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ABSTRACT
Background & Aims
The advantage of tenecteplase (TNK) over alteplase (ALT) in managing acute ischemic stroke (AIS) has been reported, but the cost-effectiveness of these two strategies has not received as much attention. The objective of this study was to compare TNK and ALT for the management of AIS patients in Iran in terms of cost-effectiveness.
Methods
This study was carried out from the payer’s perspective in Iran, with a lifetime horizon. A full economic evaluation model was designed as a decision tree and a Markov model. After defining different Markov states, each health state was assigned a utility value, and quality-adjusted life year (QALY) was estimated using that value. The incremental cost-effectiveness ratio (ICER) was ultimately used for evaluating the comparative cost-effectiveness. Both deterministic and probabilistic sensitivity analyses were carried out.
Results
Compared to ALT, TNK can save approximately 4333.81 USD, and is able to increase one unit of QALY while saving approximately 17,450.29 USD. So, Base-case results showed that TNK strongly dominates ALT. Moreover, the base case results were strongly confirmed by deterministic and probabilistic sensitivity analysis.
Conclusions
Base-case and sensitivity analysis showed that TNK is the dominant strategy compared to ALT for the management of AIS patients.
Abbreviations
TNK, tenecteplase; ALT, alteplase; ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life year
Declaration of interest
The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Author contributions
K Hajian: Writing manuscript, collecting information, carrying out the study. R Dezfouli: Writing manuscript, collecting information, carrying out the study. A Darvishi: Designing the model and the computational framework and analyzing the data. R Radmanesh: Supervising, conceiving the original idea. R Heshmat: Supervising, conceiving the original idea. All authors read and approved the final version of the manuscript to be published.