Predictors of Futile Inter-Hospital Transfer for Endovascular Thrombectomy in Anterior Circulation Acute Ischemic Stroke Due to Large Vessel Occlusion
Bibliographic record
Abstract
Background: Endovascular therapy (EVT) is a standard treatment for acute ischemic stroke (AIS) with large vessel occlusion (LVO), but inter-hospital transfers from primary stroke centers (PSCs) to comprehensive stroke centers (CSCs) can result in delayed treatment and worse outcomes. Up to 30–40% of patients transferred may not receive EVT. This study investigates the causes of futile transfers to a CSC in Canada, aiming to identify its predictors. Methods: We conducted a retrospective analysis of consecutive patients transferred for EVT between 1 April 2017 and 31 December 2020, from PSCs and community hospitals (CH) to a CSC in an urban area of Canada. Data on demographics, clinical characteristics, and treatment outcomes were collected. Descriptive and comparative analyses were performed to identify factors contributing to non-receipt of EVT. Results: Of the transferred 326 patients, 241 (73.9%) underwent EVT, and 85 (26%) did not. The main reasons for not performing EVT were recanalization of the target vessel (44.7%), infarct growth (29.4%), clinical improvement or low NIHSS (17.6%), and hemorrhagic transformation (8.2%). Predictors of futility were lower NIHSS at presentation, intravenous thrombolysis (IVT) at the PSC, and greater ASPECTS decay during transport. Conclusions: Our study concluded that 26% of inter-hospital transfers for EVT were futile, primarily due to infarct growth, recanalization of the target vessel, and low NIHSS. These findings suggest that closer monitoring of clinical status, consideration of direct transfers to CSCs, and enhanced triage strategies may help reduce futile transfers and improve patient outcomes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".