Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

Journal: Cardiology in the young
PMID: 35373725

Abstract

BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics.

Authors

  • Koichi Sughimoto
    Department of Cardiovascular Surgery, Chiba Kaihin Municipal Hospital, Chiba, Japan.
  • Jacob Levman
    Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical SchoolBoston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestown, MA, USA.
  • Fazleem Baig
    Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.
  • Derek Berger
    Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.
  • Yoshihiro Oshima
    Division of Cardiovascular Surgery, Hyogo Prefectural Kobe Children's Hospital, Kobe, Japan.
  • Hiroshi Kurosawa
    Division of Pediatric Critical Care Medicine, Hyogo Prefectural Kobe Children's Hospital, Kobe, Japan.
  • Kazunori Aoki
    Division of Pediatric Critical Care Medicine, Hyogo Prefectural Kobe Children's Hospital, Kobe, Japan.
  • Yusuke Seino
    Division of Pediatric Critical Care Medicine, Hyogo Prefectural Kobe Children's Hospital, Kobe, Japan.
  • Tetsuya Ueda
    Graduate School of Engineering, Chiba University, Chiba, Japan.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Kagami Miyaji
    Department of Cardiovascular Surgery, Kitasato University School of Medicine, Sagamihara, Japan.

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