Abstract Background Cardiac biomarkers independently predict atherosclerotic cardiovascular disease (ASCVD) events but are not integrated into the newly developed AHA PREVENT equations. We evaluated their incremental value and clinical utility of hs-cTn and NT-proBNP for primary prevention based on PREVENT equations. Methods and Results We pooled 15,477 ASCVD-free participants from ARIC and MESA cohorts (mean age 62.0 years; 55.9% female), with external validation in UK Biobank (N=40,359). Over a median 12.1-year follow-up, 1,836 events occurred. Model performance was assessed via C-index, NRI, IDI, and Decision Curve Analysis (DCA). Individuals with clinical low/borderline risk (7.5%) but elevated biomarkers exhibited higher observed event rate and hazards ratio (HR 2.74 95%CI: 2.45-3.06) than those with high clinical risk (≥7.5%) but normal biomarkers (HR 1.42 95%CI: 1.22-1.65). The combined high-risk group exhibited the highest risk (HR 4.46 95%CI: 3.92-5.07). Incorporating biomarkers reclassified 16.4% of low-risk (5%) and 25.8% of borderline-risk (5%-7.5%) individuals into the intermediate-risk category (≥7.5%). The biomarker-augmented PREVENT model was well-calibrated and significantly improved discrimination (ΔC-index: 0.022; p0.001) and reclassification (NRI: 0.193; IDI: 0.102). The reclassification improvement was highest in borderline-risk group. At the 7.5% clinical threshold, DCA demonstrated a three-fold increase in net benefit, identifying 27 additional true-positive cases per 1,000 individuals without increasing over-treatment. These findings were robustly confirmed in UK Biobank, where the borderline-risk group showed the highest improvement (ΔAUC=0.049; p 0.001). Conclusions Integrating cardiac biomarkers into PREVENT equations identifies high-risk individuals masked by traditional factors, optimizing the clinical yield of primary prevention, especially for borderline-risk populations.
Dong et al. (Wed,) studied this question.