Cost-effectiveness of remote robotic mechanical thrombectomy in acute ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: Clinical outcomes following endovascular thrombectomy (EVT) for acute ischemic stroke (AIS) treatment are highly time sensitive. Remote robotic (RR)-EVT systems may be capable of mitigating time delays in patient transfer from a primary stroke center (PSC) to a comprehensive/thrombectomy-capable stroke center. However, health economic evidence is needed to assess the costs and benefits of an RR-EVT system. Therefore, the authors of this study aimed to determine whether performing RR-EVT in suspected AIS patients at a PSC as opposed to standard of care might translate to cost-effectiveness over a lifetime. METHODS: An economic evaluation study was performed from a US healthcare perspective, combining decision analysis and Markov modeling methods over a lifetime horizon to evaluate the cost-effectiveness of RR-EVT in suspected AIS patients at a PSC compared to the standard-of-care approach. Total expected costs and quality-adjusted life-years (QALYs) were estimated. RESULTS: In the cost-effectiveness analysis, RR-EVT yielded greater effectiveness per patient (4.05 vs 3.88 QALYs) and lower costs (US$321,269 vs US$321,397) than the standard-of-care approach. Owing to these lower costs and greater health benefits, RR-EVT was the dominant cost-effective strategy. After initiation of an RR-EVT system, the average costs per year were similar (or slightly reduced), according to this simulation. Sensitivity analyses revealed that RR-EVT remains cost-effective in a wide variety of time delays and cost assumptions. In a one-way sensitivity analysis, RR-EVT remained the most cost-effective strategy when time delays were greater than 2.5 minutes, its complication rate did not exceed 37%, and costs were lower than $54,081. When the cost of the RR-EVT strategy ranged from $19,340 to $54,081 and its complication rate varied from 15% to 37%, the RR-EVT strategy remained the most cost-effective throughout the two ranges. RR-EVT was also the most cost-effective strategy even when its cost doubled (to approximately $40,000) and time delays exceeded 20 minutes. In a probabilistic sensitivity analysis, RR-EVT was the long-term cost-effective strategy in 89.8% of iterations at a willingness-to-pay threshold of $100,000/QALY. CONCLUSIONS: This analysis suggests that RR-EVT as an innovative solution to expedite EVT is cost-effective. An RR-EVT system could potentially extend access to care in underserved communities and rural areas, as well as improve care for socioeconomically disadvantaged populations affected by health inequities.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".