The potential use of ECG-based prognostic instruments in clinical trials and cost-effectiveness analyses of new therapies in acute cardiac ischemia

Abstract

The dramatic improvements in outcomes in acute cardiac ischemia because of therapeutic advances has led to “diminishing returns” with increasingly intensive therapies. This article explores the potential of electrocardiograph (ECG)-based prognostic instruments to identify patients likely to benefit from intense regimens, even in the absence of overall average benefit in the population, with 2 clinical examples; 1) Reperfusion therapy in acute myocardial infarction (AMI); and 2) anticoagulation/antiplatelet therapy in unstable angina. Based on previously developed, ECG-based prognostic instruments we explored the distribution of potential benefits in individual patients from increasingly intense therapy in both AMI and unstable angina. Predictions were obtained on community-based patient samples with both AMI and unstable angina to examine the distribution of effectiveness and cost-effectiveness. For both AMI and unstable angina, much of the benefit of intensifying therapy can be obtained by targeting a subgroup of patients that can be identified in multivariable dimensions by clinical and ECG characteristics. Treatment of these patients with more potent agents (such as hirudin or the glycoprotein inhibitors in unstable angina) is likely to be both effective and cost-effective. However, treatment of “low benefit” patients is unlikely to be effective or cost-effective, and some candidates for therapy are more likely to be harmed, than to benefit, by the more intensive regimens. Multivariable stratification can improve clinical and economic outcomes in acute cardiac ischemia, particularly when such models help identify “high benefit” patients early in their clinical course. Additionally, using validated models in the planning and execution of clinical trials of new therapies can improve the power of the trial and help target the therapies to patients most likely to benefit.