Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events.

Kankan ZhaoYanjie ZhuXiuyu ChenShujuan YangWeipeng YanKai YangYanyan SongChen CuiXi XuQingyong ZhuZhuo-Xu CuiGang YinHuaibin ChengMinjie LuDong LiangKe ShiLei ZhaoHui LiuJiayin ZhangLiang ChenSanjay K PrasadShihua ZhaoHairong Zheng

Published in: JACC. Cardiovascular imaging (2024)

ML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Keyphrases

hypertrophic cardiomyopathy
left ventricular
cardiovascular events
machine learning
coronary artery disease
aortic stenosis
cardiac resynchronization therapy
mitral valve
left atrial
type diabetes
artificial intelligence
big data
ejection fraction
deep learning
transcatheter aortic valve replacement