Prevention & lipidsDiagnostic or prognostic study
Integrating cardiac biomarkers into the AHA PREVENT equations: impact on atherosclerotic cardiovascular disease risk stratification
- pooled cohort analysis with external validation
- Atherosclerosis Risk in Communities and Multi-Ethnic Study of Atherosclerosis cohorts pooled
- external validation in UK Biobank (UKB)
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 (mean age 62.0 years; 55.9% female)pooled primary cohort, external validation in UKB n = 40,359 · Primary cohort (ARIC + MESA) 15,477 · UK Biobank (UKB) 40,359
- Participants
- 15,477 participants
- Comparison
- Biomarker-augmented PREVENT model vs PREVENT equations
Performance
As reported in the abstract
ASCVD events · median 12.1-year follow-up
Events in primary cohort
1836
Further findings
HR
| Outcome | clinical low/borderline risk (<7.5%) but elevated biomarkers | high clinical risk (≥7.5%) but normal biomarkers | combined high-risk group | Estimate | P value |
|---|---|---|---|---|---|
| ASCVD risk by clinical risk and biomarker category · comparator group of the HRs not stated in the abstract | 2.74 (95% CI 2.45–3.06) | 1.42 (95% CI 1.22–1.65) | 4.46 (95% CI 3.92–5.07) | ||
| Model performance of biomarker-augmented PREVENT model | ΔC-index 0.022 | P < 0.001 | |||
| External validation in UKB, borderline-risk group · borderline-risk group | ΔAUC 0.049 | P < 0.001 |
- Model performance of biomarker-augmented PREVENT model — net reclassification improvement (NRI)
- 0.193
- Model performance of biomarker-augmented PREVENT model — integrated discrimination improvement (IDI)
- 0.102
Reclassification into intermediate-risk category (≥7.5%) by incorporating biomarkers
- low-risk (<5%) individuals reclassified
- 16.4%
- borderline-risk (5-7.5%) individuals reclassified
- 25.8%
Model performance of biomarker-augmented PREVENT model
- well-calibrated
- reclassification improvement highest in the borderline-risk group
Decision curve analysis at the 7.5% clinical threshold
- additional true-positive cases per 1000 individuals without increasing over-treatment
- 27
- three-fold increase in net benefit
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:10.1093/ehjqcco/qcag025
Summarised from the published abstract; every number is checked against it