Aggregate Clinical and Biomarker-Based Model Predicts Adverse Outcomes in Patients With Coronary Artery Disease

Abstract

Despite guideline-based therapy, patients with coronary artery disease (CAD) are at widely variable risk for cardiovascular events. This variability demands a more individualized risk assessment. Herein, we evaluate the prognostic value of 6 biomarkers: high-sensitivity C-reactive protein, heat shock protein-70, fibrin degradation products, soluble urokinase plasminogen activator receptor, high-sensitivity troponin I, and B-type natriuretic peptide. We then develop a multi-biomarker-based cardiovascular event prediction model for patients with stable CAD. In total, 3,115 subjects with stable CAD who underwent cardiac catheterization at Emory (mean age 62.8years, 17% Black, 35% female, 57% obstructive CAD, 31% diabetes mellitus) were randomized into a training cohort to identify biomarker cutoff values and a validation cohort for prediction assessment. Main outcomes included (1) all-cause death and (2) a composite of cardiovascular death and nonfatal myocardial infarction (MI) within 5years. Elevation of each biomarker level was associated with higher event rates in the training cohort. A biomarker risk score was created using optimal cutoffs, ranging from 0 to 6 for each biomarker exceeding its cutoff. In the validation cohort, each unit increase in the biomarker risk score was independently associated with all-cause death (hazard ratio 1.62, 95% confidence interval [CI] 1.45 to 1.80) and cardiovascular death/MI (hazard ratio 1.52, 95% CI 1.35 to 1.71). A biomarker risk prediction model for cardiovascular death/MI improved the c-statistic (∆ 6.4%, 95% CI 3.9 to 8.8) and net reclassification index by 31.1% (95% CI 24 to 37), compared with clinical risk factors alone. Integrating multiple biomarkers with clinical variables refines cardiovascular risk assessment in patients with CAD.