Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

Abstract

Introduction: Traumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequence. This study focuses on identifying factors predicting EDH volume changes in TBI patients and developing a machine learning (ML) model to predict EDH expansion.

Methods: The study includes patients with traumatic EDH between 2019 and 2021. Data were gathered from CT scans performed at the time of admission and 6 hours later, and subsequently analyzed. The data was divided into three cohorts: all cases, adults, and pediatrics. To predict EDH volume changes, we used Logistic Regression (LR), Random Forest (RF), XGBoost, and K-Nearest Neighbors (KNN) models. Data was divided into an 80% training set and a 20% test set. Through a rigorous process of parameter optimization and K-fold cross-validation, focusing on the area under the receiving operating curve (AUROC), we identified the best models in all cohorts. The best models were evaluated on the test sets, reporting AUROC, recall, precision, and accuracy using the youden index threshold.

Results: Results show that age, initial EDH volume, swirl sign, intra-hematoma air bleb, contusion, otorrhagia, subarachnoid hemorrhage, location, and other side extra-axial hematoma have significant effects on changing EDH volume. Based on test AUROC, the best models were RF for adults (82.4%), KNN for pediatrics (90%), and LR for all cases (81.6%).

Discussion: In this study, we identified key features for predicting EDH expansion as well as developing ML models. Using high sensitive models, can assist clinicians in identifying high-risk patients early. This allows for enhanced monitoring and timely intervention, improving patient outcomes by facilitating quicker decisions for follow-up imaging or treatment.