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1.
Traffic Inj Prev ; 24(7): 618-624, 2023.
Article in English | MEDLINE | ID: mdl-37436170

ABSTRACT

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.


Subject(s)
Accidental Injuries , Thoracic Injuries , Wounds and Injuries , Humans , Accidents, Traffic , Logistic Models , Hemothorax/complications , Thoracic Injuries/epidemiology , Thoracic Injuries/etiology , Motor Vehicles
2.
Eur J Trauma Emerg Surg ; 49(6): 2429-2437, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37341757

ABSTRACT

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.


Subject(s)
Thoracic Injuries , Wounds and Injuries , Adult , Aged , Humans , Middle Aged , Abbreviated Injury Scale , Accidents, Traffic , Motor Vehicles , Risk Factors , Thoracic Injuries/epidemiology , Thoracic Injuries/etiology , Wounds and Injuries/complications , Retrospective Studies
3.
Comput Biol Med ; 153: 106393, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36586232

ABSTRACT

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.


Subject(s)
Accidents, Traffic , Wounds and Injuries , Humans , Trauma Centers , Automobiles , Motor Vehicles , Republic of Korea , Wounds and Injuries/epidemiology
4.
Article in English | MEDLINE | ID: mdl-36497831

ABSTRACT

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.


Subject(s)
Accidents, Traffic , Thoracic Injuries , Humans , Aged , Abbreviated Injury Scale , Motor Vehicles , Thoracic Injuries/epidemiology , Thoracic Injuries/prevention & control , Databases, Factual
5.
Article in English | MEDLINE | ID: mdl-33918843

ABSTRACT

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.


Subject(s)
Brain Injuries, Traumatic , Wounds and Injuries , Accidents, Traffic , Aged , Brain Injuries, Traumatic/epidemiology , Brain Injuries, Traumatic/etiology , Humans , Middle Aged , Motor Vehicles , Republic of Korea/epidemiology , Retrospective Studies
6.
Traffic Inj Prev ; 19(sup1): S153-S157, 2018 02 28.
Article in English | MEDLINE | ID: mdl-29584483

ABSTRACT

OBJECTIVES: In cases of car-to-person pedestrian traffic crashes (PTCs), the principal issue is determining at what point the car collided with the pedestrian. Accordingly, the objective of the present study was to use the medical records of patients injured in PTCs to investigate the characteristics of crash types and the areas and injury severity and to determine whether there are differences in injuries due to the angle, motion, and position at the point of impact. METHODS: The present study examined 231 PTC patients admitted to the emergency room (ER) between January and December 2014. Electronic medical records from the hospital were used to divide the patient data according to Abbreviated Injury Scale (AIS) codes for injured areas based on sex, age, time of the crash, outcomes after ER treatment, and major symptoms. Among 231 patients, police reports on 67 crash cases, involving 70 people, were obtained with the help of local police departments, and these reports were used to reconstruct details of the actual crash. For statistical analysis, a chi-square test and a one-way analysis of variance calculation were used to compare the Injury Severity Score (ISS) based on groups and stages, with a statistical significance level set to P < .05. RESULTS: With respect to patients who were admitted for PTC, 52.4% were females and 47.6% were males. The frequency of crashes was high in middle-aged and elderly groups, as well as for youths between 10 and 19 years old. With respect to outcomes after ER treatment, discharge to home after symptom improvement was the most common outcome (24.6%). Admissions to the intensive care unit (25.1%) and to the general ward (23.8%) were also high. In terms of major symptoms, the most common injuries were to the head, resulting from a rotatory motion post impact (35.9%), and injuries to the legs, resulting from the impact of a direct collision with an object (25.1%). CONCLUSIONS: This study demonstrated that injuries to the chest and abdomen were the most severe in the fender vault group and head and neck injuries were the most severe in the roof vault group. In particular, the Injury Severity Score was highest in the roof vault group.


Subject(s)
Accidents, Traffic/statistics & numerical data , Pedestrians , Wounds and Injuries/epidemiology , Abbreviated Injury Scale , Adolescent , Adult , Aged , Child , Craniocerebral Trauma/epidemiology , Craniocerebral Trauma/therapy , Emergency Service, Hospital , Female , Humans , Injury Severity Score , Male , Medical Records , Middle Aged , Neck Injuries/epidemiology , Neck Injuries/therapy , Thoracic Injuries/epidemiology , Thoracic Injuries/therapy , Wounds and Injuries/therapy , Young Adult
7.
Traffic Inj Prev ; 19(sup2): S151-S153, 2018.
Article in English | MEDLINE | ID: mdl-30841797

ABSTRACT

OBJECTIVE: The purpose of this study is to investigate the injury patterns of noncatastrophic accidents by individual age groups. METHODS: Data were collected from the Korean In-Depth Accident Study database based on actual accident investigation. The noncatastrophic criteria were classified according to U.S. experts from the Centers for Disease Control and Prevention's recommendations for field triage guidelines of high-risk automobile crash criteria by vehicle intrusions more than 12 in. on occupant sites (including the roof) and more than 18 in. on any site. The Abbreviated Injury Scale (AIS) was used to determine injury patterns for each body region. Severely injured patients were classified as Maximum Abbreviated Injury Scale (MAIS) 3 or higher. RESULTS: In this study, the most significant injury regions were the head and neck, extremities, and thorax. In addition, the incidence of severe injury among elderly patients was nearly 1.6 times higher than that of non-elderly patients. According to age group, injured body regions among the elderly were the thorax, head and neck, and extremities, in that order. For the non-elderly groups, these were head and neck, extremities, and thorax. Severe injury rates were slightly different for the elderly group (head and neck, abdomen) and non-elderly group (thorax, head and neck). CONCLUSIONS: In both age groups, the rate of severe injury is proportional to an increase in crush extent zone. Front airbag deployment may have a relatively significant relationship to severe injuries.


Subject(s)
Accidents, Traffic/statistics & numerical data , Automobile Driving/statistics & numerical data , Automobiles/statistics & numerical data , Wounds and Injuries/epidemiology , Abbreviated Injury Scale , Acceleration/adverse effects , Adult , Age Factors , Aged , Craniocerebral Trauma/epidemiology , Databases, Factual , Female , Humans , Leg Injuries/epidemiology , Male , Thoracic Injuries/epidemiology
8.
Traffic Inj Prev ; 19(sup2): S48-S54, 2018.
Article in English | MEDLINE | ID: mdl-30633556

ABSTRACT

OBJECTIVES: We aimed to analyze factors affecting the severity of mild whiplash-associated disorders (WADs) and to develop a predictive model to evaluate the presence of mild WAD in minor motor vehicle crashes (MVCs). METHODS: We used the Korean In-Depth Accident Study (KIDAS) database, which collects data from 4 regional emergency centers, to obtain data from 2011 to 2017. The Collision Deformation Classification code was obtained as vehicle's damage information, and Abbreviated Injury Scale (AIS), Maximum Abbreviated Injury Scale (MAIS), and Injury Severity Score (ISS) were used as occupant's injury information. The degree of WAD was determined using the Quebec Task Force (QTF) classification, comprised of 5 stages (QTF 0-4), depending on the occupant's pain and the physician's findings. QTF 1 was defined as mild WAD, and we used QTF 0 to define those who were uninjured. For KIDAS data between 2011 and 2016, a logistic regression model was used to identify factors affecting the occurrence of mild WAD and a predictive model was constructed. Internal validity was estimated using random bootstrapping, and external validity was evaluated by applying 2017 KIDAS data. Of the 2,629 occupants in the KIDAS database from 2011 to 2016, after applying several exclusion conditions, 459 occupants were used to develop the predictive model. The external validity of the derived predictive model was assessed using the 13 MVC occupants from the 2017 KIDAS database meeting our inclusion criteria. Among the 137 MVC occupants from the 2017 KIDAS database for analysis of the external validity of the derived predictive model, the predictive model was verified for 13 MVC occupants. RESULTS: Logistic regression analysis was used to derive a predictive model based on sex, age, body mass index, type of vehicle, belt status, seating row, crush type, and crush extent. This predictive model had an explanatory power of 65.5% to determine an actual QTF of 0 and 1 (c-statistics: 0.655). As a result of the external validity analysis of the predictive model using data from the 2017 KIDAS database (N = 13), sensitivity, specificity, and accuracy were 0.500, 0.857, and 0.692, respectively. CONCLUSIONS: Using the predictive model, the results of the external validity analysis showed low sensitivity but high specificity. This predictive model provided meaningful results, with a high success rate for determining no injury to an occupant. Given our study results, future research is needed to create a more accurate predictive model that includes relevant technical and sociological factors.


Subject(s)
Accidents, Traffic/statistics & numerical data , Motor Vehicles , Whiplash Injuries/epidemiology , Abbreviated Injury Scale , Adult , Databases, Factual , Female , Humans , Incidence , Injury Severity Score , Male , Middle Aged , Models, Theoretical , Republic of Korea/epidemiology , Risk Factors , Whiplash Injuries/etiology , Young Adult
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