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. 2023 Oct 9;23(1):206.
doi: 10.1186/s12911-023-02290-5.

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry

Anna Bakidou  1   2 , Eva-Corina Caragounis  3 , Magnus Andersson Hagiwara  4 , Anders Jonsson  4 , Bengt Arne Sjöqvist  5 , Stefan Candefjord  5
Affiliations

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry

Anna Bakidou et al. BMC Med Inform Decis Mak. .

Abstract

Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting.

Methods: The Swedish Trauma Registry was used to train and validate five models - Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network - in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates.

Results: There were 75,602 registrations between 2013-2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80-0.89 and AUCPR between 0.43-0.62.

Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.

Keywords: Artificial Intelligence (AI); Clinical Decision Support System (CDSS); Field triage; On Scene Injury Severity Prediction (OSISP); Prehospital care; Trauma.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow-chart of patient selection with the number of severely injured patients (NISS > 15) and field under- and overtriage included for each step. Instances presented as numbers of cases with percentage in parenthesis
Fig. 2
Fig. 2
Model performance for predicting the risk of severely injured (NISS > 15). a-b ROC curve and Precision-Recall curve for dataset A, c-d ROC curve and Precision-Recall curve for dataset B, e–f) ROC curve and Precision-Recall curve for dataset C, g-h ROC curve and Precision-Recall curve for dataset D
See this image and copyright information in PMC

References

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