Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

Journal: JAMA network open
Published Date: Apr 1, 2021

Abstract

IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and existing clinical sepsis tools have limited prognostic value among this high-risk population.

Authors

  • Margaret L Lind
    Department of Epidemiology, University of Washington, Seattle.
  • Stephen J Mooney
    Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington 98122, USA; email: sjm2186@uw.edu.
  • Marco Carone
    Department of Biostatistics, University of Washington.
  • Benjamin M Althouse
    Institute for Disease Modeling, Bellevue, Washington.
  • Catherine Liu
    Vaccine and Infectious Disease and Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
  • Laura E Evans
    Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle.
  • Kevin Patel
    Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle.
  • Phuong T Vo
    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • Steven A Pergam
    Vaccine and Infectious Disease and Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
  • Amanda I Phipps
    Department of Epidemiology, University of Washington, Seattle.

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