Abstract 2832: Rate of Change of ASPECTS can predict of outcome in Acute Ischemic Stroke patients undergoing Endovascular Therapy
Bibliographic record
Abstract
Objectives: We sought to assess how rate of infarction impacts outcome in acute ischemic stroke (AIS) patients undergoing endovascular therapy Methods: With institutional approval, we conducted a retrospective chart review on consecutive patients receiving endovascular treatment for anterior circulation AIS at our centers between January 2009 and June 2011. Beside patient’s demographics, we collected data on time from stroke onset to end of endovascular procedure, site of occlusion, and recanalization described as a Thrombolysis in Myocardial Infarction (TIMI) score ≥ 2. Neuro-imaging data included the immediate pre-treatment Alberta Stroke Program Early CT Score (ASPECTS). The ASPECTS score at onset was presumed to be 10. We calculated the rate of infarction as 10-ASPECTS/hour (ΔA). Our endpoint was good outcome defined as a modified Rankin Score ≤ 2 at 90 days. These parameters were entered into univariate and multivariate analyses to determine independent predictors of outcome. Results: Our cohort included 81 patients (45 females, mean age 67±12 years, median stroke score 17, median time from stroke onset to procedure completion 440 minutes). Occlusions were found in the middle cerebral artery (MCA) M2 (11), MCA M1 (46), intracranial internal carotid artery (ICA) (10), ICA origin (2), and tandem ICA origin with intracranial ICA or MCA occlusion (12). The mean baseline ASPECTS was 9 (75 patients had ASPECTS > 7). Recanalization was observed in 50%. The mean change in ASPECTS prior to endovascular therapy was 0.47 units/hour ± 0.72 units. Three parameters predicted a good outcome: recanalization (OR 10.1, 95% CI 2.7, 38.1, p < 0.001), recanalization in the setting of a pre-treatment ASPECTS score > 8 (OR 5.8, 95% CI 1.8, 19.3, p =0.002), recanalization in the setting of a ΔA < 1 unit/hour (OR 2.7, 95% CI 1.0-7.4, p =0.043). In a multivariate analysis controlling for stroke score, ΔA < 1 units/hour still impacted outcome (OR 3.1, 95% CI 1.1-8.9, p=0.039). Conclusions: While rate of infarction may impact the final outcome in patients receiving endovascular therapy for AIS, perhaps a better marker is recanalization in patients with a pre-treatment ASPECTS > 8. Rate of infarction may still be a valuable parameter when selecting patients for this endovascular therapy, in particular those being transferred to treatment sites.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".