Prediction of biological evolution following blood product transfusion during liver transplantation: the contribution of machine learning to decision-making.

Journal: BMJ health & care informatics
Published Date: Jun 22, 2025

Abstract

OBJECTIVES: Liver transplantation is a complex procedure frequently requiring transfusion of blood products to manage coagulopathy and haemorrhage. This study aimed to develop machine learning models to predict the biological effects of blood product transfusions, assisting clinicians in selecting optimal therapeutic combinations.

Authors

  • Olivier Duranteau
    Intensive Care, HIA Percy, Clamart, France olivier.duranteau@hubruxelles.be.
  • Benjamin Popoff
    Department of Anesthesiology, Critical Care and Perioperative Medicine, CHU Rouen, 37 Bd Gambetta, Rouen, 76000, France, 33 232888292.
  • Axel Abels
    Machine Learning Group, Université Libre de Bruxelles, Bruxelles, Belgium.
  • Valerio Lucidi
    Université Libre de Bruxelles, Brussels, Belgium.
  • Eric Savier
    Department of Hepato-Biliary and Pancreatic Surgery and Liver Transplantation, Sorbonne University, Paris, France.
  • Florian Blanchard
    Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France. Electronic address: florian.blanchard@aphp.fr.
  • Thibault Martinez
    Intensive Care, HIA Percy, Clamart, France.
  • Patrizia Loi
    Université Libre de Bruxelles, Brussels, Belgium.
  • Desislava Germanova
    Department of Digestive Surgery, Hopital Erasme, Brussels, Belgium.
  • Anne Demulder
    Université Libre de Bruxelles, Brussels, Belgium.
  • Jacques Creteur
    Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Route de Lennik 808, 1070, Brussels, Belgium.
  • Turgay Tuna
    Anesthesiology, Hopital Erasme, Bruxelles, Belgium.

Keywords

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