Citation

Holtenius J, Mosfeldt M, Enocson A, Berg HE. Injury 2024; 55(8): e111702.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.injury.2024.111702

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.

METHODS: Patient data was collected from the national trauma register in Sweden, SweTrau. The studied period was from the 1st of January 2015 to 31st of December 2019. After feature selection and multiple imputation of missing data three machine learning (ML) methods (Random Forest, eXtreme Gradient Boosting, and a Generalized Linear Model) were used to create predictive models. The ML models and TRISS were then tested on predictive ability for 30-day mortality.

RESULTS: The ML models were well-calibrated and outperformed TRISS in all the tested measurements. Among the ML models, the eXtreme Gradient Boosting model performed best with an AUC of 0.91 (0.88-0.93).

CONCLUSION: This study showed that all the developed ML-based prediction models were superior to TRISS for the prediction of trauma mortality.

Language: en

Keywords

Trauma; Mortality; Prediction; Machine learning; Extreme gradient boosting; Generalized linear model; National register; Polytrauma; Random forest; TRISS