On scene injury severity prediction (OSISP) algorithm for car occupants

Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently on mechanisms of injury in order to reduce undertriage. In this study the value of accident characteristics for field triage is evaluated by developing an on scene injury severity prediction (OSISP) algorithm using only accident characteristics that are feasible to assess at the scene of accident. A multivariate logistic regression model is constructed to assess the probability of a car occupant being severely injured following a crash, based on the Swedish Traffic Accident Data Acquisition (STRADA) database. Accidents involving adult occupants for calendar years 2003-2013 included in both police and hospital records, with no missing data for any of the model variables, were included. The total number of subjects was 29128, who were involved in 22607 accidents. Partition between severe and non-severe injury was done using the Injury Severity Score (ISS) with two thresholds: ISS>8 and ISS>15. The model variables are: belt use, airbag deployment, posted speed limit, type of accident, location of accident, elderly occupant (>55 years old), sex and occupant seat position. The area under the receiver operator characteristic curve (AUC) is 0.78 and 0.83 for ISS>8 and ISS>15, respectively, as estimated by 10-fold cross-validation. Belt use is the strongest predictor followed by type of accident. Posted speed limit, age and accident location contribute substantially to increase model accuracy, whereas sex and airbag deployment contribute to a smaller extent and seat position is of limited value. These findings can be used to refine triage protocols used in Sweden and possibly other countries with similar traffic environments.


Buendia R, Candefjord S, Fagerlind H, Bálint A, Sjöqvist BA
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Accident Analysis & Prevention
Year of publication

Safer – Vehicle and Traffic Safety Centre

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