Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

Journal: EBioMedicine
PMID: 38968776

Abstract

BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention.

Authors

  • Karlo M Pedro
    Division of Neurosurgery & Spine Program, Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
  • Mohammed Ali Alvi
    Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
  • Nader Hejrati
    Department of Neurosurgery & Spine Center of Eastern Switzerland, Kantonsspital St. Gallen & Medical School of St. Gallen, Rorschacherstrasse 95, 9007 St. Gallen, Switzerland.
  • Ayesha I Quddusi
    Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
  • Anoushka Singh
    Division of Genetics & Development, Krembil Brain Institute, University Health Network, Toronto, ON, Canada; Division of Neurosurgery, Krembil Neuroscience Centre, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
  • Michael G Fehlings
    Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.

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