A Cost-Utility Analysis of Endovascular Thrombectomy in a Real-World Setting
Bibliographic record
Abstract
OBJECTIVE: Endovascular thrombectomy (EVT) is efficacious for ischemic stroke caused by proximal intracranial large-vessel occlusion involving the anterior cerebral circulation. However, evidence of its cost-effectiveness, especially in a real-world setting, is limited. We assessed whether EVT ± tissue plasminogen activator (tPA) was cost-effective when compared with standard care ± tPA at our center. METHOD: We identified patients treated with EVT ± tPA after the Endovascular treatment for Small Core and Anterior circulation Proximal occlusion with Emphasis on minimizing computed tomography to recanalization times trial from our prospective stroke registry from February 1, 2013 to January 31, 2017. Patients admitted before February 2013 and treated with standard care ± tPA constitute the controls. The sample size was 88. Cost-effectiveness was assessed using the net monetary benefit (NMB). Differences in average costs and quality-adjusted life years (QALYs) were estimated using the augmented inverse probability weighted estimator. We accounted for sampling and methodological uncertainty in sensitivity analyses. RESULTS: Patients treated with EVT ± tPA had a net gain of 2.89 [95% confidence interval (CI): 0.93-4.99] QALYs at an additional cost of $22,200 (95% CI: -28,902-78,244) per patient compared with the standard care ± tPA group. The NMB was $122,300 (95% CI: -4777-253,133) with a 0.85 probability of being cost-effective. The expected savings to the healthcare system would amount to $321,334 per year. CONCLUSION: EVT ± tPA had higher costs and higher QALYs compared with the control, and is likely to be cost-effective at a willingness-to-pay threshold of $50,000 per QALY.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".