Importance of Reperfusion Status after Intra-Arterial Thrombectomy for Prediction of Outcome in Anterior Circulation Large Vessel Stroke
Bibliographic record
Abstract
BACKGROUND: Reperfusion status after intra-arterial thrombectomy (IAT) is a critical predictor of functional outcome after acute ischemic stroke. However, most prognostic models have not included a detailed assessment of reperfusion status after IAT. OBJECTIVE: The aim of this work was to assess the association between successful reperfusion and clinical outcome. METHODS: Clinical, radiological, and procedural variables of patients treated with IAT were extracted from our prospective stroke registry. The association with functional outcome using the modified Rankin Scale (mRS) after 3 months was assessed using multivariable logistic regression. An extension of the modified TICI score, eTICI, was used to classify reperfusion status. The prognostic value of reperfusion status after IAT in addition to age, stroke severity, imaging characteristics, treatment with intravenous thrombolysis, and time from symptom onset to the end of IAT was assessed with logistic regression and summarized with receiver operating characteristic curves. RESULTS: In total, 119 patients were included (mean age 66 years). In multivariable analysis, age >80 years (OR 6.8, 95% CI 1.2-39.8), NIHSS at presentation >15 (OR 7.3, 95% CI 2.3-23.5), and incomplete reperfusion status (eTICI score <2C; OR 10.3, 95% CI 3.5-30.6) were the strongest predictors of a poor outcome (mRS 3-6). Adding reperfusion status to the model improved the prognostic accuracy (AUC 0.88, 95% CI 0.91-0.94). Our results indicate a large difference between using an eTICI cutoff of ≥2C versus ≥2B: a cutoff ≥2C improved the predictive value for a good clinical outcome (2C: positive predictive value, PPV, 0.78; 2B: PPV 0.32). CONCLUSION: Our results promote using reperfusion status for assessing prognosis in ischemic stroke patients treated with IAT. A model using eTICI ≥2C had greater PPV than eTICI ≥2B and could improve prognostic accuracy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".