Cost-effectiveness of Endovascular Treatment for Acute Stroke with Large Infarct: A United States Perspective
Bibliographic record
Abstract
Background The health economic benefit of endovascular treatment (EVT) in addition to best medical management for acute ischemic stroke with large ischemic core is uncertain. Purpose To assess the cost-effectiveness of EVT plus best medical management versus best medical management alone in treating acute ischemic stroke with large vessel occlusion and a baseline Alberta Stroke Program Early CT Score (ASPECTS) 3–5. Materials and Methods This is a secondary analysis of the randomized RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-acute Embolism–Japan Large Ischemic Core Trial), with enrollment November 2018 to September 2021, in which the primary outcome was the modified Rankin Scale (mRS) score at 90 days. Participants with a baseline ASPECTS 3–5 (on the basis of noncontrast CT and diffusion-weighted imaging) were randomized 1:1 to receive EVT plus best medical management (n = 100) or best medical management alone (n = 102). The primary outcome of the current study was cost-effectiveness, determined according to the incremental cost-effectiveness ratio (ICER). A decision model consisting of a short-term component (cycle length of 3 months) and a long-term Markov state transition component (cycle length of 1 year) was used to estimate expected lifetime costs and quality-adjusted life-years (QALYs) from health care and societal perspectives in the United States. Upper and lower willingness-to-pay (WTP) thresholds were set at $100 000 and $50 000 per QALY, respectively. A deterministic one-way sensitivity analysis to determine the impact of participant age and a probabilistic sensitivity analysis to assess the impact of parameter uncertainty were conducted. Results A total of 202 participants were included in the study (mean age, 76 years ± 10 [SD]; 112 male). EVT plus best medical management resulted in ICERs of $15 743 (health care perspective) and $19 492 (societal perspective). At the lower and upper WTP thresholds, EVT was cost-effective up to 85 and 90 years (health care perspective) and 84 and 89 years (societal perspective) of age, respectively. When analyzing participants with the largest infarcts (ASPECTS 3) separately, EVT was not cost-effective (ICER, $337 072 [health care perspective] and $383 628 [societal perspective]). Conclusion EVT was cost-effective for participants with an ASPECTS 4–5, but not for those with an ASPECTS 3. ClinicalTrials.gov registration no. NCT03702413 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Widjaja in this issue.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".