Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.

Journal: The Annals of thoracic surgery
Published Date: Sep 25, 2021

Abstract

BACKGROUND: Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative time points.

Authors

  • Rodrigo Zea-Vera
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
  • Christopher T Ryan
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
  • Jim Havelka
    InformAI, Houston, Texas.
  • Stuart J Corr
    DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, Texas.
  • Tom C Nguyen
    Division of Adult Cardiothoracic Surgery, University of California at San Francisco, San Francisco, California.
  • Subhasis Chatterjee
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Texas.
  • Matthew J Wall
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
  • Joseph S Coselli
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Texas.
  • Todd K Rosengart
    Department of Surgery, Baylor College of Medicine, Houston, Texas.
  • Ravi K Ghanta
    Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas. Electronic address: ravi.ghanta@bcm.edu.

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