Cost‐Effectiveness of Endovascular Thrombectomy in Patients with Large Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVES: Whereas highly cost-effective and cost-saving for patients with small infarcts, whether endovascular thrombectomy (EVT) remains cost-effective in patients with extensive ischemic injury is uncertain. METHODS: We conducted a model-based cost-effectiveness analysis from the United States, Australian, and Spanish societal perspectives, using a 7-state Markov model, with each state defined by the modified Rankin Scale (mRS) score. Initial probabilities at 3 months were derived from the SELECT2 trial. All other model inputs, including transition probabilities, health care and non-health care costs, and utility weights, were sourced from published literature and government websites. Our analysis included extensive sensitivity and subgroup analyses. RESULTS: EVT in patients with large ischemic stroke improved health outcomes and was associated with lower costs from a societal viewpoint. EVT was cost-effective with a mean between-group difference of 1.24 quality-adjusted life years (QALYs), and a cost-saving of $23,409 in the United States, $10,691 in Australia, and $30,036 in Spain, in addition to uncosted benefits in productivity for patients and carers. Subgroup analyses were directionally consistent with the overall population, notably with preserved cost-effectiveness in older patients (≥ 70 years) and those with more severe strokes (National Institutes of Health Stroke Scale [NIHSS] ≥ 20). Sensitivity analyses were largely consistent with the base-case results. INTERPRETATION: EVT demonstrated cost-effectiveness in patients with large core across different settings in the United States, Australia, and Spain, including older patients and those with more severe strokes. These results further support adaptation of systems of care to accommodate the expansion of thrombectomy eligibility to patients with large cores and maximize EVT benefits. ANN NEUROL 2025;97:222-231.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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) | 0.002 | 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".