Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort.

Journal: Journal of neurosurgery. Pediatrics
PMID: 38608292

Abstract

OBJECTIVE: Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients' well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I.

Authors

  • Victor Gabriel El-Hajj
    1Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota.
  • Abdul Karim Ghaith
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Adrian Elmi-Terander
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Edward S Ahn
    3Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.
  • David J Daniels
    2Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota; and.
  • Mohamad Bydon
    4Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.

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