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Machine Learning Prediction of Treatment Failure After Middle Meningeal Artery Embolization for Subdural Hematoma
Bluyé DeMessie1, Muhammed Amir Essibayi1, Hamza Adel Salim2
1Department of Neurological Surgery and Radiology, Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA.
Background And Objectives:
Middle meningeal artery embolization (MMAE) is effective for chronic subdural hematoma (SDH), but failure rates vary and risk prediction tools are lacking. This study developed and validated a machine learning (ML) model to predict MMAE treatment failure for SDH.
Methods:
We conducted an international, multi-institutional retrospective cohort study of patients who underwent MMAE for SDH between 2019 and 2025. The cohort was split into training and test sets, stratified by treatment failure, with all hematomas from each patient assigned to a single cohort to prevent data leakage. Multiple ML algorithms were evaluated using 5-fold cross-validation, with gradient boosting selected as the final model. The primary outcome was treatment failure, defined as rescue surgical evacuation or persistent/recurrent SDH requiring retreatment. Model performance was assessed using area under the curve-receiver operating characteristic. Patients were stratified into low-risk (<5%), medium-risk (5%-15%), and high-risk (>15%) groups using a priori clinically defined thresholds.
Results:
Among 692 treated hematomas in the test cohort, 64 (9.2%) experienced treatment failure. The gradient boosting model achieved an area under the curve-receiver operating characteristic of 0.856 (95% CI, 0.803-0.902) before calibration and 0.832 (95% CI, 0.766-0.889) after isotonic calibration, with excellent calibration (Brier score, 0.053). The most important predictors were age-platelet interaction, platelet-hemoglobin ratio, platelet count, SDH thickness, and age. Observed treatment failure was 1.6% (95% CI, 0.4%-5.7%) in the low-risk group (n = 124; 17.9%), 6.0% (95% CI, 4.3%-8.4%) in the medium-risk group (n = 516; 74.6%), and 59.6% (95% CI, 46.1%-71.8%) in the high-risk group (n = 52; 7.5%) (P < .001 for trend), a 37.2-fold difference between high- and low-risk groups.
Conclusion:
This ML model stratified MMAE patients into risk groups with a 37.2-fold difference in treatment failure. High-risk patients had 59.6% failure, whereas low-risk patients had 1.6% failure. Risk stratification may improve patient selection and monitoring.