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Integrating cardiac biomarkers into the AHA PREVENT equations: impact on atherosclerotic cardiovascular disease risk
Ning Dong1,2, Yiran Wang2, Fan Tang2
1Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin 150086, China.
Insights
Integrating cardiac biomarkers like hs-cTn and NT-proBNP into the AHA PREVENT equations significantly improves atherosclerotic cardiovascular disease risk prediction. This enhances primary prevention strategies, particularly for borderline-risk individuals.
Area of Science:
- Cardiology
- Preventive Medicine
- Biomarker Research
Background:
- Cardiac biomarkers (hs-cTn, NT-proBNP) predict atherosclerotic cardiovascular disease (ASCVD) events.
- These biomarkers are not currently integrated into the AHA PREVENT equations for primary prevention.
- This study assesses their added value and clinical utility within the PREVENT framework.
Purpose of the Study:
- To evaluate the incremental value of high-sensitivity troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for primary ASCVD prevention.
- To assess the clinical utility of integrating these biomarkers into the AHA PREVENT equations.
- To identify high-risk individuals who might be missed by traditional risk factors alone.
Main Methods:
- Pooled analysis of 15,477 ASCVD-free participants from ARIC and MESA cohorts, with external validation in UK Biobank (N=40,359).
- Median follow-up of 12.1 years, with 1,836 ASCVD events.
- Model performance assessed using C-index, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis (DCA).
Main Results:
- Elevated biomarkers in low/borderline-risk individuals (<7.5%) showed higher event rates (HR 2.74) than high-risk individuals (≥7.5%) with normal biomarkers (HR 1.42).
- Biomarker integration reclassified 16.4% of low-risk and 25.8% of borderline-risk individuals into intermediate/high-risk categories.
- The augmented PREVENT model significantly improved discrimination (ΔC-index: 0.022) and reclassification (NRI: 0.193; IDI: 0.102), with highest gains in borderline-risk groups.
- DCA showed a three-fold increase in net benefit at the 7.5% threshold, identifying more true positives without increasing over-treatment.
Conclusions:
- Integrating cardiac biomarkers into PREVENT equations enhances ASCVD risk prediction.
- This approach identifies high-risk individuals missed by traditional factors.
- Optimizes primary prevention strategies, especially for borderline-risk populations, improving clinical yield.
Aims:
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.
Methods And Results:
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).
Conclusion:
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.
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