Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
PMID: 39077854

Abstract

OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and further ventilation-related complications such as subglottic stenosis. In this study, we aimed to develop a machine learning tool to predict which patients need a tracheostomy at the onset of admission to the intensive care unit (ICU).

Authors

  • Matthew Nguyen
    UVA School of Medicine, Charlottesville, Virginia, USA.
  • Ameen Amanian
    Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada.
  • Meihan Wei
    Department of Biomedical Engineering-Whiting School of Engineering, Johns Hopkins University, Baltimore, USA.
  • Eitan Prisman
    Division of Otolaryngology Head and Neck Surgery, Vancouver General Hospital, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, Vancouver, British Columbia, Canada.
  • Pedro Alejandro Mendez-Tellez
    Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, USA.

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