On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants
Artikel i vetenskaplig tidskrift, 2015

The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures. Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS]>15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ≥16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC). Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks. Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.

prehospital care

logistic regression

triage

trucks

traffic safety

post crash

Författare

Stefan Candefjord

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Biomedicinsk elektromagnetik

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Ruben Buendia

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Helen Fagerlind

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

András Bálint

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Claudia Wege

Volvo Group

Bengt-Arne Sjöqvist

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Traffic Injury Prevention

1538-9588 (ISSN) 1538-957X (eISSN)

Vol. 16 190-196

Drivkrafter

Hållbar utveckling

Styrkeområden

Transport

Ämneskategorier

Farkostteknik

Sannolikhetsteori och statistik

DOI

10.1080/15389588.2015.1057578

PubMed

26436231

Mer information

Senast uppdaterat

2018-11-16