Efficacy and safety of evogliptin in the treatment of type 2 diabetes mellitus in a Brazilian population: a randomized bridging study.

Background: Evogliptin (EVO) is a potent and selective dipeptidyl peptidase-4 inhibitor (DPP4i) developed for the treatment of type 2 diabetes mellitus (T2DM). DPP4is are known to exhibit a better glucose-lowering effect in Asians compared to other ethnic groups. Once EVO's clinical development program was conducted in Asian patients, this bridging study was designed to validate for the Brazilian population the efficacy and safety of the approved dose regimen (once-daily 5.

External validation of a smartphone app model to predict the need for massive transfusion using five different definitions.

Background: Previously, a model to predict massive transfusion protocol (MTP) (activation) was derived using a single-institution data set. The PRospective, Observational, Multicenter, Major Trauma Transfusion database was used to externally validate this model's ability to predict both MTP activation and massive transfusion (MT) administration using multiple MT definitions.

Methods: The app model was used to calculate the predicted probability of MTP activation or MT delivery.

Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study.

Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells.

Clinical gestalt and the prediction of massive transfusion after trauma.

Introduction: Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable.