Integrating cardiac biomarkers into the AHA PREVENT equations: impact on atherosclerotic cardiovascular disease risk stratification
Authors' 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.
Study context
- Population
- Atherosclerotic cardiovascular disease (ASCVD)-free participants (primary prevention)pooled primary cohort (mean age 62.0 years, 55.9% female), external validation in UK Biobank (UKB) · Primary cohort (ARIC and MESA) 15,477 · UK Biobank (UKB) external validation 40,359
- Participants
- 15,477 participants
- Comparison
- Biomarker-augmented PREVENT model vs PREVENT equations
- Outcome
- ASCVD events
- Follow-up
- median 12.1-year follow-up
Methodological detailsAtherosclerosis Risk in Communities and Multi-Ethnic Study of Atherosclerosis cohorts pooled, external validation in UK Biobank (UKB)
- Atherosclerosis Risk in Communities and Multi-Ethnic Study of Atherosclerosis cohorts pooled
- External validation in UK Biobank (UKB)
Performance
As reported in the abstract
Events in primary cohort
1836
Further findings
ASCVD events by clinical risk and biomarker category
| HR (95% CI) | ||
|---|---|---|
| Clinical low/borderline risk with elevated biomarkersReference group not stated | 2.74 (2.45–3.06) | |
| High clinical risk with normal biomarkersReference group not stated | 1.42 (1.22–1.65) | |
| Combined high-risk groupHighest risk; reference group not stated | 4.46 (3.92–5.07) | |
| 0.20.5125Hazard ratio (log scale) |
Reclassification into the intermediate-risk category (≥7.5%) by incorporating biomarkers
- Low-risk (<5%) individuals reclassified
- 16.4%
- Borderline-risk (5-7.5%) individuals reclassified
- 25.8%
Performance of the biomarker-augmented PREVENT model
- ΔC-index
- 0.022; P < 0.001
- Net reclassification improvement (NRI)
- 0.193
- Integrated discrimination improvement (IDI)
- 0.102
- Well-calibrated
- Reclassification improvement highest in the borderline-risk group
Decision curve analysis at the 7.5% clinical threshold
- Increase in net benefit
- three-fold
- Additional true-positive cases per 1000 individuals, without increasing over-treatment
- 27
External validation in UKB, borderline-risk group
- ΔAUC
- 0.049; P < 0.001
- Findings robustly confirmed in UKB; borderline-risk group showed the highest improvement
Original publication
Integrating cardiac biomarkers into the AHA PREVENT equations: impact on atherosclerotic cardiovascular disease risk stratification. Eur Heart J Qual Care Clin Outcomes. Published 26 February 2026. doi:10.
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