Abstract TP236: Pretreatment Predictors Of 30-Day Very Poor Outcome After Thrombectomy In Large Ischemic Core Stroke: A Multicenter Prospective Cohort Study
Bibliographic record
Abstract
Introduction: Despite the efficacy and safety of endovascular treatment (EVT) demonstrated in many large-core randomized controlled trials, up to half of the patients experience very poor outcomes, suggesting a significant number of futile treatments. We aimed to identify predictors of very poor 30-day outcomes (mRS 5-6) after endovascular thrombectomy in patients with large infarct. Methods: We conducted a prospective, multicenter, observational study in Vietnam involving four comprehensive stroke centers, screening all consecutive patients who underwent EVT within 24 hours of symptom onset from August 2023 to August 2024. Large cores were defined by an Alberta Stroke Program Early CT Score (ASPECTS) of 3-5 on non-contrast CT or DWI-MRI and were assessed by two ASPECTS-certified stroke neurologists, with disagreements resolved by a senior reader. Risk factors were analyzed using multivariable logistic regression models. Prognosis was evaluated using the modified Rankin Scale (mRS), with very poor outcomes defined as a mRS score of 5-6 at 30-day follow-up. The study adheres to STROBE criteria and is registered as NCT06016348. Results: Of the 1,910 patients screened, a total of 361 (18.9%) were included, with a median age of 64.0 years (55.0-70.0) and a median ASPECTS of 4.0 (4.0-5.0). Of these, 145 patients (40.2%) had a mRS of 5-6 at 30-day follow-up. In multivariable analysis, pretreatment predictors included age (aOR 1.06, 95% CI: 1.04-1.08, p <0.0001), a history of atrial fibrillation (aOR 2.65, 95% CI: 1.31-5.47, p=0.007), higher baseline NIHSS (aOR 1.06, 95% CI: 1.01-1.11, p=0.02), anterior cerebral artery (ACA) lesion (aOR 2.72, 95% CI: 1.12-6.82, p=0.03), and higher glucose levels (mmol/dL) (aOR 1.03, 95% CI: 1.01-1.08, p=0.02) and lower ASPECTS (aOR 1.36, 95% CI: 1.008-1.85, p=0.04). The multivariable model demonstrated strong predictive accuracy, with an area under the receiver operating characteristic (ROC) curve of 0.789. Conclusions: This study demonstrates that advanced age, higher NIHSS scores, a history of atrial fibrillation, pre-operative glucose levels, ACA lesions, and lower ASPECTS are predictors of very poor outcomes in patients with large ischemic core thrombectomy, which may inform treatment decisions of EVT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".