A predictive model for massive transfusion in combat casualty patients.

Area of Science:

• Trauma surgery
• Emergency medicine
• Critical care

Background:

• Massive transfusion (MT) in severely injured patients is linked to higher mortality.
• Early intervention with blood products can improve outcomes but identifying at-risk patients is challenging.

Purpose of the Study:

• To identify clinical variables for early detection of patients requiring massive transfusion.
• To develop a predictive algorithm for massive transfusion risk.

Main Methods:

• Retrospective cohort study at a combat support hospital.
• Analysis of demographic, diagnostic, and laboratory variables upon admission.
• Development and validation of a predictive algorithm and scoring system.

Main Results:

• Four independent risk factors for MT identified: heart rate >105 bpm, systolic blood pressure <110 mm Hg, pH <7.25, and hematocrit <32.0%.
• A predictive algorithm showed an area under the curve (AUC) of 0.839 for MT risk.
• Validation in an independent dataset achieved 66% accuracy (AUC = 0.747).

Conclusions:

• Key predictors for MT in severely injured patients were identified.
• Patients requiring MT can be identified using routine admission clinical variables.