Assessing and validating machine learning-enhanced imputation of admission American Spinal Injury Association Impairment Scale grades for spinal cord injury.

Journal: Journal of neurosurgery. Spine
Published Date: May 9, 2025

Abstract

OBJECTIVE: The American Spinal Injury Association Impairment Scale (AIS) assigned at patient admission is an important predictor of outcomes following spinal cord injury (SCI). However, nearly 80% of records in the Spinal Cord Injury Model Systems (SCIMS) database-a multicenter prospective database of patients with SCI-lack admission AIS grades. Accurate imputation of this missing data could enable more robust analyses and insights into SCI recovery. This study aims to develop and validate methods for imputing missing admission AIS data in the SCIMS database.

Authors

  • Ritvik R Jillala
  • Carlos A Aude
  • Vikas N Vattipally
  • Kathleen R Ran
  • Kelly Jiang
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Carly Weber-Levine
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • A Daniel Davidar
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Andrew M Hersh
  • Jacob Jo
  • Daniel Lubelski
  • Ali Bydon
  • Timothy Witham
  • Nicholas Theodore
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Orthopaedic Surgery & Biomedical Engineering, Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: theodore@jhmi.edu.
  • Tej D Azad

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