JACC: Advances (Aug 2024)
Adoption of the PREVENT (Predicting Risk of Cardiovascular Disease EVENTs) Risk Algorithm
Abstract
Background: The PREVENT (Predicting Risk of cardiovascular disease EVENTs risk algorithm was developed to better reflect the impact of metabolic factors on cardiovascular risk. Objectives: The purpose of this study was to compare the relative performance of PREVENT with standard comparator algorithms (Framingham risk score, pooled cohort equation, SCORE2 [Systematic COronary Risk Evaluation2]) for risk stratification emphasizing the implications of weighing chronic kidney disease. Methods: A simulated cohort was created of males and females aged 40 to 75 years with and without other traditional risk factors and either normal estimated glomerular filtration rates (eGFR 90 or 60 ml/min/1.73 m2) or abnormal eGFR (45 or 30 ml/min/1.73 m2). The concordance and reclassification rates were calculated for each category of risk with emphasis on subjects characterized as moderate risk by the standard comparator algorithms. Results: PREVENT demonstrated increased risk with progressive decreases in eGFR. When the standard comparator algorithms identified moderate risk, PREVENT was concordant in 6% to 88% of simulations. In simulations with normal eGFR, PREVENT identified a lower risk in 18% to 88% and a higher risk in 0% to 12% of simulations. Conversely, with abnormal eGFR, PREVENT identified lower risk in 0% to 26% and higher risk in 4% to 94% of simulations. Conclusions: PREVENT substantially reclassifies risk and has the potential to alter prevention practice patterns. The tendency to assign a lower risk compared to standard algorithms when eGFR is normal may diminish implementation of preventive therapy. National health care systems need to monitor whether such changes improve overall public health.
