Research & Innovation
Publications
Predictive modeling of long-term improvement in occlusion outcomes following Woven EndoBridge treatment of cerebral aneurysms
PMU Authors
Michael Kral, Clemens M Schirmer, Christoph J Griessenauer
All Authors
, Alireza Karandish, Muhammed Amir Essibayi, Mohamed Sobhi Jabal, Hamza Adel Salim, Basel Musmar, Nimer Adeeb, Mahmoud Dibas, Davide Simonato, Yan-Lin Li, James Grist, Fulvio Zaccagna, Oktay Algin, Sherief Ghozy, Sovann V Lay, Adrien Guenego, Leonardo Renieri, Joseph Carnevale, Guillaume Saliou, Panagiotis Mastorakos, Kareem El Naamani, Eimad Shotar, Markus Möhlenbruch, Michael Kral, Charlotte Chung, Mohamed M Salem, Ivan Lylyk, Paul M Foreman, Hamza Shaikh, Vedran Župančić, Muhammad U Hafeez, Joshua Catapano, Muhammad Waqas, Atilla Kazanci, Giyas Ayberk, James D Rabinov, Julian Maingard, Clemens M Schirmer, Mariangela Piano, Anna L Kühn, Caterina Michelozzi, Robert M Starke, Ameer Hassan, Mark Ogilvie, Anh Nguyen, Jesse Jones, Waleed Brinjikji, Marie T Nawka, Marios Psychogios, Christian Ulfert, Christoph J Griessenauer
Journal association
INTERVENTIONAL NEURORADIOLOGY
Abstract
BackgroundThe Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment.MethodsIn this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings.ResultsAmong 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis.ConclusionsMachine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.