Evaluating performance and potential clinical benefit of the Swedish On Scene Injury Severity Prediction (OSISP) model for prehospital field triage on Norwegian trauma data
Artikel i vetenskaplig tidskrift, 2026

Background: Rule-based field triage protocols used by Emergency Medicine Service (EMS) personnel for assessment and prioritization of trauma patients achieve suboptimal field triage performance. An On Scene Injury Severity Prediction (OSISP) model was previously developed on Swedish trauma data with indications to improve field triage performance. This study aims to apply OSISP on external Norwegian data to assess its performance and clinical impact on unseen data.
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)

Författare

Anna Bakidou

Chalmers, Data- och informationsteknik, Funktionell programmering

Göteborgs universitet

Högskolan i Borås

Eva Corina Caragounis

Sahlgrenska universitetssjukhuset

Göteborgs universitet

M. A. Hagiwara

Högskolan i Borås

Olav Røise

Oslo universitetssykehus

Universitetet i Oslo

Anders Jonsson

Högskolan i Borås

Bengt-Arne Sjöqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Stefan Candefjord

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine

17577241 (eISSN)

Vol. 34 1 129

ASAP PoC - bättre civila och militära prehospitala Point-of-Care beslut med hjälp av datafusion och AI

VINNOVA (-), 2023-03-01 -- 2024-10-31.

Ämneskategorier (SSIF 2025)

Anestesi och intensivvård

Artificiell intelligens

DOI

10.1186/s13049-026-01662-w

PubMed

42498962

Mer information

Senast uppdaterat

2026-08-03