Related Experiment Video
Updated: Apr 11, 2026
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Evaluation of developmental metrics for utilization in a pediatric advanced automatic crash notification algorithm
Andrea N Doud1,2, Ashley A Weaver3, Jennifer W Talton4
1a Wake Forest School of Medicine , Department of General Surgery , Winston-Salem , North Carolina.
Insights
Age is the best developmental metric for pediatric Advanced Automatic Crash Notification (AACN) algorithms, predicting injury risk in motor vehicle crashes (MVCs). This research quantifies injury patterns by age to improve trauma center triage for children.
Area of Science:
- Pediatric trauma research
- Injury biomechanics
- Public health and safety
Background:
- Motor vehicle crashes (MVCs) are a leading cause of injury in children.
- Timely triage to designated trauma centers (TCs) significantly improves outcomes for injured children.
- Advanced Automatic Crash Notification (AACN) systems show potential for optimizing prehospital triage, but pediatric-specific algorithms are lacking.
Purpose of the Study:
- To determine the optimal developmental metric (age, height, or weight) for a pediatric AACN injury risk algorithm.
- To quantify the relationship between the chosen developmental metric and specific injury types in children involved in MVCs.
Main Methods:
- Retrospective analysis of 11,541 child occupants (<19 years) from the NASS-CDS dataset (2000-2011).
- Logistic regression models compared the predictive power of age, height, and weight for 18 injury types.
- Age was selected as the covariate, categorized into age bins (0-4, 5-9, 10-14, 15-18 years).
- Adjusted odds of specific injuries were calculated, controlling for gender, delta V, and restraint use.
Main Results:
- Age demonstrated the highest predictive power for injury patterns compared to height and weight.
- Age was a significant predictor for all 18 evaluated injury types, even after controlling for confounders.
- Younger children (<5 years) had higher odds of head and spinal injuries.
- Older children (15-18 years) had higher odds of thoracic, abdominal, and extremity injuries.
Conclusions:
- Age is the most suitable developmental metric for pediatric AACN algorithms.
- Quantifiable injury risks associated with different age groups provide a basis for improved predictive capabilities.
- This age-based risk quantification is crucial for developing effective pediatric-specific AACN systems to enhance trauma care for injured children.
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:
A retrospective review of occupants age < 19 years within the MVC data set 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, suboptimal, or unrestrained), and crash mode (rollover, rear, frontal, near-side, or far-side).
Results:
NASS-CDS analysis produced 11,541 occupants age < 19 years with nonmissing 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 body mass index (BMI) classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining Abbreviated Injury Scale (AIS) 2+ and 3+ head injuries and AIS 3+ spinal injuries, whereas 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.
