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Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering.
Ma, Zhengwei; Zhang, Liming; Qiu, Changren; Xu, Gang; Liang, Ziyang; Wei, Wei.
Afiliação
  • Ma Z; College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China.
  • Zhang L; College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China.
  • Qiu C; College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China.
  • Xu G; College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China.
  • Liang Z; Department of Spine Manipulation, Shenzhen Traditional Chinese Medicine Hospital, 4th Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
  • Wei W; LBAUMRT24, Aix Marseille Université/ Université Gustave Eiffel, Marseille, France.
Traffic Inj Prev ; : 1-10, 2024 Oct 02.
Article em En | MEDLINE | ID: mdl-39356660
ABSTRACT

OBJECTIVES:

The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics.

METHODS:

A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables.

RESULTS:

This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25-59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors.

CONCLUSIONS:

Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Traffic Inj Prev Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Traffic Inj Prev Ano de publicação: 2024 Tipo de documento: Article