From ASCVD Pooled Cohort Equations to PREVENT: Rethinking Cardiovascular Risk
For more than a decade, the atherosclerotic cardiovascular disease (ASCVD) pooled cohort equations (PCEs) have been the default for estimating a patient's cardiovascular risk. Now a new model is replacing them, and it doesn't just update the math. It changes predictive outcomes and implications for millions of patients.
That shift is the subject of the latest episode of MDAware: Upgrading Clinical Judgment. Host Dr. Shazia Siddique sits down with Dr. Sadiya Khan and Dr. Donald Lloyd-Jones to walk through the PREVENT equations, why they replace a tool that's guided cardiovascular prevention since 2013, and what changes for a clinician deciding whether a patient needs drug therapy.
Meet the Guests
Dr. Sadiya Khan is the Magerstadt Professor of Cardiovascular Epidemiology and a preventive cardiologist at Northwestern, where she led the development and validation of the PREVENT equations. Dr. Donald Lloyd-Jones is the Alexander Graham Bell Professor at Boston University, director of the Framingham Heart Study, and a past AHA president who has shaped risk assessment guidelines going back to the pooled cohort equations themselves.
Why One Outcome Wasn't Enough
Dr. Lloyd-Jones traces the lineage back to the original 1998 Framingham risk equations, built almost entirely on a European ancestry population and limited to predicting coronary heart disease alone. That single-outcome design turned out to have a real cost: women tend to show elevated stroke risk earlier than coronary risk, and the same pattern holds for Black Americans, meaning a coronary-only model was leaving real risk on the table for both groups. The 2013 pooled cohort equations improved on this by pooling data across five NHLBI cohorts, but they still over-predicted risk at the higher end and stopped short of accounting for heart failure, a gap that's grown more relevant as heart failure mortality has climbed even as atherosclerotic disease outcomes have improved.
Building PREVENT Without Race as a Predictor
Dr. Khan walks through the central design decision behind PREVENT: rather than using separate equations by race, as the pooled cohort equations did, PREVENT excludes race and relies on directly measured risk factors instead. Drawing on data from more than six million individuals, including electronic medical record data that wasn't available in 2013, the team tested whether the model was well-calibrated within each race and ethnic group once race was removed as an input. Dr. Khan frames it as a confirmation of a point Dr. Lloyd-Jones makes as well: risk factors themselves are universal, and capturing their levels directly does the calibration work that race-specific equations were trying to approximate.
Calculate, Personalize, Reclassify
Dr. Lloyd-Jones introduces the CPR framework, calculate, personalize, reclassify, as the practical bridge between a PREVENT score and an actual treatment decision. Calculating the 10- and 30-year risk starts the conversation but doesn't end it. Personalizing means layering in factors the equation doesn't capture, like a prior adverse pregnancy outcome. And when the decision still isn't clear, reclassifying with a coronary artery calcium score can settle it, since a score of zero makes a near-term event unlikely regardless of what the risk equation alone suggests. Dr. Khan also walks through how the new 2025 blood pressure guidelines used this same equation to arrive at a 7.5% PREVENT CVD threshold for intensive blood pressure lowering, triangulating between clinical trial data and the prior pooled cohort equation threshold to land on that number.
The Bottom Line
Dr. Khan and Dr. Lloyd-Jones are direct about where the biggest opportunity actually sits: not only in building a better risk score, but in implementing the one clinicians already have. The PREVENT equations are endorsed by the latest guidelines andcover more clinical outcomes, and are available on MDCalc for clinicians to use at the bedside. The harder work now is getting the patients who need therapy onto it, and just as importantly, not over-treating the ones who don't.
MDAware: Upgrading Clinical Judgment is MDCalc's podcast dedicated to breaking down the science behind clinical scores and bringing transparency and confidence to your clinical workflow.