What is the PREVENT Score for Cardiovascular Disease?
The PREVENT (Predicting Risk of Cardiovascular Disease EVENTs) equations are the American Heart Association's newest cardiovascular risk assessment tool, developed in 2024 to replace the Pooled Cohort Equations (PCE), providing more accurate 10-year risk predictions for total CVD, atherosclerotic CVD, and heart failure in adults aged 30-79 years without known CVD. 1
Key Features of PREVENT
PREVENT represents a major advancement over previous risk calculators by incorporating cardiovascular-kidney-metabolic (CKM) syndrome factors and eliminating race-based calculations. 2, 1
Core Risk Factors Included:
- Traditional factors: Age, sex, smoking status, systolic blood pressure, total and HDL cholesterol, diabetes status, and use of antihypertensive or statin medications 1
- Kidney function: Estimated glomerular filtration rate (eGFR) as a standard component 1
- Optional enhanced factors: Urine albumin-to-creatinine ratio, hemoglobin A1c, and Social Deprivation Index 1
- Body mass index: Incorporated to reflect metabolic risk 2
What PREVENT Predicts:
- Total CVD risk (atherosclerotic CVD plus heart failure) 1
- Atherosclerotic CVD risk separately 1
- Heart failure risk separately 1
Performance and Validation
PREVENT demonstrates excellent discrimination with C-statistics of 0.794 in women and 0.757 in men, with good calibration across diverse populations. 1 External validation in over 3.3 million participants from 21 datasets confirmed accurate risk prediction. 1 Independent validation using NHANES data showed even better discrimination (C-statistic 0.890) and significantly outperformed the PCE (net reclassification index 0.093). 3
Clinical Impact: How PREVENT Changes Risk Classification
PREVENT substantially lowers estimated cardiovascular risk compared to the PCE, with the average 10-year ASCVD risk dropping from 9.1% to 4.7% in the US population. 4
Risk Reclassification Patterns:
- High-risk category reduction: Under PCE, 12.5% of the population was classified as high-risk (≥20%); under PREVENT, only 0.4% remain in this category 4
- Among those previously high-risk: Only 3.5% remain high-risk with PREVENT, while 93% are reclassified to intermediate risk 4
- Kidney disease emphasis: PREVENT assigns higher risk when eGFR is abnormal (45 or 30 mL/min/1.73m²) and lower risk when eGFR is normal, substantially altering treatment thresholds 5
Important Clinical Caveats
The dramatic reduction in high-risk classifications may lead to undertreatment of patients who would have received preventive therapy under previous guidelines. 4, 5 When eGFR is normal, PREVENT identifies lower risk in 18-88% of cases compared to standard algorithms, potentially diminishing implementation of preventive therapy. 5 Conversely, with abnormal eGFR, PREVENT identifies higher risk in 4-94% of simulations, appropriately emphasizing kidney disease as a cardiovascular risk factor. 5
PREVENT addresses the nearly twofold overprediction problem of the PCE, but this correction means fewer patients will qualify for statin therapy or other preventive interventions based solely on risk thresholds. 2
Comparison with European Risk Tools
Unlike PREVENT, European guidelines use the SCORE2 and SCORE2-OP calculators, which predict fatal and nonfatal CVD events but use different age-specific risk thresholds and geographic calibration. 6 SCORE2 was developed from 45 European cohorts and calibrated to four geographic risk regions (low, moderate, high, very high), with age-specific thresholds that differ substantially from American guidelines. 6 The European approach uses lower percentage thresholds for defining high risk compared to US guidelines. 6
Implementation Considerations
Endorsement by clinical practice guidelines and effective integration into electronic health record workflows will be essential for PREVENT to achieve its potential in reducing CVD burden. 2 The inclusion of Social Deprivation Index as an optional variable allows incorporation of social determinants of health, representing a significant advance in personalized risk assessment. 2, 1
Healthcare systems must monitor whether the lower risk estimates improve overall public health outcomes or lead to complacency in cardiovascular disease prevention efforts. 4, 5