Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID: 30941521

Abstract

PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis.

Authors

  • Paul T Ogink
  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Quirina C B S Thio
    Q. C. B. S. Thio, A. V. Karhade, P. T. Ogink, K. Raskin, S. Lozano-Calderon, J. H. Schwab, Division of Orthopaedic Oncology, Department of Orthopaedics, Massachusetts General Hospital-Harvard Medical School, Boston, MA, USA K. de Amorim Bernstein, Department of Radiation Oncology, Massachusetts General Hospital-Harvard Medical School, Boston, MA, USA.
  • William B Gormley
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Fetullah C Oner
    UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
  • Jorrit J Verlaan
    UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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