Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using...

Area of Science:

• Emergency Medicine
• Data Science in Healthcare
• Trauma Surgery

Background:

• Hemorrhage is a leading cause of trauma mortality, with delayed control significantly increasing death rates.
• The critical window for intervention in trauma patients is often missed due to delayed recognition of hemorrhage.
• Existing trauma care struggles to rapidly identify patients requiring immediate hemorrhage control.

Purpose of the Study:

• To develop and validate a machine learning model for predicting the need for urgent hemorrhage intervention in trauma patients.
• To leverage demographic and clinical data for early identification of critical bleeding.
• To enhance timely decision-making in emergency trauma care.

Main Methods:

• An XGBoost machine learning model was developed and validated using data from the National Trauma Data Bank (2017-2019).
• The model was trained and tested on demographic and clinical data from the initial hours post-trauma.
• Model performance was assessed using AUROC, sensitivity, specificity, and accuracy on training, internal, and external validation sets.

Main Results:

• The XGBoost model achieved high predictive performance, with an AUROC of 0.875 on the external validation set.
• The model demonstrated strong clinical utility with sensitivity of 77.8% and specificity of 82.1% on external validation.
• Overall accuracy exceeded 81% across all datasets, indicating robust and reliable prediction of hemorrhage intervention needs.

Conclusions:

• The XGBoost model accurately predicts the necessity for urgent hemorrhage intervention in trauma patients.
• This machine learning approach demonstrates superior accuracy and robustness compared to other algorithms.
• The findings underscore the potential of AI to significantly improve emergency response and clinical decision-making in trauma care.