Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing.

Authors

  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • John Theurer
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048.
  • Nathan R Stein
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • J Weston Hughes
    Department of Computer Science, Stanford University, Palo Alto, CA 94025.
  • Pierre Elias
    Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY.
  • Bryan He
    Department of Computer Science, Stanford University, Stanford, California.
  • Neal Yuan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Grant Duffy
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States.
  • Roopinder K Sandhu
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Joseph Ebinger
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
  • Patrick Botting
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
  • Melvin Jujjavarapu
    Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Brian Claggett
    Brigham and Women's Hospital, Boston, MA, USA.
  • James E Tooley
    Department of Medicine, Stanford University, Palo Alto, CA, 94025.
  • Tim Poterucha
    Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.
  • Michael Nurok
    Division of Anesthesia, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Marco Perez
    Division of Cardiology, Stanford University, Palo Alto, CA, USA.
  • Adler Perotte
    Department of Biomedical Informatics, Columbia University, New York, New York, USA.
  • James Y Zou
    Department of Computer Science, Stanford University, Stanford, CA, USA. jamesz@stanford.edu.
  • Nancy R Cook
    Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
  • Sumeet S Chugh
    Division of Artificial Intelligence in Medicine, Department of Medicine and Cedars-Sinai Smidt Heart Institute, Los Angeles, CA. Electronic address: sumeet.chugh@csmc.edu.
  • Susan Cheng
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Christine M Albert
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.