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Background Resuscitative endovascular balloon occlusion of the aorta (REBOA) has been an established life-saving procedure for adult trauma patients, but the evidence for its use in pediatric patients is still under question. The purpose of this study was to examine the outcome of REBOA in pediatric patients.Methods We retrospectively analyzed observational cohort data from the American College of Surgeons–Trauma Quality Improvement Program from 2017 to 2019. We analyzed 183 506 trauma patients aged 7–18, and 111 patients were matched by propensity score analysis. Basic demographics, injury severity, trauma type, and clinical outcomes of the patients receiving REBOA and those not receiving REBOA were compared. In the REBOA patients, a subgroup analysis was performed to evaluate the potential influence of age and body weight on the outcomes of REBOA.Results After the pretreatment factors were balanced for the REBOA and no-REBOA groups, the patients in the REBOA group had more transfused packed red blood cells within the first 4 hours (3250 mL vs. 600 mL, p15 years old/≤15 years old) or weight (>58 kg or ≤58 kg).Conclusions Pediatric trauma patients who received REBOA were not significantly associated with an increased risk of mortality when compared with no-REBOA patients with matched basic demographics and pretreatment factors. Younger age and lighter body weight did not seem to influence the outcomes of REBOA regarding survival and complications.Level of evidence Level III

Purpose To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm.Materials and methods We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps.Results The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures.Conclusion The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.