A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach.

OBJECTIVE: Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serious chest injuries from MVCs. The Korean In-Depth Accident Study database was analyzed to identify risk factors associated with motor vehicle occupants' serious chest injury. METHODS: Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was assessed using the Collision Deformation Classification (CDC) code and images of the damaged vehicle, and trauma scores were used to determine injury severity. Serious chest injury was defined as an Abbreviated Injury Scale (AIS) score for the chest code was more than 3. The patients were divided into two groups: serious chest injury patients with MAIS ≥ 3 and those with non-serious chest injury with MAIS < 3. A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on MVCs was constructed by a logistic regression analysis. RESULTS: Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicle type, the proportion of light truck occupants was higher in the serious group than in the non-serious group (p=.026). The rate of seatbelt use was lower in the serious group than in the non-serious group (p=.008). The median crush extent (seventh column of the CDC code) was higher in the serious group than in the non-serious group (p<.001). Emergency room data showed that the rates of intensive care unit (ICU) admission and death were higher among patients with serious injuries (p<.001). Similarly, the general ward/ICU admission data showed that the transfer and death rates were higher in patients with serious injuries (p<.001). The median ISS was higher in the serious group than in the non-serious group (p<.001). A predictive model was derived based on sex, age, vehicle type, seating row, belt status, collision type, and crush extent. This predictive model had an explanatory power of 67.2% for serious chest injuries. The model was estimated for external validation using the confusion matrix by applying the predictive model to the 2019 and 2020 data of the same structure as the data at the time of model development in the KIDAS database. CONCLUSIONS: Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury.

A Predictive Model to Analyze the Factors Affecting the Presence of Traumatic Brain Injury in the Elderly Occupants of Motor Vehicle Crashes Based on Korean In-Depth Accident Study (KIDAS) Database.

Traumatic brain injury (TBI), also known as intracranial injury, occurs when an external force injures the brain. This study aimed to analyze the factors affecting the presence of TBI in the elderly occupants of motor vehicle crashes. We defined elderly occupants as those more than 55 years old. Damage to the vehicle was presented using the Collision Deformation Classification (CDC) code by evaluation of photos of the damaged vehicle, and a trauma score was used for evaluation of the severity of the patient's injury. A logistic regression model was used to identify factors affecting TBI in elderly occupants and a predictive model was constructed. We performed this study retrospectively and gathered all the data under the Korean In-Depth Accident Study (KIDAS) investigation system. Among 3697 patients who visited the emergency room in the regional emergency medical center due to motor vehicle crashes from 2011 to 2018, we analyzed the data of 822 elderly occupants, which were divided into two groups: the TBI patients (N = 357) and the non-TBI patients (N = 465). According to multiple logistic regression analysis, the probabilities of TBI in the elderly caused by rear-end (OR = 1.833) and multiple collisions (OR = 1.897) were higher than in frontal collision. Furthermore, the probability of TBI in the elderly was 1.677 times higher in those with unfastened seatbelts compared to those with fastened seatbelts (OR = 1.677). This study was meaningful in that it incorporated several indicators that affected the occurrence of the TBI in the elderly occupants. In addition, it was performed to determine the probability of TBI according to sex, vehicle type, seating position, seatbelt status, collision type, and crush extent using logistic regression analysis. In order to derive more precise predictive models, it would be needed to analyze more factors for vehicle damage, environment, and occupant injury in future studies.