Candefjord Stefan, Buendia Ruben, Fagerlind Helen, Bálint András, Wege Claudia, Sjöqvist Bengt Arne
a Department of Signals and Systems , Chalmers University of Technology , Gothenburg , Sweden.
b SAFER Vehicle and Traffic Safety Centre at Chalmers , Gothenburg , Sweden.
Traffic Inj Prev. 2015;16 Suppl 2:S190-6. doi: 10.1080/15389588.2015.1057578.
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.
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).
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.
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.
本研究的目的是开发一种仅使用事故现场易于评估的事故特征来预测卡车驾乘人员现场损伤严重程度（OSISP）的算法。开发此算法的目的是将其作为涉及卡车交通事故现场分诊工具的基础。此外，该模型对于识别改善瑞典以及可能具有类似交通环境和院前程序的其他国家所使用分诊方案的重要因素可能具有重要价值。
研究范围为2003年至2013年在瑞典交通事故数据采集（STRADA）数据库中登记的、涉及瑞典公共道路交通事故的成年卡车驾乘人员。STRADA包含警方报告的信息以及在急诊医院接受治疗的受伤道路使用者的医疗数据。利用STRADA中的数据，针对轻型和重型卡车（即分别为重量达3500千克及≥16500千克的卡车）实施了2个用于推导重伤概率（此处定义为损伤严重度评分[ISS]＞15）的OSISP多变量逻辑回归模型。采用10折交叉验证程序，根据受试者工作特征曲线下面积（AUC）来评估OSISP算法的性能。
安全带使用率较低，尤其是重型卡车驾乘人员。为轻型和重型卡车开发的OSISP模型交叉验证AUC分别为0.81和0.74。在不进行交叉验证的情况下对所有数据评估模型时，轻型和重型卡车获得的AUC值均为0.87。使用和不使用交叉验证时AUC值的差异表明模型存在过拟合，这可能是数据集相对较小的结果。安全带使用在两类卡车中均是最有价值的预测因素；事故类型和年龄是轻型卡车的重要预测因素。
OSISP模型对轻型卡车驾乘人员具有良好的区分能力，对重型卡车驾乘人员具有合理的性能表现。通过获取更多数据可能会提高预测准确性。安全带使用是轻型和重型卡车驾乘人员重伤的最强预测因素。需要基于行为的安全计划和/或其他手段来鼓励卡车驾乘人员始终系好安全带。
