Machine learning-based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker.

Journal: Transfusion
Published Date: Apr 25, 2025

Abstract

BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challenges in managing bleeding complications. Optimal transfusion strategies remain uncertain for this diverse patient group, necessitating accurate predictive tools. This study developed and validated a machine learning (ML) algorithm to predict bleeding complications in patients with ECMO, using red blood cell (RBC) transfusion as a surrogate marker.

Authors

  • Tadashi Kamio
    Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
  • Masaru Ikegami
    Terumo Corporation, Shonan Center, Nakai-machi, Kanagawa, Japan.
  • Megumi Mizuno
    Terumo Corporation, Shonan Center, Nakai-machi, Kanagawa, Japan.
  • Seiichiro Ishii
    Terumo Corporation, Shonan Center, Nakai-machi, Kanagawa, Japan.
  • Hayato Tajima
    Terumo Corporation, Shonan Center, Nakai-machi, Kanagawa, Japan.
  • Yoshihito Machida
    Terumo Corporation, Shonan Center, Nakai-machi, Kanagawa, Japan.
  • Kiyomitsu Fukaguchi
    Shonan Kamakura General Hospital, Kamakura, Japan.

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