E-290 A comparison of thrombolysis with either tpa or tnk in large vessel occlusion patients undergoing transport to a comprehensive stroke centre for mechanical thrombectomy
Bibliographic record
Abstract
Introduction Recent studies have demonstrated that patients with large vessel occlusion treated with TNK showed a higher rate of early recanalization as opposed to tPA. Our centre is the lone comprehensive stroke centre (CSC) in our province of 1.2 million people and the only institution offering mechanical thrombectomy (MT). We set out to compare patients sent to the CSC treated in the periphery with these two models of stroke management for completed MT and for rates of recanalization. Materials and Methods We performed a retrospective review of consecutive patients between January 1, 2022 and December 31, 2023 with anterior circulation large vessel occlusion (LVO) stroke at a peripheral institution that were transferred to the CSC with intention to receive MT. The rates of patients who did not receive MT was compared based on thrombolytic used. We compared baseline demographics and rate of MT completion between the two groups as well as reasons for not receiving MT. Parametric continuous data were compared by Student’s t-test and non-parametric continuous data were compared by Mann-Whitney U-test. Categorical data were compared by chi-squared test. Results 214 patients transferred from peripheral sites were included, 92 of which were treated with tPA and 122 were treated with TNK. Of the patients transferred, 64 (30%) did not receive thrombectomy upon arrival to the comprehensive stroke centre. In total, 27 tPA patients (29%) and 37 (30%) TNK patients did not receive thrombectomy (p=1). The mean age in the tPA population was 73 ± 11.8, and the mean age in the TNK population was 69 ± 16.7 (p=0.463). The number of female patients who received tPA was 10 (37%) and TNK was 19 (51%) (p=0.256). The number of left sided LVOs who received tPA and TNK was 15 (56%) and 19 (51%) (p=0.739), respectively. Mean ASPECT score for both populations was 8 (p=0.648). In the tPA population, reasons for not receiving thrombectomy include recanalization (12 (44%)), symptom improvement (7 (26%)), evolution of stroke (8 (29%)). In the TNK population, reasons for not receiving thrombectomy include recanalization (13 (35%)), symptom improvement (8 (21%)), evolution of stroke (15 (40%)), and anatomy (1 (3%)). There was no statistical difference in the reasons for not receiving thrombectomy between the populations (p=0.715). Conclusions Despite recent trial evidence suggesting superiority of TNK over tPA in terms of early recanalization of LVOs, we did not observe the same trend in real-world data at our CSC. Reasons for this could be numerous and require further data extraction. Additionally, our study provides ongoing evidence that patients treated in the periphery with thrombolysis should continue to be transferred to a CSC providing endovascular therapy given the high rates of MT provided. Disclosures B. Newton: None. A. Persad: None. E. Liu: None. G. Hunter: None. R. Cooley: None. S. Wasyliw: None. B. Graham: None. R. Whelan: None. S. Ahmed: None. L. Peeling: None. M. Kelly: 2; C; Medtronic Inc., Penumbra Inc., Cerenovus Inc. 4; C; Basecamp Vascular SAS, Radical Catheter Technologies, Inc., Endostream Medical, Ltd., Temple Therapeutics.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".