The impact of general anesthesia, baseline ASPECTS, time to treatment, and IV tPA on intracranial hemorrhage after neurothrombectomy: pooled analysis of the SWIFT PRIME, SWIFT, and STAR trials
Bibliographic record
Abstract
Background Despite the proven benefit of neurothrombectomy, intracranial hemorrhage (ICH) remains the most serious procedural complication. The aim of this analysis was to identify predictors of different hemorrhage subtypes and evaluate their individual impact on clinical outcome. Methods Pooled individual patient-level data from three large prospective multicenter studies were analyzed for the incidence of different ICH subtypes, including any ICH, hemorrhagic transformation (HT), parenchymal hematoma (PH), subarachnoid hemorrhage (SAH), and symptomatic intracranial hemorrhage (sICH). All patients (n=389) treated with the Solitaire device were included in the analysis. A multivariate stepwise logistic regression model was used to identify predictors of each hemorrhage subtype. Results General anesthesia and higher baseline Alberta Stroke Program Early CT score (ASPECTS) were associated with a lower probability of any ICH (OR 0.36, p = 0.003), (OR 0.80, p = 0.032) and HT (OR 0.54, p = 0.023), (OR 0.78, p=0.001), respectively. Longer time from onset to treatment was associated with a higher likelihood of HT (OR 1.08, p = 0.001) and PH (OR 1.11, p = 0.015). Intravenous tissue plasminogen activator (IV-tPA) was also a strong predictor of PH (OR 7.63, p = 0.013). Functional independence at 90 days (modified Rankin Scale (mRS) 0–2) was observed significantly less frequently in all hemorrhage subtypes except SAH. None of the patients who achieved functional independence at 90 days had sICH. Conclusions General anesthesia and smaller baseline ischemic core are associated with a lower probability of HT whereas IV-tPA and prolonged time to treatment increase the risk of PH after neurothrombectomy. Trial registration numbers SWIFT- NCT01054560 ; post results, SWIFT PRIME- NCT01657461 ; post results, STAR- NCT01327989 ; post results.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".