E-279 Predictors and outcomes of excellent recanalization in distal medium vessel occlusion strokes: a multinational, multicenter study
Bibliographic record
Abstract
Background Acute ischemic stroke (AIS) due to distal medium vessel occlusions (DMVO) presents significant challenges in treatment and management. This study aimed to identify factors associated with achieving excellent recanalization (mTICI 2c-3) in DMVO stroke patients treated with mechanical thrombectomy (MT). Methods This prospectively collected, retrospective reviewed multinational, multicenter study analyzed the Multicenter Analysis of primary Distal medium vessel occlusions: effect of MechanicalThrombectomy (MAD-MT) registry. We included data from 37 centers across North America,Asia, and Europe, collected between September 2017 and July 2021. The study included AIS patients with DMVO treated with MT, with or without intravenous thrombolysis (IVT), and recorded mTICI scores post-MT. Univariable and multivariable logistic regression models assessed factors associated with excellent recanalization. Results Among 1,463 patients with DMVO stroke, 523 achieved TICI 2b recanalization, and 940 achieved TICI 2c-3. Our analysis revealed that distal occlusions had higher odds of excellent recanalization compared to medium vessel occlusions (OR, 1.50; 95% CI, 1.11–2.05; p=0.01).Cardioembolic stroke etiology was also associated with a higher likelihood of excellent recanalization (OR, 1.70; 95% CI, 1.09–2.66; p=0.019). Patients achieving TICI 2c-3 recanalization exhibited lower initial NIHSS scores, significant improvements post-procedural NIHSS shift, and higher percentage of favorable 90-day outcomes. However, no significantdifference in 90-day mortality rates was observed. Conclusion This study underscores the higher likelihood of achieving excellent recanalization in DMVO stroke patients with distal occlusions and cardioembolic etiology. Patients attaining higher mTICI scores post-MT demonstrated better clinical outcomes. These findings highlight thepotential for broader applicability of MT in DMVO cases and suggest a need for furtherprospective studies and randomized controlled trials for validation. Disclosures B. Musmar: None. N. Adeeb: None. H. Salim: None. S. Ghozy: None. N. M Cancelliere: None. V. Mendes Pereira: None. A. Guenego: None. P. Jabbour: None. A. A Dmytriw: None. V. Yedavalli: 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 | 0.003 |
| 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) | 0.002 | 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".