujtaba Saeed, A C Guta, Michael J. Reardon, William A. Zoghbi, Venkateshwar Polsani, Michael Elliott, Raymond Kim, Meng Li, Dipan J. Shah · 发表于：JACC. Cardiovascular imaging · 年份：2025 · DOI：10.1016/j.jcmg.2025.01.006 · 被引用次数：8 · 研究领域：Cardiac Valve Diseases and Treatments、Aortic Disease and Treatment Approaches、Congenital Heart Disease Studies

BACKGROUND: Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically. OBJECTIVES: The aim of this study was to first use unsupervised machine learning to identify unique patient phenoclusters in AR, and subsequently evaluate their prognostic relevance. METHODS: Clinical and cardiac magnetic resonance (CMR) characterization of moderate or severe AR patients was performed across 4 U.S. CENTERS: Data from 2 centers were used for derivation of phenoclusters and validation was performed in the other 2. The outcome was all-cause death. An unsupervised clustering pipeline, Partition Around Medoids, used 23 clinical and CMR variables to derive patient clusters independent of outcomes. RESULTS: Included were 972 patients with mean age 62 ± 23.2 years, 754 (78%) male, 680 (70%) trileaflet valve, and 330 (34%) underwent valve surgery. Over a median follow-up of 2.58 years (Q1-Q3: 1.03-5.50 years), the overall mortality rate was 12%. Four clusters were derived: 1) a younger predominantly male phenotype with majority of bicuspid aortic valve and high extent of left ventricular (LV) remodeling (1% mortality); 2) older male patients with predominantly tricuspid valves and intermediate outcomes (10% mortality); 3) older predominantly male patients with the highest burden of comorbidities, LV scarring, and dysfunction (22% mortality); and 4) a phenotype of predominantly f...