Mechanical thrombectomy failure in anterior circulation large vessel occlusion: an overview from the ROSSETTI registry
Bibliographic record
Abstract
BACKGROUND: Although mechanical thrombectomy (MT) is an effective treatment for large vessel occlusion (LVO) with a high successful recanalization rate, MT failure (MTF) occurs in 10-15% of cases and is associated with unfavorable outcomes. However, little is known about the clinical, technical, and radiological reasons for MTF. We investigated the technical factors associated with MTF. METHODS: We conducted a retrospective analysis of consecutive patients with anterior LVO prospectively included in the ongoing observational multicenter ROSSETTI registry. Patients were categorized according to the success (≥mTICI 2b) or failure (<mTICI 2b) of the MT procedure. Baseline clinical and demographic characteristics, endovascular MT techniques, and angiographic and clinical outcomes were compared. Multivariate analysis for prediction of MTF was performed. RESULTS: We analyzed 4135 patients, including 325 patients (7.9%) with MTF. Patients in the MTF group had a significantly lower Alberta Stroke Program Early CT Score (ASPECTS) at baseline (8 (7-10) vs 9 (8-10)), longer time since last time seen well (279 min vs 262 min), increased MT procedure time (76 min vs 31 min), higher rate of complications (23% vs 4%), higher symptomatic intracerebral hemorrhage (21% vs 7.9%), higher 24 hour National Institutes of Health Stroke Scale score (19 vs 6), worse functional outcome at 3 months (modified Rankin Scale score 0-2, 15.6% vs 53%), and higher mortality (45% vs 20%). Four or more passes were an independent predictor of MTF (OR 3.46, 95% CI 2.58 to 4.63; P<0.001). None of the endovascular techniques demonstrated a higher likelihood of MTF. CONCLUSION: In this study, MTF in anterior circulation LVO was associated with a high complication rate and worse outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".