P-010 Endovascular thrombectomy with or without bridging thrombolysis in acute ischemic stroke: a cost-effectiveness analysis
Bibliographic record
Abstract
Background There is unclear added benefit of intravenous thrombolysis (IVT) with endovascular thrombectomy (EVT). We performed a cost-effectiveness analysis to assess the cost-effectiveness comparing EVT with IVT versus EVT alone. Methods We used a decision tree to examine the short-term costs and outcomes at 90 days after the occurrence of index stroke to compare the cost-effectiveness of EVT alone with EVT plus IVT for patients with stroke. Subsequently, we developed a Markov state transition model to assess the costs and outcomes over 1-year, 5-year, and 20-year time horizons. Treatment costs and clinical outcome inputs were derived from administrative data and literature included in our systematic review, respectively. We estimated total and incremental cost, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER), expressed as an incremental cost per QALY gained of EVT with IVT compared with EVT alone. We calculated the reference case estimates through probabilistic analysis. Results The average costs per patient were estimated to be $47,304, $49,510, $59,770 and $ 76,561 for EVT only strategy, and $55,482, $57,751, $68,314, and $85,611 for EVT with IVT over 90-day, 1-year, 5-year, and 20-year, respectively. The cost saving of EVT only strategy was driven by the avoided medication costs of IVT (ranging from $8,178 to $9,050). The additional IVT led to a slight decrease in QALY estimate during the 90-day time horizon (loss of 0.002 QALY), but a small gain over 1-year and 5-year time horizons (0.011, and 0.0636 QALY). At a willingness-to-pay threshold of $50,000 per QALY gained, the probabilities of EVT only to be cost-effective were 100%, 100%, and 99.3% over 90-day, 1-year, and 5-year time horizons. Conclusion Our cost-effectiveness model suggested that EVT only may be cost-effective for patients with acute ischemic stroke secondary to large vessel occlusion. Disclosures R. Morsi: None. Y. Zhang: None. M. Zhu: None. S. Xie: None. J. Carrión-Penagos: None. H. Desai: None. E. Tannous: None. S. Kothari: None. A. Khamis: None. A. Darzi: None. A. Tarabichi: None. R. Bastin: None. L. Hneiny: None. S. Thind: None. J. Siegler: None. E. Coleman: None. S. Mendelson: None. A. Mansour: None. S. Prabhakaran: None. T. Kass-Hout: None.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".