Abstract 132: Prediction of Long‐term Treatment Failure of Intracranial Aneurysms Treated With Woven EndoBridge Device: A Multi‐center Study
Notice bibliographique
Résumé
Introduction Endovascular treatment of intracranial aneurysms with flow disruption devices like the Woven EndoBridge (WEB) is associated with variable occlusion success. Identifying determinants of long‐term treatment failure is vital for optimizing patient selection. This study aimed to determine morphological and clinical factors associated with treatment failure following WEB deployment for cerebral aneurysms. Methods This retrospective multicenter study analyzed intracranial aneurysm cases treated with WEB devices between 2011‐2022. Treatment failure, the primary outcome, was defined as persistent incomplete occlusion (Raymond‐Roy Occlusion Classification [RROC] grades 2/3) on final angiographic follow‐up, excluding cases with stable RROC grade 1 or worsening RROC grades. Follow‐up was categorized as short/midterm (<24 months) and long‐term (≥24 months). Univariate analyses assessed differences in baseline factors between failure and success groups. Machine learning models, including a CatBoost classifier, were developed to predict long‐term treatment failure and optimized using SHAP values. Multivariable logistic regression was performed to calculate odds ratios (OR) for factors associated with treatment failure in both follow‐up cohorts. Results Of the 813 cases, 206 (25.3%) had long‐term follow‐up (≥24 months). Treatment failure occurred in 210 (26%) cases based on persistent incomplete occlusion at final follow‐up. The CatBoost model achieved an AUC of 0.67 for predicting treatment failure, with the most influential predictors being aneurysm height, anterior communicating artery aneurysms, aneurysm‐neck‐diameter, age, worse pretreatment‐modified Rankin Scale scores, hemorrhagic complications, and WEB type DL. Multivariable logistic regression analysis revealed that antiplatelet therapy (OR 1.65, 95% CI [0.39, 6.96]; p=0.498) and compaction (minor/major) (OR 1.81, 95% CI [0.58, 5.57]; p=0.304) were not significantly associated with treatment failure at ≥ 24 months. Immediate flow stagnation was also not significant (OR 0.44, 95% CI [0.14, 1.38]; p=0.159). Aneurysm width (OR 1.31, 95% CI [0.91, 1.88]; p=0.144) and neck diameter (OR 1.24, 95% CI [0.83, 1.86]; p=0.286) did not show significant associations with treatment failure. Posterior circulation aneurysms were protective against failure at <24 months (OR 0.45, 95% CI [0.24, 0.81]; p=0.008). Older age was significantly associated with a reduced risk of treatment failure at ≥24 months (OR 0.93, 95% CI [0.88, 0.98]; p=0.005). Ruptured aneurysms and smoking were not significant predictors of treatment failure. Conclusion This study demonstrates that aneurysm morphology and patient characteristics influence long‐term outcomes with the WEB device. The machine learning model offers moderate predictive accuracy for treatment failure, while logistic regression identified key factors influencing persistent incomplete occlusion, particularly aneurysm location and patient age. These findings could inform patient selection and treatment strategies, although further external validation is required to confirm their clinical implications.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».