Cost-effectiveness of endovascular treatment versus best medical management in basilar artery occlusion stroke: A U.S. healthcare perspective
Bibliographic record
Abstract
INTRODUCTION: Two recent studies showed clinical benefit for endovascular treatment (EVT) in basilar artery occlusion (BAO) stroke up to 12 h (ATTENTION) and between 6 and 24 h from onset (BAOCHE). Our aim was to investigate the cost-effectiveness of EVT from a U.S. healthcare perspective. MATERIALS AND METHODS: Clinical input data were available for both trials, which were analyzed separately. A decision model was built consisting of a short-run model to analyze costs and functional outcomes within 90 days after the index stroke and a long-run Markov state transition model (cycle length of 12 months) to estimate expected lifetime costs and outcomes from a healthcare and a societal perspective. Incremental cost-effectiveness ratios (ICER) were calculated, deterministic (DSA) and probabilistic (PSA) sensitivity analyses were performed. RESULTS: EVT in addition to best medical management (BMM) resulted in additional lifetime costs of $32,063 in the ATTENTION trial and lifetime cost savings of $7690 in the BAOCHE trial (societal perspective). From a healthcare perspective, EVT led to incremental costs and effectiveness of $37,389 and 2.0 QALYs (ATTENTION) as well as $3516 and 1.9 QALYs (BAOCHE), compared to BMM alone. The ICER values were $-4052/QALY (BAOCHE) and $15,867/QALY (ATTENTION) from a societal perspective. In each trial, PSA showed EVT to be cost-effective in most calculations (99.9%) for a willingness-to-pay threshold of $100,000/QALY. Cost of EVT and age at stroke represented the greatest impact on the ICER. DISCUSSION: From an economic standpoint with a lifetime horizon, EVT in addition to BMM is estimated to be highly effective and cost-effective in BAO stroke.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".