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: CISS; MVC; NASS; Pediatrics; injury prediction; prehospital.

Figure 1

Flowchart of patients included in this analysis. [NASS: National Automotive Sampling System, CISS: Crash Injury Surveillance System, Delta-V:change in velocity, PDOF: principal direction of force]

Figure 2 –

Variable importance based on Bayesian model averaging. Higher posterior probability indicates greater variable importance. [Delta-V: change in velocity, PDOF: principal direction of force]

Figure 3 –

Maximum perform bases on the number of variables in the model. The gray bands represent the 95% confidence interval. The vertical gray lines indicate where the model performance began to be statistically significantly different that the baseline model with all predictors (p<0.05). [ISS: Injury Severity Score]