No abstract

BACKGROUND:Trauma team activation leveling decisions are complex and based on many variables. Accurate triage decisions improve patient safety and resource utilization. Our purpose was to establish proof-of-concept for using principal component analysis (PCA) to identify multivariate predictors of injury severity and to assess their ability to predict outcomes in pediatric trauma patients. We hypothesized that we could identify significant principal components (PCs) among variables used for decisions regarding trauma team activation and that PC scores would be predictive of outcomes in pediatric trauma. METHODS:We conducted a retrospective review of the trauma registry (January 2014 to December 2020) at our pediatric trauma center, including all pediatric patients (age <18 years) who triggered a trauma team activation. Data included patient demographics, prehospital report, Injury Severity Score, and outcomes. Four significant principal components were identified using PCA. Differences in outcome variables between the highest and lowest quartile for PC score were examined. RESULTS:There were 1,090 pediatric patients included. The four significant PCs accounted for greater than 96% of the overall data variance.The first PC was a composite of prehospital Glasgow Coma Scale and Revised Trauma Score and was predictive of outcomes, including injury severity, length of stay, and mortality. The second PC was characterized primarily by prehospital systolic blood pressure and high PC scores were associated with increased length of stay. The third and fourth PCs were characterized by patient age and by prehospital Revised Trauma Score and systolic blood pressure, respectively. CONCLUSION:We demonstrate that, using information available at the time of trauma team activation, PCA can be used to identify key predictors of patient outcome. While the ultimate goal is to create a machine learning-based predictive tool to support and improve clinical decision making, this study serves as a crucial step toward developing a deep understanding of the features of the model and their behavior with actual clinical data.