Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale.

Journal: Neurosurgery
Published Date: Jun 17, 2022

Abstract

BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization.

Authors

  • Aly A Valliani
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Rui Feng
    Department of Pharmacy, The Fourth Hospital of Hebei Medical University Shijiazhuang 050000, Hebei, China.
  • Michael L Martini
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Sean N Neifert
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Nora C Kim
    Department of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
  • Jonathan S Gal
  • Eric K Oermann
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • John M Caridi
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.

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