Abstract WP104: Updating Cost-Effectiveness of Stroke Thrombectomy in Patients With Large Core Ischemic Stroke Using 2023 Clinical Trial Data
Bibliographic record
Abstract
Introduction: Stroke thrombectomy (ST) is cost-effective for acute ischemic stroke (AIS) patients with large-vessel occlusion and low Alberta Stroke Program Early CT Score (ASPECTS <6) compared with non-endovascular standard care (SC). However, previous cost-effectiveness analyses (CA) were based on data that were not specific to low ASPECTS or had limited generalizability (e.g., data on Japanese patients with ASPECTS 3-5). As new results from two 2023 clinical trials that included patients with ASPECTS <6 emerged from Western countries and China, we aim to update the cost-effectiveness of ST using new data from different populations and assess how ST treatment delay might affect the comparative effectiveness of ST and SC. Methods: We updated the cost-effectiveness of ST compared with SC in AIS patients aged 67 years with ASPECTS <6 based on the data from two trials conducted in Western countries and China using a previously published Markov model that simulates the lifetime cost and quality-adjusted life years (QALYs) with annual cycles. The difference between ST and SC was characterized by the patient distribution of modified Rankin Scales at 90 days after hospital discharge (90-day mRS) as reported from the two trials. We conducted two separate CA at a willingness-to-pay threshold of $100,000/QALY using the 90-day mRS distribution from each trial with the ST treatment delay as observed in the trials (base case). In addition, we assessed how the effectiveness of ST varied with ST treatment delay up to 6 hours in addition to the observed delay in trials. Results: In the base case, irrespective of two trial datasets, ST is a dominant strategy that saved $8,431-$32,083 and nearly 1 QALY with a positive net monetary benefit (NMB) compared to SC, which had a negative NMB. Regarding ST treatment delay, the QALYs generated from ST decreased as additional treatment delay increased. If the additional ST treatment delay was within 2-3.5 hours, ST could generate more QALYs and was still cost-effective compared to SC. Conclusions: Our findings suggest ST is the preferred strategy over SC in AIS patients with low ASPECTS. ST remains effective and cost-effective even when ST is delayed beyond the median transfer time of 174 minutes in the U.S. reported in Stamm et al. JAMA study.
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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 | 0.088 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".