ArticleBMC pediatrics2023
Development and validation of a pediatric model predicting trauma-related mortality.
Article in BMC pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Development and validation of an interpretable machine-learning model for predicting treatment failure in severe trauma patients.Frontiers in medicine · 2026Article
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Authors and funding
4 authors.
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Abstract
objectivesTo develop a prediction model of mortality in pediatric trauma-based injuries. Our secondary objective was to transform this model into a translational tool for clinical use. STUDY
designA retrospective cohort study of children ≤ 18 years was derived from the National Trauma Data Bank between the years of 2007 to 2015. The goal was to identify clinical or physiologic variables that would serve as predictors for pediatric death. Data was split into a development cohort (80%) to build the model and then tested in an internal validation cohort (20%) and a temporal cohort. The area under the receiver operating characteristic curve (AUC) was assessed for the new model.
resultsIn 693,192 children, the mortality rate was 1.4% (n = 9,785). Most subjects were male (67%), White (65%), and incurred an unintentional injury (92%). The proposed model had an AUC of 96.4% (95% CI: 95.9%-96.9%). In contrast, the Injury Severity Score yielded an AUC of 92.9% (95% CI: 92.2%-93.6%), while the Revised Trauma Score resulted in an AUC of 95.0% (95% CI: 94.4%-95.6%).
conclusionThe TRAGIC + Model (Temperature, Race, Age, GCS, Injury Type, Cardiac-systolic blood pressure + Mechanism of Injury and Sex) is a new pediatric mortality prediction model that leverages variables easily obtained upon trauma admission.
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