Early venous filling is associated with unfavorable outcomes in acute ischemic stroke with large vessel occlusion after mechanical thrombectomy: a real-world analysis
Bibliographic record
Abstract
BACKGROUND: The presence of early venous filling (EVF) post-mechanical thrombectomy (MT) in acute ischemic stroke (AIS) patients has been observed, yet its prognostic value for clinical outcomes remains underexplored. This study aimed to assess the correlation between EVF and poor clinical outcomes in AIS patients who underwent MT. MATERIALS AND METHODS: This retrospective analysis included AIS patients with large vessel occlusions treated with MT at the First Affiliated Hospital of Xi'an Jiaotong University from January 2018 to June 2023. The primary outcome was mRS at 90 days, secondary outcomes included hemorrhagic transformation, symptomatic intracranial hemorrhage, and malignant brain edema. The study used inverse probability weighting for balancing baseline characteristics and employed univariate and multivariate logistic regression analyses to explore the association between EVF and clinical outcomes. G*Power was used to calculate the sample size. RESULTS: Among 307 patients, 75 (24.4%) presented with EVF. Patients with EVF had significantly higher rates of unfavorable outcomes at 90 days (76.00% vs. 46.12%, P < 0.001). Multivariate analysis revealed significant associations between EVF and unfavorable outcome (odds ratio [OR] = 2.69, 95%CI [1.37-5.26], P = 0.004), hemorrhagic transformation (OR = 3.11, 95%CI [1.73-5.62], P < 0.001), symptomatic intracranial hemorrhage (OR = 3.24, 95%CI 1.42 to 7.37, P = 0.005), and malignant brain edema (OR = 3.06, 95%CI [1.56-6.01], P = 0.001). Stratified analysis showed EVF group with a baseline Alberta Stroke Program Early CT (ASPECT) score of ≤ 8 exhibited a higher risk of unfavorable outcomes compared with patients in the non-EVF group (OR = 2.64, 95%CI [1.03-6.73], P = 0.042). Mediation analysis indicated that malignant brain edema accounted for 35.42% of the correlation between EVF and unfavorable outcomes. CONCLUSIONS: This study establishes EVF as an independent risk factor for unfavorable outcomes after MT in AIS. Therefore, EVF in conjunction with a low ASPECT score provides essential insights for identifying patients at high risk for unfavorable outcomes.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".