Pediatric firearm risk prediction in trauma centers and after discharge: A machine learning analysis

We used machine learning (ML) to develop firearm risk prediction models for injured children and adolescents admitted to U.S. trauma centers, including prediction of death after discharge among survivors of firearm injury.

We used a retrospective cohort of injured patients 0-17 years admitted to 982 trauma centers participating in the National Trauma Data Bank from 1/1/2014 to 12/31/2021, stratified by children (0-10 years) versus adolescents (11-17 years). We followed a subset of patients to one year (through 12/31/2022). We used ML to analyze 119 predictors and a primary outcome of firearm injury, and then 141 predictors to model a secondary outcome of death after discharge.

There were 260,098 children (2785 [1.1 %] with firearm injuries) and 242,669 adolescents (22,318 [9.2 %] with firearm injuries). For children, the high specificity model had an area under the curve (AUC) 0.787 with 31.2 % sensitivity, specificity 95.3 %, and positive predictive value (PPV) 6.9 %. For adolescents, the model had an AUC 0.763, sensitivity 34.7 %, specificity 95.0 %, and PPV 41.3 %. High-yield predictors included Black race, Child Opportunity Index, and median household income. Post-discharge mortality among those surviving a firearm injury was 0.38 %, but deaths often occurred within one week of discharge, frequently from a repeat firearm injury, and were predicted by home neighborhood characteristics.

Pediatric firearm injury risk stratification can be performed using information available during admission, which could guide injury prevention efforts. The first week after discharge following a firearm injury is high-risk for mortality, with risk influenced by neighborhood characteristics.