New PREVENT Equations for CVD Risk Assessment: Key Takeaways

Razavi AC, Kohli P, McGuire DK, et al.
PREVENT Equations: A New Era in Cardiovascular Disease Risk Assessment. Circ Cardiovasc Qual Outcomes 2024;Mar 20:[Epublished].

The following are five takeaways from a perspective article on the new PREVENT equations for cardiovascular disease (CVD) risk assessment:

  1. The American Heart Association’s PREVENT (Predicting Risk of CVD EVENTS) equations represent an update of the risk assessment tool, pooled cohort equation (PCE), published in 2013. PREVENT equations were developed using 25 data sets with 3,281,91 adults. The addition of heart failure (HF) allows for risk prediction of global CVD and reflects recent trends in CVD outcomes.
  2. PREVENT equations expanded the age range included in risk equations. It now includes ages from 30 years to 79 years, and allows for the estimation of 10- and 30-year risk. New sex-specific risk calculators were created. Race was removed as a risk variable, given the consideration that race is a social rather than biological construct.
  3. Increases in body mass index (BMI), pre-diabetes, diabetes, and recognition of kidney disease (i.e., cardiovascular-kidney-metabolic [CKM] syndrome) prompted the addition of new risk factors including BMI, glycated hemoglobin, and urine albumin-creatine ratio. The addition of the social deprivation index allows for the inclusion of social determinant of health factors related to CV health and CV risk.
  4. Compared with the base PREVENT models, the PREVENT equations that included hemoglobin, urine albumin-creatinine ratio, and social deprivation index significantly improved the C-statistic, although with modest incremental changes (maximal ΔC-statistic of +0.004 with the addition of each individual risk factor).
  5. Atherosclerotic CVD (ASCVD) outcomes have been expanded from ASCVD (i.e., coronary heart disease and stroke) to include HF. This change reflects the increase in HF since 2013. Over a median of 5 years of follow-up, PREVENT equations (base models) provide good discrimination for total events among women (C-statistic = 0.0794) and men (C-statistic = 0.0757). Similar discrimination was observed with and without the inclusion of HF as an outcome and across racial groups.

Note: The original reference for the PREVENT equations by Khan et al., entitled Development and Validation of the American Heart Association’s PREVENT Equations, was published in Circulation 2024;149:430-49.

Clinical Topics: Prevention

Keywords: Primary Prevention, Risk Assessment

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