A prediction model for massive hemorrhage in trauma: a retrospective observational study

Area of Science:

• Trauma care
• Emergency medicine
• Predictive modeling

Background:

• Massive hemorrhage is a leading cause of preventable death in trauma patients.
• Early and accurate diagnosis is critical for effective management.

Purpose of the Study:

• To develop and validate prediction models for the early diagnosis of massive hemorrhage in trauma.
• To create a clinical tool for assisting medical staff.

Main Methods:

• Logistic regression (LR) models were developed using a trauma database.
• Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) method.
• Two models were constructed: one based on LASSO selection and another on initial vital signs. A web calculator was created for clinical use.

Main Results:

• The LASSO-selected model (Model 1) demonstrated excellent discrimination (AUC: 0.894) and calibration.
• Key predictors included heart rate, pulse pressure, base excess, hemoglobin, pelvic fracture, and positive imaging findings.
• Model 1 outperformed the vital signs-only model (Model 2).

Conclusions:

• Validated prediction models can aid in the early diagnosis of massive hemorrhage in trauma.
• The developed web calculator provides a practical tool for clinical application.
• Improved early diagnosis can lead to timely interventions and potentially reduce mortality.