The tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations which was developed in 2023, could help health care providers more accurately identify patients who have higher CVD risk and enhance preventive care efforts, according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author of the study.

“Evaluating the new PREVENT equations in a diverse sample of patients is critical to provide primary care providers and cardiologists with further assurance that they can utilize these equations to accurately predict patients’ CVD risk, particularly in vulnerable populations,” said Khan, who is also an associate professor of Medical Social Sciences in the Division of Determinants of Health and of Preventive Medicine in the Division of Epidemiology.

More than 127 million U.S. adults had cardiovascular disease (CVD) between 2017 and 2020, according to a recent report from the American Heart Association. Given the high burden of CVD, CVD risk prediction equations have been developed to optimize preventive care and improve patient outcomes including, most recently, the AHA’s PREVENT equations, in which the development was led by Khan.