The Woven EndoBridge (WEB) device is emerging as a novel therapy for intracranial aneurysms, but its use for off-label indications requires further study. Using machine learning, we aimed to develop predictive models for complete occlusion after off-label WEB treatment and to identify factors associated with occlusion outcomes.
This multicenter, retrospective study included 162 patients who underwent off-label WEB treatment for intracranial aneurysms. Baseline, morphological, and procedural variables were utilized to develop machine-learning models predicting complete occlusion. Model interpretation was performed to determine significant predictors. Ordinal regression was also performed with occlusion status as an ordinal outcome from better (Raymond Roy Occlusion Classification [RROC] grade 1) to worse (RROC grade 3) status. Odds ratios (OR) with 95 % confidence intervals (CI) were reported.
The best performing model achieved an AUROC of 0.8 for predicting complete occlusion. Larger neck diameter and daughter sac were significant independent predictors of incomplete occlusion. On multivariable ordinal regression, higher RROC grades (OR 1.86, 95 % CI 1.25-2.82), larger neck diameter (OR 1.69, 95 % CI 1.09-2.65), and presence of daughter sacs (OR 2.26, 95 % CI 0.99-5.15) were associated with worse aneurysm occlusion after WEB treatment, independent of other factors.
This study found that larger neck diameter and daughter sacs were associated with worse occlusion after WEB therapy for aneurysms. The machine learning approach identified anatomical factors related to occlusion outcomes that may help guide patient selection and monitoring with this technology. Further validation is needed.