Acute kidney injury (AKI) is common in patients with trauma and is associated with poor outcomes. Therefore, early prediction of AKI in patients with trauma is important for risk stratification and the provision of optimal intensive care unit treatment. This study aimed to compare 2 models, machine learning (ML) techniques and logistic regression, in predicting AKI in patients with trauma. We retrospectively reviewed the charts of 400 patients who sustained torso injuries between January 2016 and June 2020. Patients were included if they were aged > 15 years, admitted to the intensive care unit, survived for > 48 hours, had thoracic and/or abdominal injuries, had no end-stage renal disease, and had no missing data. AKI was defined in accordance with the Kidney Disease Improving Global Outcomes definition and staging system. The patients were divided into 2 groups: AKI (n = 78) and non-AKI (n = 322). We divided the original dataset into a training (80%) and a test set (20%), and the logistic regression with stepwise selection and ML (decision tree with hyperparameter optimization using grid search and cross-validation) was used to build a model for predicting AKI. The models established using the training dataset were evaluated using a confusion matrix receiver operating characteristic curve with the test dataset. We included 400 patients with torso injury, of whom 78 (19.5%) progressed to AKI. Age, intestinal injury, cumulative fluid balance within 24 hours, and the use of vasopressors were independent risk factors for AKI in the logistic regression model. In the ML model, vasopressors were the most important feature, followed by cumulative fluid balance within 24 hours and packed red blood cell transfusion within 4 hours. The accuracy score showed no differences between the 2 groups; however, the recall and F1 score were significantly higher in the ML model (.94 vs 56 and.75 vs 64, respectively). The ML model performed better than the logistic regression model in predicting AKI in patients with trauma. ML techniques can aid in risk stratification and the provision of optimal care.

Background: This study aimed to validate the Geriatric Trauma Outcome Score (GTOS) for predicting mortality associated with trauma in older Korean adults and compare the GTOS with the Trauma and Injury Severity Score (TRISS).Methods: This study included patients aged ≥65 years who visited the Chungbuk National University Hospital Regional Trauma Center between January 2016 and December 2022. We used receiver operating characteristic curves and calibration plots to assess the discrimination and calibration of the scoring systems.Results: Among 3,053 patients, the median age was 77 years, and the mortality rate was 5.2%. The overall GTOS-predicted mortality and 1–TRISS were 5.4% (interquartile range [IQR], 3.7–9.5) and 4.7% (IQR, 4.7–4.7), respectively. The areas under the curves (AUCs) of 1–TRISS and GTOS for the total population were 0.763 (95% confidence interval [CI], 0.719–0.806) and 0.794 (95% CI, 0.755–0.833), respectively. In the Glasgow Coma Scale (GCS) ≤12 group, the in-hospital mortality rate was 27.5% (79 deaths). The GTOS-predicted mortality and 1–TRISS in this group were 18.6% (IQR, 7.5–34.7) and 26.9% (IQR, 11.9–73.1), respectively. The AUCs of 1–TRISS and GTOS for the total population were 0.800 (95% CI, 0.776–0.854) and 0.744 (95% CI, 0.685–0.804), respectively.Conclusion: The GTOS and TRISS demonstrated comparable accuracy in predicting mortality, while the GTOS offered the advantage of simpler calculations. However, the GTOS tended to underestimate mortality in patients with GCS ≤12; thus, its application requires care in such cases.

Purpose: In this study, we aimed to compare the characteristics of patients with trauma by age group in a single center in Korea to identify the clinical characteristics and analyze the risk factors affecting mortality. Methods: Patients aged ≥18 years who visited the Chungbuk National University Hospital Regional Trauma Center between January 2016 and December 2022 were included. The accident mechanism, severity of the injury, and outcomes were compared by classifying the patients into group A (18–64 years), group B (65–79 years), and group C (≥80 years). In addition, logistic regression analysis was performed to identify factors affecting death.Results: The most common injury mechanism was traffic accidents in group A (40.9%) and slipping in group B (37.0%) and group C (56.2%). Although group A had the highest intensive care unit admission rate (38.0%), group C had the highest mortality rate (9.5%). In the regression analysis, 3 to 8 points on the Glasgow Coma Scale had the highest odds ratio for mortality, and red blood cell transfusion within 24 hours, intensive care unit admission, age, and Injury Severity Score were the predictors of death.Conclusions: For patients with trauma, the mechanism, injured body region, and severity of injury differed among the age groups. The high mortality rate of elderly patients suggests the need for different treatment approaches for trauma patients according to age. Identifying factors affecting clinical patterns and mortality according to age groups can help improve the prognosis of trauma patients in the future.

Purpose: Appropriate scoring systems can help classify and treat polytrauma patients. This study aimed to validate chest trauma scoring systems in polytrauma patients.Methods: Data from 1,038 polytrauma patients were analyzed. The primary outcomes were one or more complications: pneumonia, chest complications requiring surgery, and mortality. The Thoracic Trauma Severity Score (TTSS), Chest Trauma Score, Rib Fracture Score, and RibScore were compared using receiver operating characteristic (ROC) analysis in patients with or without head trauma.Results: In total, 1,038 patients were divided into two groups: those with complications (822 patients, 79.2%) and those with no complications (216 patients, 20.8%). Sex and body mass index did not significantly differ between the groups. However, age was higher in the complications group (64.1±17.5 years vs. 54.9±17.6 years, P<0.001). The proportion of head trauma patients was higher (58.3% vs. 24.6%, P<0.001) and the Glasgow Coma Scale score was worse (median [interquartile range], 12 [6.5–15] vs. 15 [14–15]; P<0.001) in the complications group. The number of rib fractures, the degree of rib fracture displacement, and the severity of pulmonary contusions were also higher in the complications group. In the area under the ROC curve analysis, the TTSS showed the highest predictive value for the entire group (0.731), head trauma group (0.715), and no head trauma group (0.730), while RibScore had the poorest performance (0.643, 0.622, and 0.622, respectively)Conclusions: Early injury severity detection and grading are crucial for patients with blunt chest trauma. The chest trauma scoring systems introduced to date, including the TTSS, are not acceptable for clinical use, especially in polytrauma patients with traumatic brain injury. Therefore, further revisions and analyses of chest trauma scoring systems are recommended.

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models arenecessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict30-day mortality in severely injured elderly patients with trauma and to compare the predictive performanceof various machine learning models.Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visitedthe regional trauma center at Chungbuk National University Hospital between 2016 and 2022.Four machine learning models—logistic regression, decision tree, random forest, and eXtreme GradientBoosting (XGBoost)—were developed to predict 30-day mortality. The models’ performancewas compared using metrics such as area under the receiver operating characteristic curve (AUC),accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) valuesand learning curves.Results: The performance evaluation of the machine learning models for predicting mortality in severelyinjured elderly patients with trauma showed AUC values for logistic regression, decision tree,random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models,XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72,0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealedassociations such as a high Glasgow Coma Scale negatively impacting mortality probability, whilehigher counts of transfused red blood cells were positively correlated with mortality probability. Thelearning curves indicated increased generalization and robustness as training examples increased.Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict30-day mortality in severely injured elderly patients with trauma.Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality inelderly patients with severe trauma.

Few cases of injuries caused by wild boars have been reported in the literature. Here, we present the case of a 64-year-old male patient who was attacked by a wild boar. The patient had a laceration to the right forehead and a penetrating wound in the area before the right auricle. Computed tomography scan revealed traumatic subarachnoid hemorrhage, subdural hemorrhage, right temporal lobe contusion hemorrhage, pneumocephalus, right zygomatic arch and temporal bone fracture, and right coronal process fracture of the mandible. Prophylactically, 2,000 mg of ceftriaxone and 400 mg of moxifloxacin were intravenously administered every 24 hours. An emergency craniectomy was performed because the skull fracture was an open fracture and control of the increased intracranial pressure was necessary. Inactivated Rabies Virus Vaccine was also administered postoperatively. We concluded that, unlike typical wounds from other traumatic causes, the risk of wound infection is higher. In order to reduce morbidity and mortality due to wild boar attacks, rapid stabilization of the patient’s vital signs, prevention of infection with appropriate antibiotics, and surgical intervention at the appropriate time and method are necessary.