Predicting the need for massive transfusion in trauma patients: the Traumatic Bleeding Severity Score

Area of Science:

• Trauma care
• Hemorrhage management
• Clinical prediction models

Background:

• Predicting massive transfusion needs in severe trauma is challenging.
• Current criteria for massive transfusion activation are lacking.
• Improved prediction can optimize resource allocation and patient care.

Purpose of the Study:

• To develop a scoring system for predicting massive transfusion in trauma patients.
• To validate the accuracy of this novel scoring system.

Main Methods:

• Retrospective review of 119 severely injured trauma patients to identify predictors.
• Development of the Traumatic Bleeding Severity Score (TBSS) based on logistic regression.
• Validation using data from 113 trauma patients and comparison with existing scores (THASH, ABC).

Main Results:

• Key predictors identified: age, systolic blood pressure, FAST scan, pelvic fracture severity, lactate level.
• TBSS demonstrated high predictive accuracy (AUC 0.985) in validation.
• High sensitivity (97.4%) and specificity (96.2%) for TBSS > 15.

Conclusions:

• The TBSS is a simple and accurate tool for predicting massive transfusion needs.
• An iOS application is available for easy TBSS calculation.
• Further multicenter validation is recommended to assess broader utility.