Abstract
Pelvic fractures have been identified as the second most common AIS2+ injury in motor vehicle crashes, with the highest early mortality rate compared to other orthopaedic injuries. Further, the risk is associated with occupant sex, age, stature and body mass index (BMI). In this study, clinical pelvic CT scans from 132 adults (75 females, 57 males) were extracted from a patient database. The population shape variance in pelvis bone geometry was studied by Sparse Principal Component Analysis (SPCA) and a morphometric model was developed by multivariate linear regression using overall anthropometric variables (sex, age, stature, BMI). In the analysis, SPCA identified 15 principal components (PCs) describing 83.6% of the shape variations. Eight of these were significantly captured (α < 0.05) by the morphometric model, which predicted 29% of the total variance in pelvis geometry. The overall anthropometric variables were significantly related to geometrical features primarily in the inferior-anterior regions while being unable to significantly capture local sacrum features, shape and position of ASIS and lateral tilt of the iliac wings. In conclusion, a new detailed morphometric model of the pelvis bone demonstrated that overall anthropometric variables account for only 29% of the variance in pelvis geometry. Furthermore, variations in the superior-anterior region of the pelvis, with which the lap belt is intended to interact, were not captured. Depending on the scenario, shape variations not captured by overall anthropometry could have important implications for injury prediction in traffic safety analysis.
Highlights
Road traffic injuries are the eighth leading cause of death globally and the leading cause of death in the age-group 5–29 (WHO – Road traffic injuries, 2020)
Real-life motor vehicle crashes (MVCs) data has shown that pelvic fracture risk is associated with occupant sex, age, stature and body mass index (BMI) (Melocchi et al, 2010; Schiff et al, 2008; Sochor et al, 2003; Stein et al, 2006; Sunne vång et al, 2015)
The present study aims to: (1) Describe the shape of the pelvic bones using Sparse Principal Component Analysis (SPCA), (2) generate an associated morphometric model of the pelvic bones using overall anthropometry as independent variables and (3) identify the local features that are significantly captured by the overall anthropometry
Summary
Road traffic injuries are the eighth leading cause of death globally and the leading cause of death in the age-group 5–29 (WHO – Road traffic injuries, 2020). Prevention of these injuries is listed as one of the UN sustainable development goals (UN – SDGs, 2015). Real-life MVC data has shown that pelvic fracture risk is associated with occupant sex, age, stature and BMI (Melocchi et al, 2010; Schiff et al, 2008; Sochor et al, 2003; Stein et al, 2006; Sunne vång et al, 2015). Considering future autonomous vehicles and seating positions the pelvis injury risk might be accentuated. Reclined seating positions creates less benefi cial interactions between the pelvis and the lap-belt, increasing the risk for submarining (Rawska et al, 2020)
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