Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.

Journal: Anesthesia and analgesia
PMID: 32287123

Abstract

BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE.

Authors

  • Ményssa Cherifa
    Institut National de la Santé et de la Recherche Médicale - Paris, France.
  • Alice Blet
    The ACTERREA Research Group, Université De Paris, Paris, France.
  • Antoine Chambaz
    MAP5 (UMR CNRS 8145), université Paris Descartes, 75006 Paris, France.
  • Etienne Gayat
    Department of Anesthesia Burn and Critical Care, University Hospitals Saint-Louis-Lariboisière, AP-HP, Paris, France.
  • Matthieu Resche-Rigon
    Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France.
  • Romain Pirracchio

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