Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations: What Clinicians Need to Know?

Area of Science:

• Cardiology
• Public Health
• Epidemiology

Background:

• Traditional cardiovascular disease (CVD) risk calculators, such as Pooled Cohort Equations (PCEs), have limitations in accuracy and inclusivity.
• Existing models may overpredict risk and do not fully account for the complex interplay of metabolic, kidney, and social factors.

Purpose of the Study:

• To review the rationale, development, and implications of the novel Predicting Risk of CVD EVENTs (PREVENT) equations.
• To evaluate PREVENT's performance in assessing total CVD, atherosclerotic CVD (ASCVD), and heart failure (HF) risk.
• To highlight PREVENT's advancements over PCEs, including the integration of cardiovascular-kidney-metabolic (CKM) syndrome factors and social determinants of health.

Main Methods:

• Development of PREVENT equations using diverse, contemporary, real-world datasets.
• Inclusion of key risk factors such as body mass index (BMI) and estimated glomerular filtration rate (eGFR).
• Optional inclusion of albumin-creatinine ratio (ACR) and hemoglobin A1c (HbA1c) for enhanced CKM risk assessment.
• Exclusion of race as a predictor, unlike PCEs.
• Integration of the Social Deprivation Index (SDI) to account for social determinants of health.

Main Results:

• PREVENT equations demonstrate accurate discrimination for predicting total CVD, ASCVD, and HF risk.
• PREVENT addresses the nearly twofold overprediction of ASCVD risk associated with PCEs.
• Estimates of 10-year ASCVD risk using PREVENT are significantly lower than those derived from PCEs.
• PREVENT incorporates CKM syndrome risk factors and allows for the inclusion of social determinants of health (SDH).

Conclusions:

• The PREVENT equations represent a significant advancement in personalized CVD risk assessment.
• PREVENT overcomes limitations of PCEs by incorporating a wider range of CKM risk factors and SDH.
• Further research and guideline endorsement are crucial for the clinical implementation and public health impact of PREVENT in reducing CVD burden.