Poor clinical outcome despite successful basilar occlusion recanalization in the early time window: incidence and predictors
Bibliographic record
Abstract
BACKGROUND: Endovascular treatment (EVT) for basilar artery occlusions (BAO) is associated with a higher rate of futile recanalization compared with anterior circulation procedures. We aimed to identify the incidence and predictors of poor clinical outcome despite successful reperfusion in current clinical practice. METHODS: We used data from the ETIS (Endovascular Treatment in Ischemic Stroke) registry, a prospective multicenter observational registry of stroke treated with EVT in France. Patients undergoing EVT for acute BAO from January 2014 to May 2019 successfully treated within 8 hours from onset were included. Predictors of 90-day poor outcome (modified Rankin Scale (mRS) 4-6) were researched within patients with successful (modified Thrombolysis In Cerebral Infarction (mTICI 2b-3)) and excellent (mTICI 2c-3) reperfusion. RESULTS: Among 242 patients treated within 8 hours, successful reperfusion was achieved in 195 (80.5%) and excellent reperfusion in 120 (49.5%). Poor outcome was observed in 107 (54.8%) and 60 (50%) patients, respectively. In patients with successful early reperfusion, age, higher initial National Institutes of Health Stroke Scale (NIHSS) score, lower posterior circulation Alberta Stroke Programme Early CT Score (pc-ASPECTS), and absence of prior intravenous thrombolysis were independent predictors of poor outcome. The only treatment factor with an independent predictive value was first-pass mTICI 2b-3 reperfusion (adjusted OR 0.13, 95% CI 0.05 to 0.37, p<0.001). In patients with excellent early reperfusion, independent predictors were age, initial NIHSS score, first-pass mTICI 2c-3 reperfusion, and hemorrhagic transformation on post-interventional imaging. CONCLUSIONS: Early successful reperfusion with EVT occurred in 80.5% of patients, and the only treatment-related factor predictive of clinical outcome was first pass mTICI 2b-3 reperfusion. Further research is warranted to identify the optimal techniques and devices associated with first pass reperfusion in the posterior circulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".