Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning.

Journal: JACC. Cardiovascular imaging
Published Date: Oct 18, 2017

Abstract

OBJECTIVES: This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning (ML) to predict major adverse cardiac events (MACE).

Authors

  • Julian Betancur
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Yuka Otaki
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Manish Motwani
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Mathews B Fish
    Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon.
  • Mark Lemley
    Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon.
  • Damini Dey
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Heidi Gransar
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Balaji Tamarappoo
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Guido Germano
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Tali Sharir
    Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel.
  • Daniel S Berman
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Piotr J Slomka
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.

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