Cost-effectiveness of endovascular thrombectomy in patients with low Alberta Stroke Program Early CT Scores (< 6) at presentation
Bibliographic record
Abstract
OBJECTIVE: The utility of endovascular thrombectomy (EVT) in patients with acute ischemic stroke, large vessel occlusion (LVO), and low Alberta Stroke Program Early CT Scores (ASPECTS) remains uncertain. The objective of this study was to determine the health outcomes and cost-effectiveness of EVT versus medical management in patients with ASPECTS < 6. METHODS: A decision-analytical study was performed with Markov modeling to estimate the lifetime quality-adjusted life-years (QALYs) and associated costs of EVT-treated patients compared to medical management. The study was performed over a lifetime horizon with a societal perspective in the US setting. RESULTS: The incremental cost-effectiveness ratios were $412,411/QALY and $1,022,985/QALY for 55- and 65-year-old groups in the short-term model. EVT was the long-term cost-effective strategy in 96.16% of the iterations and resulted in differences in health benefit of 2.21 QALYs and 0.79 QALYs in the 55- and 65-year-old age groups, respectively, equivalent to 807 days and 288 days in perfect health. EVT remained the more cost-effective strategy when the probability of good outcome with EVT was above 16.8% or as long as the good outcome associated with the procedure was at least 1.6% higher in absolute value than that of medical management. EVT remained cost-effective even when its cost exceeded $100,000 (threshold was $108,036). Although the cost-effectiveness decreased with age, EVT was cost-effective for 75-year-old patients as well. CONCLUSIONS: This study suggests that EVT is the more cost-effective approach compared to medical management in patients with ASPECTS < 6 in the long term (lifetime horizon), considering the poor outcomes and significant disability associated with nonreperfusion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".