TY - JOUR
T1 - Middle meningeal artery embolization for chronic subdural hematoma
T2 - A single-center experience and predictive modeling of outcomes
AU - Cohen-Cohen, Salomon
AU - Jabal, Mohamed Sobhi
AU - Rinaldo, Lorenzo
AU - Savastano, Luis E.
AU - Lanzino, Giuseppe
AU - Cloft, Harry
AU - Brinjikji, Waleed
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - Chronic Subdural Hematoma
KW - MMA Embolization
KW - Middle Meningeal Artery
KW - Outcome
KW - Prognosis
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U2 - 10.1177/19714009231224431
DO - 10.1177/19714009231224431
M3 - Article
C2 - 38147825
AN - SCOPUS:85180899991
SN - 1971-4009
VL - 37
SP - 192
EP - 198
JO - Neuroradiology Journal
JF - Neuroradiology Journal
IS - 2
ER -