Predictors of a Good Outcome after Endovascular Stroke Treatment with Stent Retrievers
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Successful recanalization after endovascular stroke therapy (EVT) did not translate into a good clinical outcome in randomized trials. The goal of the study was to identify the predictors of a good outcome after mechanical thrombectomy with stent retrievers. METHODS: A retrospective analysis of a prospectively collected database included consecutive patients treated with stent retrievers. We evaluated the influence of risk factors for stroke, baseline NIHSS score, Alberta Stroke Program Early CT (ASPECT) score, recanalization rate, onset-to-recanalization and onset-to-groin puncture time, and glucose levels at admission on good outcomes. The number of stent passes during procedure and symptomatic hemorrhage rate were also recorded. A modified Rankin Scale (mRS) score of 0-2 at 90 days was considered as a good outcome. RESULTS: From January 2011 to 2014, 70 consecutive patients with an acute ischemic stroke underwent EVT with stent retrievers. The absence of a medical history of diabetes was associated with good outcomes. Apart from diabetes, the baseline demographic and clinical characteristics of patients were similar between subjects with poor outcome versus those with good outcomes. Median time from onset to recanalization was significantly shorter in patients with good outcomes 245 (IQR: 216-313 min) compared with poor outcome patients (315 (IQR: 240-360 min); P = 0.023). Symptomatic intracranial hemorrhage was observed in eight (21.6%) of 37 patients with poor outcomes and no symptomatic hemorrhage was seen in patients with good outcomes (P = 0.006). In multivariate stepwise logistic regression analysis, a favorable ASPECT score (ASPECT > 7) and successful recanalization after EVT were predictors of good outcomes. Every 10-year increase was associated with a 3.60-fold decrease in the probability of a good outcome at 3 months. The probability of a good outcome decreases by 1.43-fold for each 20 mg/dL increase in the blood glucose at admission. CONCLUSION: To achieve a good outcome after EVT with stent retrievers, quick and complete recanalization and better strategies for patient selection are warranted. We need randomized trials to identify the significance of tight blood glucose control in clinical outcome during or after 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".