Machine Learning Identifies Higher Survival Profile In Extracorporeal Cardiopulmonary Resuscitation.

Journal: Critical care medicine
PMID: 38535090

Abstract

OBJECTIVES: Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR.

Authors

  • Ruben Crespo-Diaz
    Mayo Clinic, Department of Cardiovascular Diseases, Rochester, MN.
  • Julian Wolfson
    Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street S.E., MMC 303, Minneapolis, MN 55455, United States. Electronic address: julianw@umn.edu.
  • Demetris Yannopoulos
    Center for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
  • Jason A Bartos
    Center for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.

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