Evaluating performance and potential clinical benefit of the Swedish On Scene Injury Severity Prediction (OSISP) model for prehospital field triage on Norwegian trauma data
Journal article, 2026
Methods: Adult trauma incidents involving EMS resources at the scene of incident were included. An eXtreme Gradient Boosting OSISP model was developed on Swedish trauma registry (SweTrau) data for 2013–2020 and trained to predict severely injured patients defined as new injury severity score (NISS) > 15. OSISP was evaluated on Norwegian trauma registry (NTR) data for 2017–2022. The model performance on NTR data was evaluated using overall performance measures for discrimination and calibration (receiver operating characteristic curve, precision-recall curve, area under these curves, Brier score, calibration curve, calibration slope, calibration in the large). OSISP’s clinical impact was assessed by evaluating its under- and overtriage rates and comparing to current clinical outcome and mortality.
Results: The raw data from SweTrau and NTR had 75,602 and 76,529 registrations, with 47,357 and 29,709 remaining after applying eligibility criteria. In the included data, there were 16.9% and 25.2% patients with NISS > 15 in SweTrau and NTR, respectively. The OSISP predictors represented age, sex, details of the patient’s condition, incident and injury details, vital signs, and administrative details. OSISP applied on NTR data yielded an overall performance of AUCROC=0.83, AUCPR=0.64, Brier score = 0.14, calibration slope = 0.79, and calibration in the large=–0.09. On its own, OSISP reduced undertriage to 5.0% but increased overtriage to 61.0%, while the complementary use (NTR’s triage outcomes combined with OSISP’s predictions) reduced undertriage to 2.7%, overtriage to 19.7% (compare to current clinical practice with an undertriage = 56.0% and overtriage = 30.6%), and mortality from 4.1% to 3.4–3.5%.
Conclusions: OSISP’s overall performance was successfully validated and a potential capability to improve triage and decrease mortality was observed, also for elderly and patients from the northern region, groups often missed with current triage tools.
Registration: Not applicable.
Trial registration: Not applicable.
On Scene Injury Severity Prediction (OSISP)
Machine Learning (ML)
Trauma
External validation
Artificial Intelligence (AI)
Prehospital care
Norwegian Trauma Registry (NTR)
Diagnostic modelling
Swedish Trauma Registry (SweTrau)
Author
Anna Bakidou
Chalmers, Computer Science and Engineering (Chalmers), Functional Programming
University of Gothenburg
University of Borås
Eva Corina Caragounis
Sahlgrenska University Hospital
University of Gothenburg
M. A. Hagiwara
University of Borås
Olav Røise
Oslo University Hospital
University of Oslo
Anders Jonsson
University of Borås
Bengt-Arne Sjöqvist
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Stefan Candefjord
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
17577241 (eISSN)
Vol. 34 1 129ASAP PoC Improved Civil and Military prehospital Point-of-Care decisions through Data Fusion and AI
VINNOVA (-), 2023-03-01 -- 2024-10-31.
Subject Categories (SSIF 2025)
Anesthesiology and Intensive Care
Artificial Intelligence
DOI
10.1186/s13049-026-01662-w
PubMed
42498962