Subphenotyping prone position responders with machine learning.

Journal: Critical care (London, England)
PMID: 40087660

Abstract

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their association with mortality and response to prone positioning.

Authors

  • Maxime Fosset
    Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Dario von Wedel
    Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
  • Simone Redaelli
    Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Daniel Talmor
    Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Nicolas Molinari
    Desbrest Institute of Epidemiology and Public Health, University of Montpellier, INRIA, Montpellier, France.
  • Julie Josse
    Inria Montpellier, Bâtiment 5, 860 Rue de St-Priest, 34090 Montpellier, France.
  • Elias N Baedorf-Kassis
    Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Maximilian S Schaefer
    Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Center for Anesthesia Research Excellence, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Dusseldorf University Hospital, Dusseldorf, Germany.
  • Boris Jung
    Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. b-jung@chu-montpellier.fr.

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