Abstract Number ‐ 171: Stroke location as predictor of bleeding after EVT for ischemic stroke in the anterior circulation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".