External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

Journal: The spine journal : official journal of the North American Spine Society
Published Date:

Abstract

BACKGROUND CONTEXT: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were previously developed in a single institutional sample but remain to be externally validated.

Authors

  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Ali K Ahmed
    Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Zach Pennington
    Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Alejandro Chara
    Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Andrew Schilling
    Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, 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.
  • Paul T Ogink
  • Daniel M Sciubba
    Department of Neurosurgery, The Johns Hopkins Hospital, 600 North Wolfe Street; Meyer Building, Room 7-109, Baltimore, MD 21287, USA. Electronic address: Dsciubb1@jhmi.edu.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.