Impact of Stenting with Angioplasty and MTICI 2c-3 Recanalization On Outcome in Acute MCA Occlusion with Underlying Stenosis
Bibliographic record
Abstract
PURPOSE: Mechanical thrombectomy (MT) is standard care for acute large vessel occlusion (LVO), but it fails in 10-20% of cases, often due to underlying intracranial artery stenosis (ICAS). In such cases, rescue stenting (RS), with or without angioplasty, may improve recanalization, but its clinical benefit remains debated. The purpose of this study was to define predictors of clinical outcome in this patient population. METHODS: We conducted a retrospective multicenter study including 115 patients with ICAS-related occlusion of the middle cerebral artery (MCA) treated with MT and RS across 27 international stroke centers. Baseline, procedural, and post-procedural variables were analyzed. The outcome measure was the ordinal shift of the 90-day modified Rankin Scale (mRS) score. Stepwise multivariate regression and structural equation modeling (SEM) were used to identify outcome predictors and explore mediation pathways. RESULTS: Successful recanalization (modified Treatment in Cerebral Infarction (mTICI) score ≥ 2b) was achieved in 94.8% of patients, with 73.0% reaching mTICI 2c‑3. SEM showed that baseline Alberta Stroke Program Early CT Score (ASPECTS), stenting with angioplasty and achieving mTICI 2c‑3 were associated with improved functional outcome, mediated by higher post-procedural ASPECTS. Post-procedural ASPECTS influenced functional outcome both directly (estimate = -0.45, p < 0.001) and indirectly by reducing the occurrence of symptomatic intracranial hemorrhage (sICH) (estimate = -0.09, p = 0.004). This model explained 36.5% of the variance in 90-day mRS scores. CONCLUSION: In patients with acute ICAS-related MCA occlusion, stenting with angioplasty and achieving mTICI 2c-3 recanalization are associated with improved clinical outcome. These benefits are mediated by better post-procedural ASPECTS and reduced sICH. Prospective studies are warranted to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".