Researchers have found that a version of the Multi-Ethnic Study of Atherosclerosis (MESA) heart disease risk score that excludes race as a factor is just as effective as the original version that includes it. This finding, presented at the American Heart Association's Scientific Sessions 2024 in Chicago, could have significant implications for predicting heart disease risk across diverse populations.
The MESA score, developed in 2015, is used to predict the risk of coronary heart disease over a 10-year period. It traditionally incorporates factors such as traditional risk factors, sex, race, and coronary artery calcium levels. However, the inclusion of race in medical risk calculations has been a subject of debate, as it may perpetuate health disparities.
Lead investigator Quinn White, a doctoral student at the University of Washington, Seattle, stated, 'This change broadens the potential use of the score, since it can now be calculated for those who do not fit into one of the racial or ethnic groups of the original score and for those who do not wish to disclose their race.'
The study compared the effectiveness of the original MESA score with a version that excluded race and ethnicity. Researchers found virtually no difference in heart disease prediction between the two versions. The score without race had a concordance value of 0.800, while the original score had a value of 0.797, both indicating very good predictive models.
This research is part of the American Heart Association's De-biasing Clinical Care Algorithms project, which aims to investigate and address how race and ethnicity in clinical care algorithms and risk prediction tools affect equity in clinical decision-making. The findings support the development of unbiased tools that do not rely on race or ethnicity to predict heart disease risk.
Dr. Sadiya Khan, associate professor at Northwestern School of Medicine and head of the writing group for the PREVENT equations, emphasized the importance of diverse population samples in developing models and ensuring relevant predictors are included. She noted that with these elements in place, the model performs well even without 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. As risk calculators continue to be revised with contemporary patient data and additional health, social, and historical factors, the goal is to support more equitable clinical decision-making.
This research contributes to the ongoing conversation about the role of race in medical risk calculations and highlights the potential for developing more inclusive and equitable health assessment tools. As the medical community continues to grapple with issues of bias and disparity in healthcare, studies like this one provide valuable insights into creating more effective and fair risk prediction models.
