Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study.

Journal: PloS one
PMID: 32658901

Abstract

BACKGROUND: Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs model).

Authors

  • Ryo Ueno
    Department of Intensive Care Medicine, Kameda Medical Center, Chiba, Japan.
  • Liyuan Xu
    Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
  • Wataru Uegami
    Anatomical Pathology, Kameda Medical Center, Chiba, Japan.
  • Hiroki Matsui
    Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan.
  • Jun Okui
    Post-Graduate Education Center, Kameda Medical Center, Chiba, Japan.
  • Hiroshi Hayashi
    Post-Graduate Education Center, Kameda Medical Center, Chiba, Japan.
  • Toru Miyajima
    Post-Graduate Education Center, Kameda Medical Center, Chiba, Japan.
  • Yoshiro Hayashi
    Department of Intensive Care Medicine, Kameda Medical Center, Chiba, Japan.
  • David Pilcher
    School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
  • Daryl Jones
    Australian and New Zealand Intensive Care Research Center, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.

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