Abstract TMP67: Impact of Pre-Hospital Stroke Alerts and Parallel Process on Door-to-Puncture Times in Large Vessel Occlusion
Bibliographic record
Abstract
Background: Endovascular mechanical thrombectomy is now the standard of care for acute strokes with large vessel occlusion (LVO). Time to reperfusion is a significant predictor of favorable outcomes in strokes caused by LVO. Pre-hospital notification by Emergency Medical Services (EMS) and parallel in-hospital processes may reduce time to treatment. Methods: A single center stroke redesign initiative was launched with implementation of: 1) EMS pre-hospital stroke alerts comprised of last known well (LKW) time, neurological deficits, estimated time of arrival; 2) immediate notification of NeuroInterventionalist (NI) if presence of severe deficits (e.g., gaze preference, aphasia, hemiplegia); 3) early activation (i.e., pre-imaging) of cath lab team based on clinical judgement of NI. Results: A retrospective analysis was performed on 164 consecutive stroke patients transported by EMS who underwent mechanical thrombectomy for LVO from August 2014 to July 2016. The median NIHSS score was 17. Pre-hospital EMS stroke alerts were called in 80% (n=132) of treated patients. Among patients with EMS alerts, the NI was notified prior to imaging in 64% (n=80) of cases and the cath lab team was mobilized in parallel for 33 patients. The median door-to-puncture times for patients with EMS alerts + cath lab activation pre-imaging vs EMS alerts + cath lab activation post-imaging vs no EMS alerts were: 66, 79, and 100 minutes, respectively (p<0.05). The impact of field notification was even more pronounced after hours: median door-to-puncture time 76 minutes with EMS alerts (n=70) compared to 111 minutes without EMS alerts (n=21). For patients treated with bridging therapy (IV tPA + IA thrombectomy), the picture-to-puncture interval was notably shorter among patients with EMS alerts, 62 vs 80 minutes (p<0.05). Conclusion: We demonstrate a stroke system of care aimed to reduce time to treatment in patients with LVO. In the new era of mechanical thrombectomy, this is the first study to show feasibility and efficacy of pre-hospital EMS stroke alerts triggering early activation of the cath lab team in patients with possible LVO. Development of regional stroke protocols aligning EMS with efficient in-hospital processes are now a top priority.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".