Key result
PREVENT equations show reasonable transportability for cardiovascular risk prediction in East Asian populations.
Why the study?
Cardiovascular risk prediction models derived from Western populations often overestimate risk in East Asian cohorts, necessitating evaluation of the PREVENT equations in these populations.
The PREVENT risk equations provide an integrated cardiovascular-kidney-metabolic framework for risk prediction that shows promise for application in East Asia, though regional validation and adaptation remain necessary.
Cardiovascular disease remains a leading cause of morbidity and mortality in East Asia, although its epidemiological profile differs substantially from that of Western populations. As East Asia experiences rapid increases in chronic kidney disease, diabetes, multimorbidity, and heart failure driven by population aging, accurate cardiovascular risk prediction has become increasingly important for the risk-stratified prevention of these diseases. Traditional risk prediction models, including the Framingham Risk Score, Pooled Cohort Equations, and SCORE, were derived largely from Western populations and often overestimated the risk in East Asian cohorts without recalibration. In 2025, the American Heart Association introduced the PREVENT equations to estimate 10-year and 30-year risks of total cardiovascular disease, atherosclerotic cardiovascular disease, and heart failure within an integrated cardiovascular-kidney-metabolic framework. Given the growing globalization of cardiovascular prevention and international clinical research, understanding whether PREVENT performs well in East Asian populations has important implications for the clinical implementation and harmonization of risk assessment frameworks. This review summarizes the evolution of cardiovascular risk prediction, key innovations of PREVENT, and emerging validation evidence from East Asia. Early studies from Korea and China suggest the reasonable transportability of these approaches. However, evidence from Japan remains limited, and further validation and recalibration are warranted. We also discuss the challenges related to heart failure prediction, add-on variables, electronic health record data infrastructure, and AI-assisted risk assessment.
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Sawano et al. (2026) conducted a review in Cardiovascular disease. PREVENT risk equations vs. Traditional risk prediction models (e.g., PCE, FRS) was evaluated. Early validation studies from Korea and China suggest the AHA PREVENT risk equations demonstrate reasonable transportability for cardiovascular risk prediction in East Asian populations.
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