ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden.

Journal: Acta anaesthesiologica Scandinavica
Published Date: Jul 21, 2024

Abstract

BACKGROUND: A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and society. Logistic regression models like Simplified Acute Physiology Score 3 (SAPS 3) and APACHE are the traditional ICU mortality prediction models. With the emergence of machine learning (machine learning) and artificial intelligence, new possibilities arise to create prediction models that have the potential to sharpen predictive accuracy and reduce the likelihood of misclassification in the prediction of 30-day mortality.

Authors

  • Tobias Siöland
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Araz Rawshani
    University of Gothenburg, Institute of Medicine, Sahlgrenska Academy, Gröna Stråket 4, 43146, Gothenburg, Sweden.
  • Bengt Nellgård
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Johan Malmgren
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Jonatan Oras
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Keti Dalla
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Giovanni Cinà
    Pacmed B.V., Amsterdam, The Netherlands.
  • Lars Engerström
    Department of Anesthesiology and Intensive Care, Linköping University, Norrköping, Sweden.
  • Fredrik Hessulf
    Department of Anesthesiology and Intensive Care Medicine, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

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