E-029 Thrombectomy in stroke patients with low ASPECTS: is TICI 2C/3 superior to TICI 2B?
Bibliographic record
Abstract
Introduction/Purpose the procedural success of mechanical thrombectomy (MT) has been traditionally defined by a final thrombolysis in cerebral infarction (TICI) score of 2B/3. However, several studies have shown that patients with a TICI score of 2C and 3 have a significantly better outcome than those with a TICI score of 2B. This retrospective cohort study aimed to compare the outcomes of patients with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (2-5) who achieved TICI 2B versus those who achieved TICI 2C/3 after MT in the early and late time window periods. Materials and Methods This study utilized data from the Stroke Thrombectomy and Aneurysm Registry (STAR), which combined databases from 32 thrombectomy-capable stroke centers between 2013 and 2023. The study included only patients with low ASPECTS who achieved TICI 2B, 2C, or 3 after MT for internal carotid artery (ICA) or middle cerebral artery (M1) stroke. Results Of the 10,081 patients who underwent MT, 309 met the inclusion criteria. Of these, 138 (44.6%) achieved TICI 2B, and 171 (55.4%) achieved TICI 2C/3. There were no significant differences in baseline characteristics between the two groups. The 90-day favorable outcome (Modified Rankin Score [mRS]: 0-3) was significantly better in the TICI 2C/3 group than in the TICI 2B group (42.0% versus 25.4%; P=0.047). After adjusting for Age, NIHSS, intravenous tPA administration, total attempts, distal embolization, successful recanalization, symptoms onset to groin puncture, and ICA involvement, binary regression analysis revealed that achieving TICI 2C/3 was significantly associated with higher odds of a favorable 90-day outcome (OR 3.30; 95% CI 1.24-9.51; P=0.02). Conclusion In patients with low ASPECTS, achieving a TICI 2C/3 score after MT is associated with a more favorable 90-day outcome. These findings suggest that TICI 2C/3 is a better target for MT than TICI 2B in patients with low ASPECTS. Disclosures S. Samir Elawady: None. M. Mahdi Sowlat: None. I. Maier: None. P. Jabbour: None. J. Kim: None. S. Quintero Wolfe: None. A. Rai: None. R. M Starke: None. M. Psychogios: None. E. Samaniego: None. A. Arthur: None. S. Yoshimura: None. J. A. Grossberg: None. A. Alawieh: None. J. Mascitelli: None. I. Fragata: None. H. Cuellar: None. A. Polifka: None. J. Osbun: None. R. Crosa: None. C. Matouk: None. M. S. Park: None. M. R. Levitt: None. W. Brinjikji: None. T. Dumont: None. R. Williamson Jr: None. P. Navia: None. A. M Spiotta: None. S. Al Kasab: None.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".