ArticleFrontiers in pharmacology2025
Argatroban in patients with acute ischemic stroke with early neurological deterioration: a cost-effectiveness analysis from the perspective of Chinese healthcare system.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Comparison of clinical efficacy between argatroban and tirofiban in patients with atherosclerotic cerebral infarction complicated by early neurological deterioration.Frontiers in pharmacology · 2026Article
- Prediction model for early neurological deterioration in large artery atherosclerotic stroke.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Studies have shown that argatroban improves 90-day functional outcomes in patients with acute ischemic stroke (AIS) with early neurological deterioration (END). However, its cost-effectiveness in this patient population remains unclear. Methods: A combination of a short-term decision tree and a long-term Markov model was developed to calculate the total cost and effectiveness for Chinese patients with AIS with END treated with intravenous argatroban plus standard therapy or standard therapy alone. Cost data were accessed from our institution, the China National Stroke Registry, and other public sources, while effectiveness data were obtained from the EASE trial and the China Health Statistical Yearbook 2022. The primary outcome was the incremental cost-effectiveness ratio (ICER), with secondary outcomes including total cost, total effectiveness, and incremental effectiveness. One-way sensitivity analysis, probabilistic sensitivity analysis, and scenario analysis were performed to assess certainty, uncertainty, and robustness. Results: For Chinese patients with AIS with END, treatment combining argatroban with standard therapy resulted in a lifetime cost of 138,812 Chinese Yuan (CNY), compared to 136,353 CNY for standard therapy alone. The combined treatment achieved 4.19 quality-adjusted life years (QALYs) (equivalent to 8.43 life years), while standard therapy yielded 3.78 QALYs (equivalent to 8.17 life years). This led to an ICER of 5968 CNY per QALY (9367 CNY per life year), below the willingness-to-pay threshold. One-way sensitivity analysis indicated that argatroban's efficacy was the primary driver of the ICER, consistently remaining below the threshold. PSA showed that argatroban was highly cost-effective in over 99% of cases and dominant in 0.54% of cases. Scenario analysis confirmed the robustness of these findings across various scenarios. Conclusion: Argatroban is highly cost-effective for Chinese patients with AIS and END from the perspective of the Chinese healthcare system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.