Public Health and Cost Benefits of Successful Reperfusion After Thrombectomy for Stroke
Bibliographic record
Abstract
Background and Purpose- The benefit that endovascular thrombectomy offers to patients with stroke with large vessel occlusions depends strongly on reperfusion grade as defined by the expanded Thrombolysis in Cerebral Infarction (eTICI) scale. Our aim was to determine the lifetime health and cost consequences of the quality of reperfusion for patients, healthcare systems, and society. Methods- A Markov model estimated lifetime quality-adjusted life years (QALY) and lifetime costs of endovascular thrombectomy-treated patients with stroke based on eTICI grades. The analysis was performed over a lifetime horizon in a United States setting, adopting healthcare and societal perspectives. The reference case analysis was conducted for stroke at 65 years of age. National health and cost consequences of improved eTICI 2c/3 reperfusion rates were estimated. Input parameters were based on best available evidence. Results- Lifetime QALYs increased for every grade of improved reperfusion (median QALYs for eTICI 0/1: 2.62; eTICI 2a: 3.46; eTICI 2b: 5.42; eTICI 2c: 5.99; eTICI 3: 6.73). Achieving eTICI 3 over eTICI 2b reperfusion resulted on average in 1.31 incremental QALYs as well as healthcare and societal cost savings of $10 327 and $20 224 per patient. A 10% increase in the eTICI 2c/3 reperfusion rate of all annually endovascular thrombectomy-treated patients with stroke in the United States is estimated to yield additional 3656 QALYs and save $21.0 million and $36.8 million for the healthcare system and society, respectively. Conclusions- Improved reperfusion grants patients with stroke additional QALYs and leads to long-term cost savings. Procedural strategies to achieve complete reperfusion should be assessed for safety and feasibility, even when initial reperfusion seems to be adequate.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".