Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

Journal: World neurosurgery
Published Date: May 28, 2021

Abstract

BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.

Authors

  • Akash A Shah
    David Geffen School of Medicine UCLA, Los Angeles, CA, USA.
  • Sai K Devana
    David Geffen School of Medicine UCLA, Los Angeles, CA, USA.
  • Changhee Lee
    University of California, Los Angeles, CA, USA.
  • Amador Bugarin
    Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Elizabeth L Lord
    Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Arya N Shamie
    Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Don Y Park
    Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.
  • Nelson F SooHoo
    David Geffen School of Medicine UCLA, Los Angeles, CA, USA.

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