Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study.

Journal: BMJ open
Published Date: Dec 2, 2019

Abstract

INTRODUCTION: About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delayed treatment leads to increased morbidity and mortality. The goal of this proposal is to develop, validate and test real-time intraoperative risk prediction tools based on clinical data and high-fidelity physiological waveforms to predict haemodynamic instability during surgery.

Authors

  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
  • Ira Hofer
  • Joseph Rinehart
  • Christine Lee
    Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
  • Kathirvel Subramaniam
    Anesthesiology, UPMC, Pittsburgh, Pennsylvania, USA.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.
  • Artur Dubrawski
    Auton Lab, School of Computer Science, Carnegie Mellon University Pittsburgh, PA, USA.
  • Michael R Pinsky
    Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.

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