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Background: Firearm-related injury in children is a public health crisis. The Social Vulnerability Index (SVI) identifies communities at risk for adverse effects due to natural or human-caused crises. We sought to determine if SVI was associated with pediatric firearm-related injury and thus could assist in prevention planning., Methods: The Centers for Disease Control and Prevention's 2018 SVI data were merged on census tract with 2015 to 2022 open-access shooting incident data in children 19 years or younger from Baltimore, Chicago, Los Angeles, New York City, and Philadelphia. Regression analyses were performed to uncover associations between firearm violence, SVI, SVI themes, and social factors at the census tract level., Results: Of 11,654 shooting incidents involving children, 52% occurred in just 6.7% of census tracts, which were on average in the highest quartile of SVI. A decile increase in SVI was associated with a 45% increase in pediatric firearm-related injury in all cities combined (incidence rate ratio, 1.45; 95% confidence interval, 1.41-1.49; p < 0.001). A similar relationship was found in each city: 30% in Baltimore, 51% in Chicago, 29% in Los Angeles, 37% in New York City, and 35% in Philadelphia (all p < 0.001). Socioeconomic status and household composition were SVI themes positively associated with shootings in children, as well as the social factors below poverty, lacking a high school diploma, civilian with a disability, single-parent household, minority, and no vehicle access. Living in areas with multi-unit structures, populations 17 years or younger, and speaking English less than well were negatively associated., Conclusion: Geospatial disparities exist in pediatric firearm-related injury and are significantly associated with neighborhood vulnerability. We demonstrate a strong association between SVI and pediatric shooting incidents in multiple major US cities. Social Vulnerability Index can help identify social and structural factors, as well as geographic areas, to assist in developing meaningful and targeted intervention and prevention efforts., Level of Evidence: Prognostic and Epidemiological; Level III., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Firearm violence in the United States is a public health crisis, but accessing accurate firearm assault data to inform prevention strategies is a challenge. Vulnerability indices have been used in other fields to better characterize and identify at-risk populations during crises, but no tool currently exists to predict where rates of firearm violence are highest. We sought to develop and validate a novel machine-learning algorithm, the Firearm Violence Vulnerability Index (FVVI), to forecast community risk for shooting incidents, fill data gaps, and enhance prevention efforts., Methods: Open-access 2015 to 2022 fatal and nonfatal shooting incident data from Baltimore, Boston, Chicago, Cincinnati, Los Angeles, New York City, Philadelphia, and Rochester were merged on census tract with 30 population characteristics derived from the 2020 American Community Survey. The data set was split into training (80%) and validation (20%) sets; Chicago data were withheld for an unseen test set. XGBoost, a decision tree-based machine-learning algorithm, was used to construct the FVVI model, which predicts shooting incident rates within urban census tracts., Results: A total of 64,909 shooting incidents in 3,962 census tracts were used to build the model; 14,898 shooting incidents in 766 census tracts were in the test set. Historical third grade math scores and having a parent jailed during childhood were population characteristics exhibiting the greatest impact on FVVI's decision making. The model had strong predictive power in the test set, with a goodness of fit ( D2 ) of 0.77., Conclusion: The Firearm Violence Vulnerability Index accurately predicts firearm violence in urban communities at a granular geographic level based solely on population characteristics. The Firearm Violence Vulnerability Index can fill gaps in currently available firearm violence data while helping to geographically target and identify social or environmental areas of focus for prevention programs. Dissemination of this standardized risk tool could also enhance firearm violence research and resource allocation., Level of Evidence: Prognostic and Epidemiological; Level IV., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Shock index, pediatric age adjusted (SIPA), has been widely applied in pediatric trauma but has limited precision because of the reference ranges used in its derivation. We hypothesized that a pediatric shock index (PSI) equation based on age-based vital signs would outperform SIPA., Methods: A retrospective cohort of trauma patients aged 1 to 18 years from Trauma Quality Programs - Participant Use File 2010 to 2018 was performed. A random 70% training subset was used to derive Youden index-optimizing shock index (SI) cutoffs by age for blood transfusion within 4 hours. We used linear regression to derive equations representing the PSI cutoff for children 12 years or younger and 13 years or older. For children 13 years or older, the well-established SI of 0.9 remained optimal, consistent with SIPA and other indices. For children 12 years or younger in the 30% validation subset, we compared our age-based PSI to SIPA as predictors of early transfusion, mortality, pediatric intensive care unit admission, and injury severity score of ≥25. For bedside use, a simplified "rapid" pediatric shock index (rPSI) equation was also derived and compared with SIPA., Results: A total of 439,699 patients aged 1 to 12 years met the inclusion criteria with 2,718 (1.3% of those with available outcome data) requiring transfusion within 4 hours of presentation. In the validation set, positive predictive values for early transfusion were higher for PSI (8.3%; 95% confidence interval [CI], 7.5-9.1%) and rPSI (6.3%; 95% CI, 5.7-6.9%) than SIPA (4.3%; 95% CI, 3.9-4.7%). For early transfusion, negative predictive values for both PSI (99.3%; 95% CI, 99.2-99.3%) and rPSI (99.3%; 95% CI, 99.2-99.4%) were similar to SIPA (99.4%; 95% CI, 99.3-99.4%)., Conclusion: We derived the PSI and rPSI for use in pediatric trauma using empiric, age-based SI cutoffs. The PSI and rPSI achieved higher positive predictive values and similar negative predictive values to SIPA in predicting the need for early blood transfusion and mortality., Level of Evidence: Prognostic/Epidemiological; level III., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The equivalent Injury Severity Score (ISS) cutoffs for severe trauma vary between adult (ISS, >16) and pediatric (ISS, >25) trauma. We hypothesized that a novel injury severity prediction model incorporating age and mechanism of injury would outperform standard ISS cutoffs., Methods: The 2010 to 2016 National Trauma Data Bank was queried for pediatric trauma patients. Cut point analysis was used to determine the optimal ISS for predicting mortality for age and mechanism of injury. Linear discriminant analysis was implemented to determine prediction accuracy, based on area under the curve (AUC), of ISS cutoff of 25 (ISS, 25), shock index pediatric adjusted (SIPA), an age-adjusted ISS/abbreviated Trauma Composite Score (aTCS), and our novel Trauma Composite Score (TCS) in blunt trauma. The TCS consisted of significant variables (Abbreviated Injury Scale, Glasgow Coma Scale, sex, and SIPA) selected a priori for each age., Results: There were 109,459 blunt trauma and 9,292 penetrating trauma patients studied. There was a significant difference in ISS (blunt trauma, 9.3 ± 8.0 vs. penetrating trauma, 8.0 ± 8.6; p < 0.01) and mortality (blunt trauma, 0.7% vs. penetrating trauma, 2.7%; p < 0.01). Analysis of the entire cohort revealed an optimal ISS cut point of 25 (AUC, 0.95; sensitivity, 0.86; specificity, 0.95); however, the optimal ISS ranged from 18 to 25 when evaluated by age and mechanism. Linear discriminant analysis model AUCs varied significantly for each injury metric when assessed for blunt trauma and penetrating trauma (penetrating trauma-adjusted ISS, 0.94 ± 0.02 vs. ISS 25, 0.88 ± 0.02 vs. SIPA, 0.62 ± 0.03; p < 0.001; blunt trauma-adjusted ISS, 0.96 ± 0.01 vs. ISS 25, 0.89 ± 0.02 vs. SIPA, 0.70 ± 0.02; p < 0.001). When injury metrics were assessed across age groups in blunt trauma, TCS and aTCS performed the best., Conclusion: Current use of ISS in pediatric trauma may not accurately reflect injury severity. The TCS and aTCS incorporate both age and mechanism and outperform standard metrics in mortality prediction in blunt trauma., Level of Evidence: Retrospective review, level IV., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Injury Severity Score (ISS) is a widely used metric for trauma research and center verification; however, it does not account for age-related physiologic parameters. We hypothesized that a novel age-based injury severity metric would better predict mortality., Methods: Adult patients (≥18 years) sustaining blunt trauma (BT) or penetrating trauma (PT) were abstracted from the 2010 to 2016 National Trauma Data Bank. Admission vitals, Glasgow Coma Scale, ISS, mechanism, and outcomes were analyzed. Patients with incomplete/non-physiologic vital signs were excluded. For each age: (1) a cut point analysis was used to determine the ISS with the highest specificity and sensitivity for predicting mortality and (2) a linear discriminant analysis was performed using ISS, ISS greater than 16, Trauma and Injury Severity Score, and Revised Trauma Scale to compare each scoring system's mortality prediction. A novel injury severity metric, the trauma component score (TCS), was developed for each age using significant (p < 0.05) variables selected from Abbreviated Injury Scale scores, Glasgow Coma Scale, vital signs, and gender. Receiver operator curves were developed and the areas under the curve were compared between the TCS and other systems., Results: There 777,794 patients studied (BT, 91.1%; PT, 8.9%). Blunt trauma patients were older (53.6 ± 21.3 years vs. 34.4 ± 13.8 years), had higher ISS scores (11.1 ± 8.5 vs. 8.5 ± 8.9), and lower mortality (2.9% vs. 3.4%) than PT patients (p < 0.05). When assessing the entire PT and BT cohort the optimal ISS cut point was 16. The optimal ISS was between 20 and 25 for BT younger than 70 years. For those older than 70 years, the optimal BT ISS steadily declined as age increased PT's cut point was 16 or less for all ages assessed. When the injury metrics were compared by area under the curve, our novel TCS more accurately predicted mortality across all ages in both BT and PT (p < 0.001)., Conclusion: Injury Severity Score is a poor mortality predictor in older patients and those sustaining penetrating trauma. The age-based TCS is a superior metric for mortality prediction across all ages., Level of Evidence: Clinical outcomes, Level IV., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)