• Craig D Newgard, Sean Babcock, Susan Malveau, Amber Lin, Jason Goldstick, Patrick Carter, CookJennifer N BJNBFrom the Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR., Xubo Song, Ran Wei, Apoorva Salvi, Mary E Fallat, Nathan Kuppermann, Peter C Jenkins, Joel A Fein, and N Clay Mann.
• From the Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR.
• Pediatr Emerg Care. 2024 Dec 12.

ObjectiveAmong children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data sources.MethodsWe included children and adolescents 0-17 years transported by ambulance to acute care hospitals in 47 states from January 1, 2014 through December 31, 2022. We used 96 predictors, including basic demographic information and neighborhood measures matched to home ZIP code from 5 data sources: EMS records, American Community Survey, Child Opportunity Index, County Health Rankings, and Social Vulnerability Index. We separated children into 0-10 years (preadolescent) and 11-17 years (adolescent) cohorts and used machine learning to develop high-specificity risk prediction models for each age group to minimize false positives.ResultsThere were 6,191,909 children transported by ambulance, including 21,625 (0.35%) with firearm injuries. Among children 0-10 years (n = 3,149,430 children, 2,840 [0.09%] with firearm injuries), the model had 95.1% specificity, 22.4% sensitivity, area under the curve 0.761, and positive predictive value 0.41% for identifying children with firearm injuries. Among adolescents 11-17 years (n = 3,042,479 children, 18,785 [0.62%] with firearm injuries), the model had 94.8% specificity, 39.0% sensitivity, area under the curve 0.818, and positive predictive value 4.47% for identifying patients with firearm injury. There were 7 high-yield predictors among children and 3 predictors among adolescents, with little overlap.ConclusionsAmong pediatric patients transported by ambulance, basic demographic information and neighborhood measures can identify children and adolescents at elevated risk of firearm injuries, which may guide focused injury prevention resources and interventions.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

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