A Prospective Economic Evaluation of Rapid Endovascular Therapy for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: During the Randomized Assessment of Rapid Endovascular Treatment (EVT) of Ischemic Stroke (ESCAPE) trial, patient-level micro-costing data were collected. We report a cost-effectiveness analysis of EVT, using ESCAPE trial data and Markov simulation, from a universal, single-payer system using a societal perspective over a patient's lifetime. METHODS: Primary data collection alongside the ESCAPE trial provided a 3-month trial-specific, non-model, based cost per quality-adjusted life year (QALY). A Markov model utilizing ongoing lifetime costs and life expectancy from the literature was built to simulate the cost per QALY adopting a lifetime horizon. Health states were defined using the modified Rankin Scale (mRS) scores. Uncertainty was explored using scenario analysis and probabilistic sensitivity analysis. RESULTS: The 3-month trial-based analysis resulted in a cost per QALY of $201,243 of EVT compared to the best standard of care. In the model-based analysis, using a societal perspective and a lifetime horizon, EVT dominated the standard of care; EVT was both more effective and less costly than the standard of care (-$91). When the time horizon was shortened to 1 year, EVT remains cost savings compared to standard of care (∼$15,376 per QALY gained with EVT). However, if the estimate of clinical effectiveness is 4% less than that demonstrated in ESCAPE, EVT is no longer cost savings compared to standard of care. CONCLUSIONS: Results support the adoption of EVT as a treatment option for acute ischemic stroke, as the increase in costs associated with caring for EVT patients was recouped within the first year of stroke, and continued to provide cost savings over a patient's lifetime.Clinical Trial Registration: NCT01778335.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".