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This review aims to examine the rationale, development, and implications of the newly developed Predicting Risk of CVD EVENTs (PREVENT) equations for cardiovascular disease (CVD) risk assessment.

The PREVENT equations were developed from diverse, contemporary, real-world datasets and offer accurate discrimination for predicting risk of total CVD and separately, atherosclerotic CVD (ASCVD) and heart failure (HF). It addresses the nearly twofold overprediction of ASCVD risk with PCEs and includes risk factors related to cardiovascular-kidney-metabolic (CKM) syndrome (body mass index and estimated glomerular filtration rate, with the option to include albumin-creatinine ratio and haemoglobin A1C). Unlike PCEs, PREVENT did not include race as a predictor. PREVENT provides an option to add Social Deprivation Index (SDI) as variable in risk prediction which allows incorporation of social determinants of health. Studies indicate that PREVENT estimates for 10-year ASCVD risk are significantly lower than those obtained using PCEs. PREVENT also has potential to assess HF risk and guide potential therapies in the future for the prevention of HF. The PREVENT equations represent a crucial step forward in personalized CVD risk assessment, addressing limitations of PCEs by incorporating a broader range of CKM risk factors and accounting for social determinants of health. While promising for guiding future preventive strategies and public health initiatives, endorsement by guidelines and effective implementation into clinical workflows will be essential to realize its full potential in reducing the burden of CVD.