Integrating cardiac biomarkers into the AHA PREVENT equations: impact on atherosclerotic cardiovascular disease risk stratification

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 high-sensitivity cardiac troponin T (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for primary prevention based on PREVENT equations.

We pooled 15 477 ASCVD-free participants from Atherosclerosis Risk in Communities and Multi-Ethnic Study of Atherosclerosis cohorts (mean age 62.0 years; 55.9% female) for primary analysis, with external validation in UK Biobank (UKB) (n = 40 359). Model performance was assessed via C-index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Over a median 12.1-year follow-up, 1836 events occurred in primary cohort. Individuals with clinical low/borderline risk (<7.5%) but elevated biomarkers exhibited higher observed event rate and hazards ratio [HR 2.74, 95% confidence interval (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; P < 0.001) and reclassification (NRI: 0.193; IDI: 0.102). The reclassification improvement was highest in the 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 1000 individuals without increasing over-treatment. These findings were robustly confirmed in UKB, where the borderline-risk group showed the highest improvement (ΔAUC = 0.049; P < 0.001).

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.