Evaluation of developmental metrics for utilization in a pediatric advanced automatic crash notification algorithm

ABSTRACTObjective: 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: A retrospective review of occupants age < 19 years within the MVC data set NASS-CDS 2000–2011 was performed. R2 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 a...