A recent study published in Nature Medicine reveals that a new tool developed by the American Heart Association (AHA), called Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations, offers more accurate cardiovascular disease (CVD) risk estimation than current models. The PREVENT equations, created in 2023, could significantly enhance preventive care efforts according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author.
"Testing the new PREVENT equations on a diverse group of patients is essential for giving primary care providers and cardiologists confidence that these equations can accurately predict CVD risk, especially in vulnerable populations," said Khan, who also serves as an associate professor in several departments focused on health determinants and preventive medicine.
In 2017-2020 alone, over 127 million U.S. adults were affected by cardiovascular disease according to a recent AHA report. To address this burden, the PREVENT equations were developed to optimize preventative care and improve patient outcomes, with Khan leading their development.
A notable feature of the PREVENT equations is the exclusion of race as a biological risk factor, recognizing it instead as a social construct. This approach has sparked concerns about potentially underestimating CVD risk in racial and ethnic minority groups that experience systemic racism or discrimination.
"The omission of race from clinical algorithms has been debated, and while it addresses race as a social construct, we need to ensure models like PREVENT accurately reflect the healthcare needs of all populations. In our study, we evaluated how the PREVENT equations perform in a high-risk veteran population comprising diverse racial and ethnic groups," Khan explained.
Using data from the Veterans Health Administration (VHA) on over 2.5 million U.S. veterans aged 30-79 with no history of CVD or kidney failure, researchers found that the PREVENT equations performed consistently well across different races and ethnicities, outshining the current standard, Pooled Cohort Equations.
"Race is a proxy for discrimination experiences which influence risk factors like high blood pressure and diabetes included in the PREVENT equations. Hence, even though race isn't directly factored into the model, its effects on health disparities are indirectly reflected," Khan commented.
Khan emphasized that accurate CVD risk prediction does not require racial categorization. "Tailoring clinical care based on a patient's race is detrimental as it incorrectly implies inherent biological differences. Focusing instead on social factors and racism, which affect key risk factors like blood pressure and diabetes, is vital."
Overall, the PREVENT tool can aid healthcare providers in identifying high-risk patients earlier, allowing for preventive interventions such as lifestyle changes or appropriate medications.
"PREVENT could help predict those likely to develop CVD, including heart failure. Early lifestyle changes like exercise or using specific medications may enhance cardiovascular outcomes," Khan said. She noted that her team continues to assess PREVENT's performance in diverse settings, striving to use accurate predictive models for preventing CVD and reducing healthcare costs.