O28/275 Cost utility analysis of bridging intravenous thrombolysis with endovascular thrombectomy
Bibliographic record
Abstract
Introduction Clinical equipoise exists behind bridging intravenous thrombolysis (BT) with endovascular thrombectomy (EVT). Aim of Study To compare the cost-effectiveness of EVT alone vs. BT in acute ischemic stroke (AIS) Methods We conducted a model-based cost-utility analysis comparing the cost-effectiveness of EVT alone vs. BT in AIS. Subsequently, we developed a Markov state transition model to assess the costs and outcomes over 1-year, 5-year, and 20-year time horizons. We considered the impact of disability and recurrent stroke on mortality risk, health-related quality of life, and costs. 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 BT compared with EVT alone. Probabilistic analysis was used to calculate the reference case estimates. Results The average costs per patient were estimated to be $55,503, $57,814, $68,183, and $84,946 for BT, and $47,311, $49,556, $59,625, and $75,898 for EVT only 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 $8,193 to $9,048). The additional thrombolytics led to slight decrease in QALY estimate during the 90-day time horizon (loss of 0.0016 QALY), but a small gain over 1-year, 5-year, and 20-year time horizons (0.0108, 0.0638, and 0.1481 QALY). With similar outcomes and less cost, the EVT only strategy was cost-effective compared with BT. Conclusion Our cost-effectiveness model suggests bridging with thrombolytics may not be cost-effective in AIS secondary to large vessel occlusion. Disclosure of Interest The authors have nothing to disclose.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".