The addition of ROTEM parameter did not significantly improve the massive transfusion prediction in severe trauma patients

Objective: This study aimed to assess whether rotational thermoelectrometry (ROTEM) data could improve the massive transfusion (MT) prediction model.MethodThis 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 transfusion and in-hospital mortality, respectively. We constructed two models using multivariate logistic regression with backward conditional stepwise elimination (Model 1: without ROTEM parameter and Model 2: with ROTEM parameters). The area under the receiver operating characteristic curve (AUROC) was calculated to assess the predictive ability of the models.ResultIn 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 Model 1 and 2, respectively. The AUROC values for Models 1 and 2 were not statistically different for either MT or in-hospital mortality.ConclusionWe found that addition of the ROTEM parameter did not significantly improve the predictive power of MT and in-hospital mortality in trauma patients.