Development of a concise injury severity prediction model for pediatric patients involved in a motor vehicle collision

Abstract

Objective

Transporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients.

Methods

Crash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p < 0.05). A reduced variable set model was derived using this information.

Results

The baseline models performed well (ISS ≥ 16: AUC 0.91 [95% CI: 0.86-0.95], TIL: AUC 0.90 [95% CI: 0.86-0.94]). Using BMA, the rank of the importance of the predictors was identical for both ISS ≥ 16 and TIL. There was no statistically significant decrease in accuracy until the models were reduced to fewer than five and six variables for predicting ISS ≥ 16 and TIL, respectively. A reduced variable set model developed using the top five variables (delta-V, entrapment, ejection, restraint use, and near-side collision) to predict ISS ≥ 16 had an AUC 0.90 [95% CI: 0.84-0.96]. Among the models that did not include delta-V, the highest AUC was 0.82 [95% CI: 0.77-0.87].

Conclusions

A succinct logistic regression model can accurately predict severely injured pediatric patients, which could be used for prehospital trauma triage. However, there remains a critical need to obtain delta-V in real-time.

Keywords:

• Pediatrics
• injury prediction
• prehospital
• MVC
• NASS
• CISS

Data availability

The authors of this manuscript are committed to transparency and reproducibility in research. The code files used for this analysis are publicly available at https://github.com/thartka/peds_mvc_triage. This code is made available under GNU General Public License v3.0. To view a copy of this license, visit https://www.gnu.org/licenses/gpl-3.0.en.html. The data can be obtained from the National Highway Traffic Safety Administration (NHTSA) website at https/www.nhtsa.gov. Contact the Center for Injury Biomechanics at Wake Forest for information regarding the Target Injury List.

Disclosure statement

The author(s) have no conflicts of interest to disclose.

Additional information

Funding

The work of the primary author was conducted with the support of the iTHRIV Scholars Program. The iTHRIV Scholars Program is supported in part by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR003015 and KL2TR003016. The Target Injury List was developed under the sponsorship of the National Science Foundation (NSF) Center for Child Injury Prevention Studies at the Children’s Hospital of Philadelphia (CHOP) and Ohio State University (OSU), Toyota Motor Corporation, and Toyota’s Collaborative Safety Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, NSF, or Toyota.