Controversies of prophylactic hypothermia and the emerging use of brain tissue oxygen tension monitoring and decompressive craniectomy in traumatic brain-injured children

Expert Perspectives on Time Sensitivity and a Related Metric for Children Involved in Motor Vehicle Crashes

OBJECTIVE

Advanced Automatic Crash Notification (AACN) uses vehicle telemetry data to predict risk of serious injury among motor vehicle crash occupants and can thus improve the accuracy with which injured children are triaged by first responders. To better define serious injury for AACN systems (which typically use Abbreviated Injury Scale [AIS] metrics), an age-specific approach evaluating severity, time sensitivity (TS), and predictability of injury has been developed. This study outlines the development of the TS score.

METHODS

The 95% most frequent AIS 2+ injuries in a national motor vehicle crash data set spanning 2000 to 2011 were determined for the following age groups: 0 to 4, 5 to 9, 10 to 14, and 15 to 18 years. For each age-specific injury, clinicians with pediatric trauma expertise were asked if treatment at a trauma center was required and were asked about the urgency of treatment. A TS score (range 0-1) was calculated by combining the mean trauma center decision and urgency scores.

RESULTS

A total of 30 to 32 responses were obtained for each age-specific injury. The most frequent motor vehicle crash-induced injuries in the younger groups received significantly higher scores than those in the older groups (median TS score 0 to 4 years: 0.89, 5-9 years: 0.87, 10-14 years: 0.82, 15-18 years: 0.72, P < .001). Large variations in TS existed within each AIS severity level; for example, scores among AIS 2 injuries in 0- to 4-year-olds ranged from 0.12 to 0.98.

CONCLUSIONS

The TS of common pediatric injuries varies on the basis of age and may not be accurately reflected by AIS metrics. AIS may not capture all aspects of injury that should be considered by AACN systems.

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

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.

Mortality Risk in Pediatric Motor Vehicle Crash Occupants: Accounting for Developmental Stage and Challenging Abbreviated Injury Scale Metrics

OBJECTIVE

Survival risk ratios (SRRs) and their probabilistic counterpart, mortality risk ratios (MRRs), have been shown to be at odds with Abbreviated Injury Scale (AIS) severity scores for particular injuries in adults. SRRs have been validated for pediatrics but have not been studied within the context of pediatric age stratifications. We hypothesized that children with similar motor vehicle crash (MVC) injuries may have different mortality risks (MR) based upon developmental stage and that these MRs may not correlate with AIS severity.

METHODS

The NASS-CDS 2000-2011 was used to define the top 95% most common AIS 2+ injuries among MVC occupants in 4 age groups: 0-4, 5-9, 10-14, and 15-18 years. Next, the National Trauma Databank 2002-2011 was used to calculate the MR (proportion of those dying with an injury to those sustaining the injury) and the co-injury-adjusted MR (MRMAIS) for each injury within 6 age groups: 0-4, 5-9, 10-14, 15-18, 0-18, and 19+ years. MR differences were evaluated between age groups aggregately, between age groups based upon anatomic injury patterns and between age groups on an individual injury level using nonparametric Wilcoxon tests and chi-square or Fisher's exact tests as appropriate. Correlation between AIS and MR within each age group was also evaluated.

RESULTS

MR and MRMAIS distributions of the most common AIS 2+ injuries were right skewed. Aggregate MR of these most common injuries varied between the age groups, with 5- to 9-year-old and 10- to 14-year-old children having the lowest MRs and 0- to 4-year-old and 15- to 18-year-old children and adults having the highest MRs (all P <.05). Head and thoracic injuries imparted the greatest mortality risk in all age groups with median MRMAIS ranging from 0 to 6% and 0 to 4.5%, respectively. Injuries to particular body regions also varied with respect to MR based upon age. For example, thoracic injuries in adults had significantly higher MRMAIS than such injuries among 5- to 9-year-olds and 10- to 14-year-olds (P =.04; P <.01). Furthermore, though AIS was positively correlated with MR within each age group, less correlation was seen for children than for adults. Large MR variations were seen within each AIS grade, with some lower AIS severity injuries demonstrating greater MRs than higher AIS severity injuries. As an example, MRMAIS in 0- to 18-year-olds was 0.4% for an AIS 3 radius fracture versus 1.4% for an AIS 2 vault fracture.

CONCLUSIONS

Trauma severity metrics are important for outcome prediction models and can be used in pediatric triage algorithms and other injury research. Trauma severity may vary for similar injuries based upon developmental stage, and this difference should be reflected in severity metrics. The MR-based data-driven determination of injury severity in pediatric occupants of different age cohorts provides a supplement or an alternative to AIS severity classification for pediatric occupants in MVCs.