Abstract Number ‐ 171: Stroke location as predictor of bleeding after EVT for ischemic stroke in the anterior circulation
Notice bibliographique
Résumé
Introduction Hemorrhage is a known risk of Endovascular thrombectomy (EVT) for ischemic stroke . Several predictors of bleeding have been identified. However, stroke location has not yet been evaluated as an independent predictor. Methods Patients ≥ 18 years who underwent EVT for anterior circulation large vessel occlusion between January 1, 2020, and December 31, 2021, at our centre were included. After the exclusion criteria, a total of 344patients were analyzed. The admission CT scans were reviewed by a neuroradiologist to determine the ASPECTS, and classify the involved regions into location groups: only central core, only cortical, and both central and cortical areas. Post EVT images were evaluated to assign the Heidelberg bleeding classification. Statistical analysis was performed in R, version 4.1. For univariate analyses, Fisher’s exact or chi‐square test was used for categorical data. Quantitative data were tested for normality, and the t‐test or Mann‐Whitney test was applied accordingly. All variables in the univariate analyses with p < 0.15 were considered for the stepwise logistic regression models. Results Patients had a median age of 73 years (IQR 20), NIHSS of 16 (IQR 9),ASPECTS of 7 (IQR 3), systolic blood pressure of 146 mmHg (IQR 25), and glycemia of 7 mmol/L (IQR 2). The most frequent occlusion site was M1 (65.7%), and most patients had an mTICI > 2A (89.5%). Bleeding occurred in 182 (52.9%) and intraparenchymal hematoma in 73 (21.2%) patients, with most bleeding only in the central core (65.4%), with the lentiform nucleus involved in 122 (67%) of the bleeds. Thirty‐eight patients died (11%), and 237 had a modified Rankin score > 2 at discharge (68.9%). Stroke location was significant for all types of bleeding (p < 0.001) and intraparenchymal hematoma (p = 0.137) in the univariate analyses. However, after multiple logistic regression, the stroke location was not an independent predictor of any type of bleeding. On the other hand, a lower ASPECTS was a significant predictor of all types of bleeding (p < 0.001; OR 1.347; 95% CI 1.1799 ‐ 1.539) and intraparenchymal hematoma (p = 0.009; OR 1.756; 95% CI 1.152 ‐ 2.677). In addition to ASPECTS, high NIHSS (p = 0.038; OR 1.058; 95% CI 1.003 ‐ 1.115) was a significant predictor of all types of bleeding, while high systolic blood pressure (p = 0.027; OR 1. 037;95% CI 1.004 ‐ 1.071), cardioembolic stroke (p = 0.042; OR 10.408; 95% CI 1.085 ‐ 99.889), and poor collaterals (p = 0.046; OR 5.068; 95% CI 1.029 ‐ 24.951) were significant for intraparenchymal hematoma. Conclusions Stroke location is not an independent predictor of bleeding. However, stroke size, as indicated by the ASPECT, is a predictor of bleeding after EVT. Moreover, some modifiable predictors were not significant because they are already controlled for in the study, but systolic pressure was a significant predictor of intraparenchymal hematoma and shows that more studies are needed to determine the appropriate control levels to reduce the chance of bleeding without compromising cerebral perfusion.
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 | 0,000 | 0,002 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,002 |
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 ».