RESUMO
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.
RESUMO
Background: Rotational thrombelastometry (ROTEM) has been used to evaluate the coagulation state, predict transfusion, and optimize hemostatic management in trauma patients. However, there were limited studies on whether the prediction value could be improved by adding the ROTEM parameter to the prediction model for in-hospital mortality and massive transfusion (MT) in trauma patients. Objective: This study assessed whether ROTEM data could improve the MT prediction model. Method: This was a single-center, retrospective study. Patients who presented to the trauma center and underwent ROTEM between 2016 and 2020 were included. The primary and secondary outcomes were massive transfusions and in-hospital mortality, respectively. We constructed two models using multivariate logistic regression with backward conditional stepwise elimination (Model 1: without the ROTEM parameter and Model 2: with the ROTEM parameter). The area under the receiver operating characteristic curve (AUROC) was calculated to assess the predictive ability of the models. Result: In total, 969 patients were included; 196 (20.2%) received MT. The in-hospital mortality rate was 14.1%. For MT, the AUROC was 0.854 (95% confidence interval [CI], 0.825-0.883) and 0.860 (95% CI, 0.832-0.888) for Model 1 and 2, respectively. For in-hospital mortality, the AUROC was 0.886 (95% CI, 0.857-0.915) and 0.889 (95% CI, 0.861-0.918) for models 1 and 2, respectively. The AUROC values for models 1 and 2 were not statistically different for either MT or in-hospital mortality. Conclusion: We found that the addition of the ROTEM parameter did not significantly improve the predictive power of MT and in-hospital mortality in trauma patients.
