Prediction of mortality among severely injured trauma patients A comparison between TRISS and machine learning-based predictive models.

Journal: Injury
PMID: 38936227

Abstract

BACKGROUND: Given the huge impact of trauma on hospital systems around the world, several attempts have been made to develop predictive models for the outcomes of trauma victims. The most used, and in many studies most accurate predictive model, is the "Trauma Score and Injury Severity Score" (TRISS). Although it has proven to be fairly accurate and is widely used, it has faced criticism for its inability to classify more complex cases. In this study, we aimed to develop machine learning models that better than TRISS could predict mortality among severely injured trauma patients, something that has not been studied using data from a nationwide register before.

Authors

  • Jonas Holtenius
    Department of Clinical Science, Intervention and Technology, Karolinska Institute, 14152 Stockholm, Sweden; Department of Trauma, Acute Surgery and Orthopaedics, Karolinska University Hospital, 17177 Stockholm, Sweden. Electronic address: jonas.holtenius@regionstockholm.se.
  • Mathias Mosfeldt
    Department of Orthopaedics, Karolinska University Hospital, Stockholm, Sweden. mathias.mosfeldt@ki.se.
  • Anders Enocson
    Department of Molecular Medicine and Surgery, Karolinska Institute, 17176 Stockholm, Sweden; Department of Trauma, Acute Surgery and Orthopaedics, Karolinska University Hospital, 17177 Stockholm, Sweden.
  • Hans E Berg
    a Department of Orthopaedics , Karolinska University Hospital, Institution of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet , Stockholm , Sweden.

Keywords

External Resources

Popular Topics
Recent Journals