New Tool Predicts Cardiovascular Disease Risk More Accurately

Cardiovascular disease (CVD) affects over 127 million adults in the U.S. and remains a leading cause of illness and death. Accurate prediction of CVD risk is essential for guiding early interventions, yet current risk models may not perform equally across diverse populations. Some existing algorithms include race as a variable, which has raised concerns about reinforcing biological assumptions and possibly misestimating risk in minority groups. Recognizing race as a social rather than biological construct, researchers have aimed to build better tools that reflect lived experiences—such as exposure to racism and structural inequities—through clinically measurable factors like blood pressure and diabetes. Now, a newly developed model has been found to more accurately estimate 10-year CVD risk across race and ethnicity groups and could help improve preventive care efforts.

Researchers at Northwestern University Feinberg School of Medicine (Chicago, IL, USA) tested the tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations, that was developed by the American Heart Association (Dallas, TX, USA) in 2023. For the study, the researchers used data from the Veterans Health Administration (VHA) warehouse, which included more than 2.5 million U.S. veterans aged 30 to 79 years without prior CVD or kidney failure. These participants represented diverse racial and ethnic backgrounds, including Asian/Native Hawaiian/Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic white, and other/unknown groups. The PREVENT equations were designed without including race as a predictor to align with the principle that race is a social construct. Instead, they incorporate clinical risk factors like high blood pressure and diabetes, which reflect the downstream effects of structural racism and socioeconomic disparities. This approach aims to provide clinicians with more equitable and personalized tools for estimating patient risk.

The study, published in Nature Medicine, found that the PREVENT equations outperformed the existing Pooled Cohort Equations, which are the current standard for assessing atherosclerotic CVD risk. The model delivered consistent and accurate predictions across all race and ethnicity groups. These findings support the notion that race is not necessary to accurately estimate CVD risk and highlight the importance of using models that reflect social determinants of health through measurable clinical variables. Researchers are now studying the tool’s performance in global contexts and exploring ways to link individual risk profiles to targeted interventions, including structured lifestyle programs and early medication use. The ultimate goal is to reduce healthcare costs, improve outcomes, and enable earlier, more personalized preventive care.

"If we can accurately identify patients who would benefit from earlier interventions, lifestyle changes, or medication management to help prevent the onset of CVD, then we can improve patient outcomes and reduce healthcare spending costs. Accurate predictive models are an invaluable part of preventive medicine," said Sadiya Khan, MD, MSc, co-first author of the study.

Related Links:
Northwestern University Feinberg School of Medicine
American Heart Association