Risk factors affecting severe thoracic injuries in motor vehicle collisions based on age group and collision directions.

OBJECTIVE: This study aimed to investigate the effect of age and collision direction on the severity of thoracic injuries based on a real-world crash database. METHODS: This was a retrospective, observational study. We used the Korean In-Depth Accident Study (KIDAS) database, which was collected from crash injury patients who visited emergency medical centers between January 2011 and February 2022 in Korea. Among the 4520 patients enrolled in the database, we selected 1908 adult patients with abbreviated injury scale (AIS) scores between 0 and 6 in the thoracic region. We classified patients with an AIS score of 3 or higher into the severe injury group. RESULTS: The incidence rate of severe thoracic injuries due to motor vehicle accidents was 16.4%. Between the severe and non-severe thoracic injury groups, there were significant differences in sex, age, collision direction, crash object, seatbelt use, and delta-V parameters. Among the age groups, over 55 years occupants had a higher risk in the thoracic regions than those under 54 years occupants. The risk of severe thoracic injury was highest in near-side collisions in all collision directions. Far-side and rear-end collisions showed a lower risk than frontal collisions. Occupants with unfastened seatbelts were at greater risk. CONCLUSIONS: The risk of severe thoracic injury is high in near-side collisions among elderly occupants. However, the risk of injury for elderly occupants increases in a super-aging society. To reduce thoracic injury, safety features made for elderly occupants in near-side collisions are required.

Machine learning-based injury severity prediction of level 1 trauma center enrolled patients associated with car-to-car crashes in Korea.

Injury prediction models enables to improve trauma outcomes for motor vehicle occupants in accurate decision-making and early transport to appropriate trauma centers. This study aims to investigate the injury severity prediction (ISP) capability in machine-learning analytics based on five-different regional Level 1 trauma center enrolled patients in Korea. We study car crash-related injury data of 1417 patients enrolled in the Korea In-Depth Accident Study database from January 2011 to April 2021. Severe injury classification was defined using an Injury Severity Score of 15 or greater. A planar crash was considered by excluding rollovers to compromise an accurate prediction. Furthermore, dissimilarities of the collision partner component based on vehicle segmentation were assumed for crash incompatibility. To handle class-imbalanced clinical datasets, we used four data-sampling techniques (i.e., class-weighting, resampling, synthetic minority oversampling, and adaptive synthetic sampling). Machine-learning analytics based on logistic regression, extreme gradient boosting (XGBoost), and a multilayer perceptron model were used for the evaluations. Each model was executed using five-fold cross-validation to solve overfitting consistent with the hyperparameters tuned to improve model performance. The area under the receiver operating characteristic curve of 0.896. Additionally, the present ISP model showed an under-triage rate of 6.1%. The Delta-V, age, and Principal ~ were significant predictors. The results demonstrated that the data-balanced XGBoost model achieved a reliable performance on injury severity classification of emergency department patients. This finding considers ISP model selection, which affected prediction performance based on overall predictor variables.

Preventive Effectiveness of Thoracic Side Airbags in Side-Impact Crashes Based on Korea In-Depth Accident Study (KIDAS) Database.

Studies on the effectiveness of thoracic side airbags (tSABs) in preventing thoracic injuries is limited and conflicting. This retrospective observational study aims to evaluate the effectiveness of tSABs in side-impact crashes based on data for motor vehicle occupants (MVOs) who visited an emergency department in Korea. The data were obtained from the Korean In-Depth Accident Study (KIDAS) database for patients treated at Wonju Severance Christian Hospital between January 2011 and April 2020. Of the 3899 patients with road traffic injuries, data for 490 patients were used. The overall frequency of tSAB deployment in side-impact crashes was found to be 8.1%. In the multivariate analysis, elderly age, near-side impact, colliding with fixed objects, non-oblique force, and higher crush extent were found to be factors associated with higher thoracic injuries (Abbreviated Injury Scale ≥ 2). MVOs in crashes with tSAB deployment were at an increased risk of injury compared with MVOs in crashes with no deployment, but no statistical difference was observed [adjusted odds ratios (AORs): 1.65 (0.73-3.73)]. Further, the incidence of lung injury and rib fractures increased with tSAB activation (p < 0.05). These results demonstrate the limited capability of tSABs in preventing thoracic injuries in motor vehicle crashes.