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Abstract
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OBJECTIVE: Appropriate treatment at designated trauma centers (TCs) improves outcomes among injured children after motor vehicle crashes (MVCs). Advanced Automatic Crash Notification (AACN) has shown promise in improving triage to appropriate TCs. Pediatric-specific AACN algorithms have not yet been created. To create such an algorithm, it will be necessary to include some metric of development (age, height or weight) as a covariate in the injury risk algorithm. This study sought to determine which marker of development should serve as a covariate in such an algorithm and to quantify injury risk at different levels of this metric.
METHODS: Retrospective review of occupants <19yrs within the MVC dataset NASS-CDS 2000-2011 was performed. R(2) values of logistic regression models using age, height or weight to predict 18 key injury types were compared to determine which metric should be used as a covariate in a pediatric AACN algorithm. Clinical judgment, literature review and Chi Square analysis were used to create groupings of the chosen metric that would discriminate injury patterns. Adjusted odds of particular injury types at the different levels of this metric were calculated from logistic regression while controlling for gender, vehicle velocity change (delta V), belted status (optimal, sub-optimal or unrestrained) and crash mode (rollover, rear, frontal, near-side or far-side).
RESULTS: NASS-CDS analysis produced 11,541 occupants <19yrs with non-missing data. Age, height and weight were correlated with one another and with injury patterns. Age demonstrated the best predictive power in injury patterns and was categorized into bins of 0-4 years, 5-9 years, 10-14 years and 15-18 years. Age was a significant predictor of all 18 injury types evaluated even when controlling for all other confounders and when controlling for age and gender specific BMI classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining AIS 2+ and 3+ head injuries and AIS 3+ spinal injuries, while older children were at increased odds of sustaining thoracic fractures, AIS 3+ abdominal injuries, and AIS 2+ upper and lower extremity injuries.
CONCLUSIONS: The injury patterns observed across developmental metrics in this study mirror those previously described among children with blunt trauma. This study identifies age as the metric best suited for use in a pediatric AACN algorithm and utilizes 12 years of data to provide quantifiable risks of particular injuries at different levels of this metric. This risk quantification will have important predictive purposes in a pediatric-specific AACN algorithm.
Language: en |