Cost-effectiveness of endovascular thrombectomy for acute ischemic stroke with established large infarct in Germany: a decision tree and Markov model
Bibliographic record
Abstract
BACKGROUND: Recent studies, including the TENSION trial, support the use of endovascular thrombectomy (EVT) in acute ischemic stroke with large infarct (Alberta Stroke Program Early Computed Tomography Score (ASPECTS) 3-5). OBJECTIVE: To evaluate the cost-effectiveness of EVT compared with best medical care (BMC) alone in this population from a German healthcare payer perspective. METHODS: A short-term decision tree and a long-term Markov model (lifetime horizon) were used to compare healthcare costs and quality-adjusted life years (QALYs) between EVT and BMC. The effectiveness of EVT was reflected by the 90-day modified Rankin Scale (mRS) outcome from the TENSION trial. QALYs were based on published mRS-specific health utilities (EQ-5D-3L indices). Long-term healthcare costs were calculated based on insurance data. Costs (reported in 2022 euros) and QALYs were discounted by 3% annually. Cost-effectiveness was assessed using incremental cost-effectiveness ratios (ICERs). Deterministic and probabilistic sensitivity analyses were performed to account for parameter uncertainties. RESULTS: Compared with BMC, EVT yielded higher lifetime incremental costs (€24 257) and effects (1.41 QALYs), resulting in an ICER of €17 158/QALY. The results were robust to parameter variation in sensitivity analyses (eg, 95% probability of cost-effectiveness was achieved at a willingness to pay of >€22 000/QALY). Subgroup analyses indicated that EVT was cost-effective for all ASPECTS subgroups. CONCLUSIONS: EVT for acute ischemic stroke with established large infarct is likely to be cost-effective compared with BMC, assuming that an additional investment of €17 158/QALY is deemed acceptable by the healthcare payer.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".