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The base deficit (B), international normalized ratio (I), and Glasgow coma scale (GCS) (BIG) score is useful in predicting mortality in pediatric trauma patients; however, studies on the use of BIG score in adult patients with trauma are sparse. In addition, studies on the correlation between the BIG score and massive transfusion (MT) have not yet been conducted. This study aimed to evaluate the predictive value of BIG score for mortality and the need for MT in adult trauma patients. This retrospective study used data collected between 2016 and 2020 at our hospital’s trauma center and registry. The predictive value of BIG score was compared with that of the Injury Severity Score (ISS) and Revised Trauma Score (RTS). Logistic regression analysis was carried out to assess whether BIG score was an independent risk factor. Receiver operating characteristic (ROC) curve analysis was performed, and predictive values were evaluated by measuring the area under the ROC curve (AUROC). In total, 5,605 patients were included in this study. In logistic regression analysis, BIG score was independently associated with in-hospital mortality (odds ratio (OR): 1.1859; 95% confidence interval (CI): 1.1636–1.2086) and MT (OR: 1.0802; 95% CI: 1.0609–1.0999). The AUROCs of BIG score for in-hospital mortality and MT were 0.852 (0.842–0.861) and 0.848 (0.838–0.857), respectively. Contrastingly, the AUROCs of ISS and RTS for in-hospital mortality were 0.795 (0.784–0.805) and 0.859 (0.850–0.868), respectively. Moreover, AUROCs of ISS and RTS for MT were 0.812 (0.802–0.822) and 0.838 (0.828–0.848), respectively. The predictive value of BIG score for mortality and MT was significantly higher than that of the ISS. The BIG score also showed a better AUROC for predicting in-hospital mortality compared with RTS. In conclusion, the BIG score is a useful indicator for predicting mortality and the need for MT in adult trauma patients.

Introduction. This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents. Methods. This was a multicenter observational study. For four years, we included patients with bicycle rider injuries in the Emergency Department-Based Injury In-depth Surveillance database. In this study, we regarded ICD admission or in-hospital mortality as parameters of severe trauma. Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. A receiver operating characteristic (ROC) curve was generated to evaluate the performance of the regression model. Results. This study included 19,842 patients, of whom 1,202 (6.05%) had severe trauma. In multivariate regression analysis, male sex, older age, alcohol use, motor vehicle opponent, load state (general and crosswalk), blood pressure, heart rate, respiratory rate, and Glasgow Coma Scale were the independent factors for predicting severe trauma. In the ROC analysis, the area under the ROC curve for predicting severe trauma was 0.848 (95% confidence interval: 0.830–0.867). Conclusion. We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.

Background: Health screening is a preventive and cost-effective public health strategy for early detection of diseases. However, the COVID-19 pandemic has decreased health screening participation. The aim of this study was to examine regional differences in health screening participation between before and during COVID-19 pandemic and vulnerabilities of health screening participation in the regional context. Methods: Administrative data from 229 districts consisting of 16 provinces in South Korea and health screening participation rate of each district collected in 2019 and 2020 were included in the study. Data were then analyzed via descriptive statistics and geographically weighted regression (GWR). Results: This study revealed that health screening participation rates decreased in all districts during COVID-19. Regional vulnerabilities contributing to a further reduction in health screening participation rate included COVID-19 concerns, the population of those aged 65+ years and the disabled, lower education level, lower access to healthcare, and the prevalence of chronic disease. GWR analysis showed that different vulnerable factors had different degrees of influence on differences in health screening participation rate. Conclusions: These findings could enhance our understanding of decreased health screening participation due to COVID-19 and suggest that regional vulnerabilities should be considered stringent public health strategies after COVID-19.