Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation.

Journal: JACC. Cardiovascular imaging
PMID:

Abstract

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.

Authors

  • Maan Malahfji
    Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
  • Xin Tan
    School of Public Health, Chengdu Medical College, Chengdu 610500, China.
  • Yodying Kaolawanich
    Division of Cardiology, Department of Internal Medicine, Duke University, Durham, North Carolina, USA.
  • Mujtaba Saeed
    Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
  • Andrada Guta
    Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA; Division of Cardiology, Department of Medicine, Emergency Clinical Hospital of Bucharest, Bucharest, Romania.
  • Michael J Reardon
    Department of Cardiovascular Surgery, Houston Methodist Hospital, Houston, Tex. Electronic address: mreadon@houstonmethodist.org.
  • William A Zoghbi
    Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
  • Venkateshwar Polsani
    Department of Cardiac Surgery, Piedmont Heart Institute, Atlanta, Ga.
  • Michael Elliott
  • Raymond Kim
    Department of Urology, Gosford District Hospital and Gosford Private Hospital, Gosford, Australia.
  • Meng Li
    Co-Innovation Center for the Sustainable Forestry in Southern China; Cerasus Research Center; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.
  • Dipan J Shah
    Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, Texas.