Abstract 145: Endovascular Thrombectomy for Large Vessel Ischemic Stroke Patients With Low Aspects: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Most trials for the endovascular thrombectomy (EVT) of large vessel ischemic stroke excluded patients with large core infarcts and low Alberta Stroke Program Early CT Score (ASPECTS). As a result, the current American Heart Association guidelines for acute ischemic stroke reserve Grade 1A recommendation for the use of EVT for patients with an ASPECTS of 6 or more. However recent data from the HERMES collaboration has shown that even stroke patients with large core infarcts may still benefit from EVT. Objectives: Through this systematic review, we aim to determine the safety and efficacy of EVT for large vessel ischemic stroke patients with low ASPECTS (5 or less). Methods: Medline, Cochrane Central Register of Systematic Reviews and ClinicalTrials.gov were searched for studies appraising the outcomes of EVT for low ASPECTS acute ischemic stroke patients. Patients with low ASPECTS who underwent EVT were compared to those who only received best medical therapy (BMT). A meta-analysis of proportions was done to compare the outcomes of the two groups in terms of symptomatic intracranial hemorrhage, mortality and good 3-month functional outcomes (modified Rankin Scale < 2). Results: Nine studies with a total of 1,196 acutes stroke patients with low ASPECTS (712 undergoing EVT and 484 with only BMT) were included in the study. There was a trend towards a higher rate of sICH in the EVT group (9.2%; 95% CI 6.1% to 13.6%; I 2 53.37%) compared to the BMT group (5.5%; 95% CI 3.7% to 8.1%; I 2 =0%) but this did not reach statistical significance (p=0.11). There was no difference (p=0.41) in the pooled 3-month mortality of EVT patients (30.7%; 95% CI 21.7 to 41.5%; I 2 84.23%) and BMT patients (36.6%; 95% CI 26.4% to 48.1%; I 2 76.2%). Patients who underwent EVT had significantly better (p=0.001) 3-month outcomes, with 27.7% (95% 21.8 to 34.5%; I 2 62.08%) of patients attaining an MRS 0-2 compared to only 3.7% (95% 2.3 to 5.9%; I 2 87.21%) of patients in the BMT. Conclusion: Our meta-analysis suggests that acute stroke patients with low ASPECTS score may still benefit from EVT. Larger registry based studies and randomized controlled trials are needed to further substantiate the findings of our review.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".