Predicting Recanalization Failure With Conventional Devices During Endovascular Treatment Related to Vessel Occlusion
Bibliographic record
Abstract
Background Among patients with stroke eligible for endovascular treatment, preprocedure identification of those with low chances of successful recanalization with conventional devices (stent‐retrievers and/or direct aspiration) may allow anticipating procedural rescue strategies. We aimed to develop a preprocedural algorithm able to predict recanalization failure with conventional devices (RFCD). Methods Observational study. Data from consecutive patients with stroke who received endovascular treatment between 2019 and 2022 in 10 centers were collected from the Catalan Stroke Registry (Codi Ictus Catalunya Registry, CICAT). RFCD was defined as final thrombolysis in cerebral infarction ≤2a or the use of rescue therapy defined as balloon angioplasty±stent deployment. Univariate and multivariate analysis to identify variables associated with RFCD were performed. A gradient boosted decision tree machine learning model to predict RFCD was developed utilizing preprocedure variables previously selected. Clinical improvement at 24 hours was defined as a drop of ≥4 points from baseline National Institutes of Health Stroke Scale score or 0–1 at 24 hours. Results In total, 984 patients were included; RFCD was observed in 14.3% (n:141) of the cases. Of these, 47.5% (n = 67) received balloon angioplasty±stent deployment as rescue therapy. Among patients receiving balloon angioplasty±stent deployment, clinical improvement was associated with lower number of attempts with conventional devices (median number of passes 2 versus 3; P = 0.045). In logistic regression, the absence of atrial fibrillation (odds ratio [OR]: 2.730, 95%CI: 1.541–4.836; P = 0.007) and no‐thrombolytic treatment (OR: 1.826, 95%CI: 1.230–2.711; P = 0.003) emerged as independent predictors of RFCD. A predictive model for RFCD, based on age, sex, hypertension, wake‐up stroke, baseline National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT [Computed Tomography] Score, occlusion site, thrombolysis, and atrial fibrillation showed an acceptable discrimination (area under the curve: 0.72±0.024 SD) and accuracy (0.75±0.015 SD). Overall performance was moderate (weighted F1‐score: 0.77±0.041 SD). Conclusion In RFCD patients, early balloon angioplasty±stent deployment rescue was associated with improved outcomes. A predictive model using affordable preprocedure clinical variables could be useful to identify these patients before intervention.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".