Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.

Journal: Anesthesia and analgesia
Published Date: Dec 5, 2023

Abstract

BACKGROUND: Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS).

Authors

  • Pascal Laferrière-Langlois
    From the Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, California.
  • Fergus Imrie
    Oxford Protein Informatics Group, Department of Statistics , University of Oxford , Oxford OX1 3LB , U.K.
  • Marc-Andre Geraldo
    Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montréal, Québec, Canada.
  • Theodora Wingert
    University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Suite 3325, Los Angeles, CA 90095-7403, USA. Electronic address: twingert@mednet.ucla.edu.
  • Nadia Lahrichi
    Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montréal, Québec, Canada.
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.
  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.

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