Cost-effectiveness of endovascular thrombectomy in acute stroke patients with large ischemic core
Bibliographic record
Abstract
BACKGROUND: Evidence has shown that endovascular thrombectomy (EVT) treatment improves clinical outcomes. Yet, its benefit remains uncertain in patients with large established infarcts as defined by ASPECTS (Alberta Stroke Program Early CT Score) <6. This study evaluates the cost-effectiveness of EVT, compared with standard care (SC), in acute ischemic stroke (AIS) patients with ASPECTS 3-5. METHODS: An economic evaluation study was performed combining a decision tree and Markov model to estimate lifetime costs (2021 US$) and quality-adjusted life years (QALYs) of AIS patients with ASPECTS 3-5. Incremental cost-effectiveness ratios (ICERs), net monetary benefits (NMBs), and deterministic one-way and two-way sensitivity analyses were performed. Probabilistic sensitivity analyses were also performed to evaluate the robustness of our model. RESULTS: Compared with SC, the cost-effectiveness analyses revealed that EVT yields higher lifetime benefits (2.20 QALYs vs 1.41 QALYs) with higher lifetime healthcare cost per patient ($285 861 vs $272 954). The difference in health benefits between EVT and SC was 0.79 QALYs, equivalent to 288 additional days of healthy life per patient. Even though EVT is more costly than SC alone, it is still cost-effective given better outcomes with ICER of $16 239/QALY. The probabilistic sensitivity analyses indicated that EVT was the most cost-effective strategy in 98.8% (9882 of 10 000) of iterations at the willingness-to-pay threshold of $100 000 per QALY. CONCLUSIONS: The results of this study suggest that EVT is cost-effective in AIS patients with a large ischemic core (ASPECTS 3-5), compared with SC alone over the patient's lifetime.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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".