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Crash data from the International Center of Automotive Medicine (ICAM) database, with analytic morphomics, were used to evaluate thoracolumbar spine fractures for obese occupants in frontal crashes. Two BMI (Body Mass Index) groups (non-obese and obese) with a maximum abbreviated injury scale (MAIS) in the spine region of ≥2 (MAIS_6S 2+) were categorised and compared. The fracture types were assessed based on AIS for each occupant. Univariate analyses were conducted to investigate the association between analytic morphomics measures and thoracolumbar spine fracture. The results indicate that MAIS 2+ injury occurred mainly in severe crashes with high delta-V and large intrusion. Transverse process fractures were the most common AIS 2+ fractures, followed by minor compression type fractures (≤ 20% anterior height). Compared to the non-obese occupants, the majority of obese occupants sustained transverse process fractures at lumbar vertebra with a higher incidence ratio. A statistical analysis was conducted, using vehicle, demographic, and morphomic variables, to explain the difference between transverse process fractures and vertebra body compression fractures. Transverse process fractures were related to BMI and vehicle factors (intrusion) in the obese group. In addition, morphomics related to fat distribution, muscle area, and cortical bone density are the major difference between non-obese and obese occupants.

Background The "Cushion Effect," the phenomenon in which obesity protects against abdominal injury in adults in motor vehicle accidents, has not been evaluated among pediatric patients. This work evaluates the association between subcutaneous fat cross-sectional area, quantified using analytic morphomic techniques and abdominal injury. Methods This retrospective study includes 119 patients aged 1 to 18years involved in frontal impact motor vehicle accidents (2003–2015) with computed tomography scans. Subcutaneous fat cross-sectional area was measured and converted to age- and gender-adjusted percentiles from population-based normative data. Multivariable analysis determined the risk of the primary outcome, Maximum Abbreviated Injury Scale (MAIS) 2+ abdominal injury, after adjusting for age, weight, seatbelt status, and impact rating. Results MAIS 2+ abdominal injuries occurred in 20 (16.8%) of the patients. Subcutaneous fat area percentile was not significantly associated with MAIS 2+ abdominal injury on multivariable logistic regression (adjusted Odds Ratio, 0.86; 95% CI, 0.72–1.03; p=0.10). Discussion The "cushion effect" was not apparent among pediatric frontal motor vehicle crash victims in this study. Future work is needed to investigate other analytic morphomic measures. By understanding how body composition relates to injury patterns, there is a unique opportunity to improve vehicle safety design. Level of Evidence Prognosis Study, Level III.

Background/purpose Analytic morphomics is being used to identify 3-D biologic measures with superior clinical utility and risk stratification over traditional factors such as age, height, and weight. The purpose of this study is to define age and gender specific Pediatric Reference Analytic Morphomics Population (PRAMP™) growth charts. Methods This retrospective study population contains 2591 individual CT scans of a normative reference population of males and females (1–20years old). Growth curves were constructed at the 5th, 25th, 50th, 75th, and 95th quantiles for morphomic variables, including psoas muscle area, trabecular bone density, and visceral fat area by age and gender. Results Total psoas muscle area increases over time until late adolescence. Trabecular bone density remains stable until adolescence, decreases during adolescence, and increases in young adulthood. Visceral fat area increases over time with greater variation between the 5th and 95th percentile with increasing age. Conclusions The PRAMP™ data have been used to construct age- and sex-specific reference growth curves. This may be used to better define "abnormal" in efforts to create unique risk-categorization algorithms specific to particular clinical and global health investigations. Level of evidence Level II.

Abdominal injuries resulting from vehicle crashes can be significant, in particular when undetected. In this study, abdominal injuries for occupants involved in frontal impacts were assessed using crash and medical data.Injury rates and patterns were first assessed with respect to thoracic injuries. A statistical analysis was then conducted to predict abdominal injury outcome using 18 covariate variables, including 4 vehicle, 4 demographic, and 10 morphomic, derived from computed tomography (CT) scans. More than 260,000 logistic regression models were fitted using all possible variable combinations. The models were ranked using the Akaike information criterion (AIC) and combined through the model-averaging approach to produce the optimal predictive model. The performance of the models was then assessed using the area under the curve (AUC).The rate of serious thoracic injury was 2.49 times higher than the rate of abdominal injury. The associated odds ratio was 2.31 (P.01). These results suggest a strong association between serious abdominal and thoracic injuries. The optimal model AUC was 0.646 when using solely vehicle data, 0.696 when combining vehicle and demographic data, 0.866 when combining vehicle and morphomic data, and 0.879 when combining vehicle, demographic, and morphomic data. These results suggest that morphomic variables better predict abdominal injury outcomes than demographic variables. The most important morphomics variables included visceral fat area, trabecular bone density, and spine angulation.This study is the first to combine vehicle, demographic, and anatomical data to predict abdominal injury rates in frontal crashes.

Background: The pattern and severity of crash injury depends on a complex interaction of biomechanical factors such as deceleration velocity at impact (delta-V), seat-belt and airbag use, and type of impact. Human body characteristics such as height and weight may play an important role. We hypothesized that body mass index (BMI) will influence crash injury patterns. Methods: The University of Michigan Program for Injury Research and Education database was queried. Three cohorts were analyzed, lean (BMI ≤ 25 kg/ m 2 ), overweight (BMI 25-30 kg/m 2 ), and obese (BMI > 30 kg/m 2 ) Results: There were 189 detailed crash cases, with 22 fatalities. There was an increased risk of fatal outcome associated with the obese cohort (adjusted odds ratio, 4.2 compared with lean; p = 0.04). Age, delta-V, seat-belt use, and type of impact were independent predictors of Injury Severity Score (ISS). After adjusting for other modifiers, being overweight was associated with decreased ISS (p = 0.03) and abdominal maximal Abbreviated Injury Scale (mAIS) score (p = 0.008) when compared with the lean cohort. However, the lower extremity mAIS score increased when overweight (p = 0.03) and obese cohorts (p = 0.001) were compared with the lean cohort. Conclusion: Although no difference in ISS was identified between the lean and obese cohorts, there was an increase in mortality with the obese cohort. The severity of lower extremity injuries increased with increasing BMI. The overweight cohort was associated with lower ISS and abdominal mAIS score compared with the lean cohort. This protection may be attributable to an increase in insulating tissue, or a cushion effect, without a significant increase in mass and momentum.

This study resulted in a model-averaging methodology that predicts crash injury risk using vehicle, demographic, and morphomic variables and assesses the importance of individual predictors. The effectiveness of this methodology was illustrated through analysis of occupant chest injuries in frontal vehicle crashes. The crash data were obtained from the International Center for Automotive Medicine (ICAM) database for calendar year 1996 to 2012. The morphomic data are quantitative measurements of variations in human body 3-dimensional anatomy. Morphomics are obtained from imaging records. In this study, morphomics were obtained from chest, abdomen, and spine CT using novel patented algorithms. A NASS-trained crash investigator with over thirty years of experience collected the in-depth crash data. There were 226 cases available with occupants involved in frontal crashes and morphomic measurements. Only cases with complete recorded data were retained for statistical analysis. Logistic regression models were fitted using all possible configurations of vehicle, demographic, and morphomic variables. Different models were ranked by the Akaike Information Criteria (AIC). An averaged logistic regression model approach was used due to the limited sample size relative to the number of variables. This approach is helpful when addressing variable selection, building prediction models, and assessing the importance of individual variables. The final predictive results were developed using this approach, based on the top 100 models in the AIC ranking. Model-averaging minimized model uncertainty, decreased the overall prediction variance, and provided an approach to evaluating the importance of individual variables. There were 17 variables investigated: four vehicle, four demographic, and nine morphomic. More than 130,000 logistic models were investigated in total. The models were characterized into four scenarios to assess individual variable contribution to injury risk. Scenario 1 used vehicle variables; Scenario 2, vehicle and demographic variables; Scenario 3, vehicle and morphomic variables; and Scenario 4 used all variables. AIC was used to rank the models and to address over-fitting. In each scenario, the results based on the top three models and the averages of the top 100 models were presented. The AIC and the area under the receiver operating characteristic curve (AUC) were reported in each model. The models were re-fitted after removing each variable one at a time. The increases of AIC and the decreases of AUC were then assessed to measure the contribution and importance of the individual variables in each model. The importance of the individual variables was also determined by their weighted frequencies of appearance in the top 100 selected models. Overall, the AUC was 0.58 in Scenario 1, 0.78 in Scenario 2, 0.76 in Scenario 3 and 0.82 in Scenario 4. The results showed that morphomic variables are as accurate at predicting injury risk as demographic variables. The results of this study emphasize the importance of including morphomic variables when assessing injury risk. The results also highlight the need for morphomic data in the development of human mathematical models when assessing restraint performance in frontal crashes, since morphomic variables are more "tangible" measurements compared to demographic variables such as age and gender.

The size and shape of the acetabulum and of the femoral head influence the injury tolerance of the hip joint. The aim of this study is to quantify changes in acetabular cup geometry that occur with age, gender, height, and weight. Anonymized computed tomography (CT) scans of 1,150 individuals 16+ years of age, both with and without hip trauma, were used to describe the acetabular rim with 100 equally spaced points. Bilateral measurements were taken on uninjured patients, while only the uninjured side was valuated in those with hip trauma. Multinomial logistic regression found that after controlling for age, height, weight, and gender, each 1 degree decrease in acetabular anteversion angle (AAA) corresponded to an 8 percent increase in fracture likelihood (p

Every year in the US, over 40,000 individuals are killed and more than 3 million others injured as a result of motor vehicle collisions (MVCs). The economic cost of crash injuries each year is greater than $230 billion. In order to improve vehicle safety, a better understanding of the mechanism by which injuries are caused in current crashes as well as the human factors that affect injury tolerance is required. Biomechanical studies using cadavers suggest that factors such as bone density, muscle mass and body geometry influence the body’s ability to tolerate injurious forces. We hypothesized that fat in both the subcutaneous and visceral compartments may act as an energy absorbing material and affect injury patterns in MVCs. This study examined the relationship between the severity of the injuries sustained in MVCs and that occupant’s subcutaneous and visceral fat volumes as measured by CT. 139 adults MVC occupants were studied. Field and vehicle inspection was performed to determine crash configuration, crash severity and restraint use. All of the patients received CT evaluation of their abdomen and pelvis as part of their trauma workup. A three-dimensional image of the abdomen was reconstructed and volumetric measurement of both the subcutaneous and visceral fat were performed on a two-inch slab at the level of the third lumbar vertebra. The results showed that increased volume of either subcutaneous or visceral fat correlated with a significant decrease in the overall Injury Severity Score (ISS) (p