Middle meningeal artery embolization for chronic subdural hematoma: A single-center experience and predictive modeling of outcomes.

BACKGROUND: Remarkable interest is rising around middle meningeal artery embolization (MMAE) as an emerging alternative therapy for chronic subdural hematoma (cSDH). The study aims to highlight a large center experience and the variables associated with treatment failure and build experimental machine learning (ML) models for outcome prediction.

MATERIAL AND METHODS: A 2-year experience in MMAE for managing patients with chronic subdural hematoma was analyzed. Descriptive statistical analysis was conducted using imaging and clinical features of the patients and cSDH, which were subsequently used to build predictive models for the procedure outcome. The modeling evaluation metrics were the area under the ROC curve and F1-score.

RESULTS: A total of 100 cSDH of 76 patients who underwent MMAE were included with an average follow-up of 6 months. The intervention had a per procedure success rate of 92%. Thrombocytopenia had a highly significant association with treatment failure. Two patients suffered a complication related to the procedure. The best performing machine learning models in predicting MMAE failure achieved an ROC-AUC of 70%, and an F1-score of 67%, including all patients with or without surgical intervention prior to embolization, and an ROC-AUC of 82% and an F1-score of 69% when only patients who underwent upfront MMAE were included.

CONCLUSION: MMAE is a safe and minimally invasive procedure with great potential in transforming the management of cSDH and reducing the risk of surgical complications in selected patients. An ML approach with larger sample size might help better predict outcomes and highlight important predictors following MMAE in patients with cSDH.