MESA Heart Disease Risk Score Proves Effective Without Race Factor

A recent study presented at the American Heart Association's Scientific Sessions 2024 in Chicago has revealed that the widely-used MESA heart disease risk score maintains its predictive accuracy even when the race factor is removed. This finding could have significant implications for the future of cardiovascular risk assessment and the ongoing efforts to reduce racial bias in medical algorithms.

The MESA score, originally developed in 2015, combines traditional risk factors, sex, race, and coronary artery calcium scores to predict an individual's risk of developing coronary heart disease over a 10-year period. The new research, led by Quinn White, a doctoral student at the University of Washington, Seattle, demonstrates that a modified version of the score without the race component performs just as effectively as the original.

In the study, researchers compared the original MESA score to a version that excluded race and ethnicity. The results showed virtually no difference in heart disease prediction between the two versions. The race-free score achieved a concordance value of 0.800, while the original score had a value of 0.797, both indicating very good predictive models. Furthermore, the actual rate of heart disease among participants matched the predicted rate for both versions of the risk score.

This development is particularly significant as it addresses growing concerns about the use of race in clinical risk prediction models. Race is not a biological factor, and its inclusion in risk calculations may perpetuate healthcare disparities. The modified MESA score without race could be more broadly applicable, especially for individuals who identify with multiple racial or ethnic groups or those who prefer not to disclose their race.

The study's findings contribute to a larger conversation about the role of race in medical algorithms and decision-making tools. As Sadiya Khan, M.D., M.Sc., from Northwestern School of Medicine, points out, the research underscores the importance of developing models using diverse population samples and including relevant predictors beyond the social construct of race.

While the study represents a step forward in creating more equitable risk assessment tools, it's important to note that the original MESA study included only four racial and ethnic groups, which may not fully represent the diversity of the U.S. population. This limitation suggests that further research with even more diverse cohorts could yield additional insights.

The American Heart Association, which funded this research through its De-biasing Clinical Care Algorithms project, supports the development of unbiased tools that do not rely on race or ethnicity to predict heart disease risk. Jennifer Hall, Ph.D., FAHA, chief of data science for the American Heart Association, emphasized that this research is helping to change assumptions about the role of race in risk calculation.

As medical professionals and researchers continue to refine risk prediction models, the goal is to create tools that support more equitable clinical decision-making. This study represents an important step towards that objective, potentially paving the way for more inclusive and accurate heart disease risk assessments in the future.

The implications of this research extend beyond the MESA score itself. It challenges the medical community to reevaluate the use of race in various clinical algorithms and risk prediction tools across different areas of healthcare. As the field moves forward, there is likely to be an increased focus on developing risk assessment methods that account for health, social, community, and historical factors without relying on broad racial categories.

While these findings are preliminary and await publication in a peer-reviewed journal, they signal a promising direction for cardiovascular risk assessment. As the medical community continues to address issues of equity and bias in healthcare, studies like this one provide valuable insights that could lead to more personalized and fair approaches to predicting and preventing heart disease for all individuals, regardless of their racial or ethnic background.