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.

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.

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.