New Tool Predicts Heart Disease Risk More Accurately
AI-Free Model Enhances Preventive Care for Diverse Populations
A groundbreaking risk prediction tool from the American Heart Association (AHA) demonstrates superior accuracy in identifying cardiovascular disease (CVD) risk across varied patient groups, according to recent findings. This development promises to refine preventive healthcare strategies by more precisely flagging individuals at elevated risk.
PREVENT Equations Show Strong Performance
The recently developed Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations offer healthcare providers a more precise method to estimate a patient’s 10-year CVD risk. Sadiya Khan, a leading cardiovascular epidemiologist and co-first author of the study, highlighted the tool’s critical role in bolstering preventive care, particularly for vulnerable communities.
“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.”
—Sadiya Khan, Magerstadt Professor of Cardiovascular Epidemiology
The AHA reports that over 127 million U.S. adults experienced cardiovascular disease between 2017 and 2020, underscoring the urgent need for effective risk assessment tools.
Addressing Social Determinants in Risk Prediction
Recognizing race as a social construct rather than a biological factor, the PREVENT equations deliberately exclude race as a direct predictor. This approach aims to mitigate concerns about potential underestimation of risk in minority groups who may face systemic racism and discrimination, factors known to impact cardiovascular health.
“There has been growing awareness of race as a social construct that has led to discussions about the role of race in clinical algorithms,” Khan explained. “As such, the new PREVENT equations did not include race as a predictor. However, removing race as a predictor has also raised concerns that it may underestimate risk in individuals who are more likely to experience racism or discrimination, which increase CVD risk.”
The study examined the PREVENT equations’ efficacy within a large, diverse cohort of U.S. veterans, specifically evaluating their performance in a high-risk population.
Study Validates PREVENT Across Diverse Groups
Investigators analyzed data from over 2.5 million U.S. veterans aged 30 to 79 without a history of CVD or kidney failure. The cohort represented various racial and ethnic groups, including Asian/Native Hawaiian/Pacific Islander, Hispanic, non-Hispanic Black, and non-Hispanic white individuals.
The findings indicate that the PREVENT equations maintained similar performance levels across these diverse groups, outperforming the current clinical standard, the Pooled Cohort Equations, in accurately estimating CVD risk.
“Race is a complex social construct that is often used as a proxy to represent lived experiences of racism and discrimination,” Khan stated. “But the pervasive effects of racism also influence CVD risk factors, such as high blood pressure and diabetes, which are included in the PREVENT equations. Thus, even without race in the equations, PREVENT captures the effects of racism on CVD risk through these risk factors.”
This suggests that race is not a necessary component for precise CVD risk assessment.
“Providing a person different clinical care based on their race is potentially harmful because it suggests that race is a biological determinant of risk or that there are differences in how Black Americans develop heart disease that are inherent to racial identity,” Khan elaborated. “In contrast, raising awareness for the impact of adverse social factors and structural racism in the development of risk factors and CVD is critical.”
The PREVENT model empowers healthcare providers to proactively identify patients at higher risk, facilitating earlier interventions. This could include encouraging lifestyle changes like structured exercise programs or initiating medications such as GLP-1 receptor agonists sooner.
“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,” Khan concluded. “Accurate predictive models are an invaluable part of preventive medicine.”
Future research will explore PREVENT’s performance in global settings and its potential to guide personalized interventions for CVD risk reduction.
