Abstract P524: Impact of Intra-Procedural Workflow and Time Metrics of Establishing Fast Reperfusion on Clinical Outcomes in the ESCAPE-NA1 Trial
Bibliographic record
Abstract
Background: Time from imaging to establishing reperfusion is a major influencer of clinical outcomes and over the years thrombectomy techniques have evolved rapidly. This has led to improvements in achieving fast and complete reperfusion. We analyzed the impact of various intra-procedural techniques and tools on the speed of reperfusion and correlated procedural duration with probability of achieving good clinical outcomes. Methods: We analyzed intra-procedural time metrics and examined factors leading to delays during EVT. The relationship between outcome (mRS Scale) and procedural time from arterial puncture to time of achieving mTICI 2b-3 First Reperfusion (FRE) was modeled using logistic regression. Results: The various procedural time metrics are summarized in Figure 1. Every 10-minute increase in FRE time reduced the probability of achieving functional independence(90-day modified Rankin Scale 0-2) by 6.7% (P=0.021, adjusted). The medianFRE timewas 25min (IQR 17-39) and was significantly longer in patients with tandem occlusions(median 34min, p 0.0005). General anesthesia vs procedural sedation vs no sedation use did not significantly alter the FRE time (p = 0.1453). The use of BGC (54.2%) was nominally longer FRE (median 26min “IQR 18-38” vs 23ming, “IQR 16-38”; p 0.095)while the use of contact aspiration (n=213) vs retrievable stents (n=676) as the first approach was associated with a shorter FRE time (21min “IQR 14-35” vs 26 min “IQR18-40”, p =0.001). Conclusions: Puncture to first reperfusion time is a significant predictor of clinical outcome in theESCAPE-NA1 trial. Various procedural and anatomical factors influence this timemetric. Figure: Intra-Procedural workflow time metrics expressed in medians and 90th percentiles. The cumulative times are calculated for each major milestone in the procedure for upto three attempts. First reperfusion duration where TICI 2b was achieved is shown in comparison to other procedural time metrics.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".