Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925-0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.

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Publication Details

Journal
Nature Communications
Published
2026-01-07
DOI
https://doi.org/10.1038/s41467-025-68198-y
Citations
1
Primary Topic
Trauma and Emergency Care Studies
Type
article
Field-Weighted Citation Impact
38.36

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Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study

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Nature Communications
Trauma and Emergency Care Studies
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Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study

Paul Testa, Daniel K. Sodickson, Byungchul Yu, Wan Shin Kim, Robert Femia, Jina Kim, Woocheol Jang, J. Hsu, Jayun Cho, Dong Keon Yon, Junepill Seok, Na-eun Oh, Wu Seong Kang, Jinseok Lee, Thomas Young-Chul Oh, Jin Young Lee
article en
1 citations

Abstract

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925-0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.

Nature CommunicationsVol. 17(1)
Gachon University (KR), Chungbuk National University (KR), Korea University Medical Center (KR), Westmead Hospital (AU), Kyung Hee University (KR), Gachon University Gil Medical Center (KR), Chonnam National University Hospital (KR), Kyung Hee University Medical Center (KR), Cheju Halla General Hospital (KR), Armed Forces Capital Hospital (KR), Advanced Imaging Research (United States) (US), Chungbuk National University Hospital (KR), New York University (US)
Korea Health Industry Development Institute, Iran Telecommunication Research Center, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 2%
Trauma and Emergency Care Studies
38.36
DOIOpen Access PDFOpenAlex
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