Intraoperative Features Improve Model Risk Predictions After Coronary Artery Bypass Grafting.

Journal: Annals of thoracic surgery short reports
Published Date: Mar 7, 2024

Abstract

BACKGROUND: Intraoperative physiologic parameters could offer predictive utility in evaluating risk of adverse postoperative events yet are not included in current standard risk models. This study examined whether the inclusion of continuous intraoperative data improved machine learning model predictions for multiple outcomes after coronary artery bypass grafting, including 30-day mortality, renal failure, reoperation, prolonged ventilation, and combined morbidity and mortality (MM).

Authors

  • Willa Potosnak
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Keith A Dufendach
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Chirag Nagpal
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • David J Kaczorowski
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Pyongsoo Yoon
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Johannes Bonatti
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • James K Miller
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Artur W Dubrawski
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals