Cost-effectiveness of antiplatelet agents in secondary stroke prevention: the limits of certainty.

UNLABELLED: Which of the available antiplatelet therapies should be preferred for secondary prevention of recurrent ischemic stroke has been contentious. OBJECTIVE: We applied the Duke Stroke Policy Model (DSPM) to reconsider this issue, paying particular attention to the degree of uncertainty in the estimates of their efficacy. The DSPM is a continuous-time simulation model of stroke development and outcome. METHODS: We modified the inputs to reflect the cost of the drugs aspirin (ASA), extended release dipyridamole/aspirin (DP/A) and clopidogrel (CLO), as well as their relative risk in preventing subsequent ischemic stroke in comparison with placebo (PBO). These relative risks were derived from published reports from the second European Stroke Prevention Study (ESPS-2) and Clopidogrel Versus Aspiring in Patients at Risk of Ischemic Events studies. Precision was addressed by applying bootstrapping to the above estimates of relative risk. The target population was 70-year-old men with nondisabling stroke. The outcome measures were quality-adjusted life-years (QALYs), costs, and costs per QALY. RESULTS: Results of Base Case Analysis: In large part because of its modest drug cost, ASA was cost-effective in comparison with PBO. DP/A tended to have improved outcomes, but at increased costs. CLO was dominated in the base case. RESULTS OF SENSITIVITY ANALYSIS: ASA and DP/A cannot be differentiated on a statistical basis alone. In probabilistic sensitivity analysis, CLO was rarely preferred. CONCLUSIONS: Either DP/A or ASA appear to be a good value in comparison with no treatment, but there is no clear winner between the two. In the absence of a definitive randomized trial, simulation modeling can help clarify the trade-offs between the various antiplatelet agents, but not beyond the constraints imposed by the imprecision in the estimates that can be obtained from the current evidence base.

Duke Scholars

Author David Bruce Matchar Medicine, General Internal Medicine

Author Gregory P. Samsa Biostatistics & Bioinformatics, Division of Biostatistics