Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

Journal: British journal of anaesthesia
Published Date:

Abstract

BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthoracic echocardiography (TTE) data might be used as a tool for predicting fluid responsiveness in critically ill patients.

Authors

  • Benoît Bataille
    Service de Réanimation Polyvalente, Centre Hospitalier de Narbonne, Narbonne, France. Electronic address: b_bataille2@yahoo.fr.
  • Jade de Selle
    Service de Réanimation Polyvalente, Centre Hospitalier de Narbonne, Narbonne, France.
  • Pierre-Etienne Moussot
    Service de Réanimation Polyvalente, Centre Hospitalier de Narbonne, Narbonne, France.
  • Philippe Marty
    Service d'Anesthésie, Clinique Medipôle Garonne, Toulouse, France.
  • Stein Silva
    Réanimation UMR, Centre Hospitalier Universitaire, CHU Purpan, Toulouse, France; Toulouse NeuroImaging Center, UMR UPS/INSERM 1214, CHU Purpan, Toulouse, France.
  • Pierre Cocquet
    Service de Réanimation Polyvalente, Centre Hospitalier de Narbonne, Narbonne, France.