Race and Ethnicity in Cardiovascular Disease Risk Prediction for Multiethnic Populations: Insights From Global...

Area of Science:

• Cardiology
• Public Health
• Health Disparities

Background:

• Cardiovascular disease (CVD) is a leading global cause of mortality.
• Significant disparities in CVD risk exist across racial and ethnic groups.
• Clinical guidelines vary in addressing these disparities in risk prediction.

Purpose of the Study:

• To compare the approaches to race and ethnicity in three major CVD risk prediction models: AHA's 2024 PREVENT, ESC's 2021 SCORE2, and the Singapore-modified Framingham risk score.
• To analyze how these models incorporate or exclude race/ethnicity and social determinants of health (SDOH).

Main Methods:

• Comparative analysis of the methodologies of the PREVENT, SCORE2, and Singapore-modified Framingham risk score.
• Examination of the inclusion/exclusion of race/ethnicity and SDOH variables within each model.
• Focus on the conceptual underpinnings of race-neutral versus race-adjusted risk prediction.

Main Results:

• The PREVENT model is race-neutral, utilizing the Social Deprivation Index to account for SDOH.
• SCORE2 employs multiplier factors for different ethnicities.
• The Singapore-modified Framingham risk score retains ethnicity as a direct variable.
• This highlights diverse strategies for addressing health disparities in CVD risk assessment.

Conclusions:

• Race-neutral models like PREVENT aim to mitigate the biological conceptualization of race while addressing SDOH.
• Effective implementation of race-neutral models requires robust data infrastructure and diverse clinical trial participation, particularly in diverse regions like Asia.
• Future research should focus on validating and refining models that equitably predict CVD risk across populations.