Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.

Journal: Critical care (London, England)
PMID: 32731878

Abstract

BACKGROUND: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM).

Authors

  • Jesper Johnsson
    Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Helsingborg Hospital, Lund University, Helsingborg, Sweden. jesper.johnsson@skane.se.
  • Ola Björnsson
    Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.
  • Peder Andersson
    Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Skåne University Hospital, Lund University, Lund, Sweden.
  • Andreas Jakobsson
    Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.
  • Tobias Cronberg
    Department of Clinical Sciences Lund, Neurology, Skåne University Hospital, Lund University, Lund, Sweden.
  • Gisela Lilja
    Department of Clinical Sciences Lund, Neurology, Skåne University Hospital, Lund University, Lund, Sweden.
  • Hans Friberg
    Department of Clinical Sciences Lund, Intensive and Perioperative Care, Skåne University Hospital, Lund University, Malmö, Sweden.
  • Christian Hassager
    Department of Cardiology, The Heart Centre, Rigshospitalet University Hospital and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
  • Jesper Kjaergard
    Department of Cardiology, The Heart Centre, Rigshospitalet University Hospital and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
  • Matt Wise
    Department of Critical Care, University Hospital of Wales, Cardiff, UK.
  • Niklas Nielsen
    Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Helsingborg Hospital, Lund University, Helsingborg, Sweden.
  • Attila Frigyesi
    Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.

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