Abstract 24: Differences in Door to Needle and Door In Door Out Times for Tenecteplase vs Alteplase in Acute Ischemic Stroke: Findings From Get With the Guidelines Stroke
Bibliographic record
Abstract
Introduction: Tenecteplase (TNK) has arisen as an alternative to alteplase (ALT) for emergent treatment of acute ischemic stroke. Shorter times to prepare and administer TNK raises the possibility that TNK use leads to faster treatment and transfer times. Hypothesis: We hypothesized that treatment with TNK is associated with shorter door-to-needle (DTN) and door-in-door-out (DIDO) times. Methods: Using the US Get With The Guidelines-Stroke registry, we performed a retrospective, observational cohort study of consecutive patients treated with either TNK or ALT between July 1, 2020 and June 30, 2022. The exposure was treatment with TNK vs ALT. The primary endpoints were DTN and DIDO. We fit generalized linear mixed models to determine the association between TNK (vs ALT) and endpoints after adjustment for key demographic, clinical, and hospital-level variables. A secondary analysis compared changes in DTN among hospitals that switched to TNK in 2021 with at least 10 cases per year pre and post switch. Results: From 2092 sites, 133,228 patients received intravenous thrombolysis. Among the 13,988 (10%) treated with TNK, median age was 70 yrs, median NIHSS 7, 47% female, 21% received endovascular thrombectomy (EVT), and 9% were transferred from the hospital emergency department after receiving lytic. Among 119,240 (90%) treated with ALT, median age was 69 yrs, median NIHSS 7, 48% female; 17% received EVT, and 12% were transferred after lytic. In the primary DTN analysis, time to treatment was shorter with TNK, with mean 47.0 vs 52.7 minutes and DTN ≤60 mins in 77.5% vs 70.7% (TABLE). In the primary DIDO analysis, time to departure was shorter with TNK, 108.3 vs 114.1 minutes. In centers that changed from ALT to TNK during this period DTN times were significantly lower after switching. Conclusions: In this largest study of TNK vs ALT workflow time intervals in ischemic stroke using population-based data, TNK use was associated with more favorable DTN and DIDO times relative to ALT use.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".