Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

Journal: PloS one
Published Date: Jul 21, 2025

Abstract

OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU patients receiving unfractionated heparin for anticoagulation and to identify key predictive factors.

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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