Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

Journal: The spine journal : official journal of the North American Spine Society
Published Date: Jan 30, 2025

Abstract

BACKGROUND CONTEXT: Preoperative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of postsurgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated machine learning models are both effective and user-friendly, allowing healthcare professionals with minimal technical expertise to identify high-risk patients who may need additional preoperative support.

Authors

  • Attri Ghosh
    Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Philip J Freda
    University of Pennsylvania, Institute for Biomedical Informatics, Philadelphia, PA.
  • Shane Shahrestani
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
  • Andre E Boyke
    1Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and.
  • Alena Orlenko
    Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Hyunjun Choi
    Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, United States.
  • Nicholas Matsumoto
    Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, West Hollywood, CA 90069, United States.
  • Tayo Obafemi-Ajayi
  • Jason H Moore
    University of Pennsylvania, Philadelphia, PA, USA.
  • Corey T Walker
    1Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and.

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