Purpose: Major adverse cardiovascular events (MACE) are now commonly reported as a composite endpoint in pharmaceutical and medical device trials in cardiovascular medicine. Review of lipid lowering trials demonstrated 47 different endpoints in the primary endpoint and 49 endpoints in secondary endpoints in 40 trials evaluated. There is controversy concerning the use of multiple endpoints in these trials and how to combine endpoints in evaluating the benefits of interventions. We sought to determine the magnitude of benefit attained from the prevention of several key MACE commonly reported in clinical trials.
Method: We calculated discounted quality adjusted life expectancy (dQALY) for events prevented for cardiovascular death, myocardial infarction (MI), nonfatal CVA, target lesion revascularization (TLR), and hospitalization for unstable angina. For this analysis we utilized a probabilistic programming language which allows for the analysis of continuous variables and uncertainty.
Result: Preventing an immediate death in a 50 year old male would result in approximately 19 dQALY, while preventing a stroke would save 7.6 dQALY, and preventing an MI would save 2.7 dQALY. Prevention of TLR and hospitalization save the cost of the procedure and hospitalization respectively ($3000-$10,000) and several days of decreased quality of life. For 70 year old men, dQALYs are decreased by 40% for a death and 23% for an MI. Preventing MACE 5 years in the future markedly decreases the dQALYs saved. Women have higher values of dQALY at all ages for death, MI and stroke.
Conclusion: Assuming the value of a dQALY is $50,000, there can be a 300 fold difference in the value of MACEs. While there is no single value to a MACE, the value of MACEs decrease with increasing age and this decrease in value is different for the three major MACEs of death, MI and stroke. Counting MACEs is insufficient for determining the cost-effectiveness of treatments evaluated in clinical trials and other methods are necessary to. To individualize our care of patients, we need a simple, but robust method to determine the expected benefit of treatment.
Candidate for the Lee B. Lusted Student Prize Competition