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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Triage | Research

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

Authors: Anna Bakidou, Eva-Corina Caragounis, Magnus Andersson Hagiwara, Anders Jonsson, Bengt Arne Sjöqvist, Stefan Candefjord

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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.

Available only for authorised users

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Title: On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
Authors: Anna Bakidou
Eva-Corina Caragounis
Magnus Andersson Hagiwara
Anders Jonsson
Bengt Arne Sjöqvist
Stefan Candefjord
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Triage
Artificial Intelligence
Care
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02290-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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