ReviewJournal of atherosclerosis and thrombosis2026
The PREVENT Risk Equations and Cardiovascular Prevention in East Asia.
Review in Journal of atherosclerosis and thrombosis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
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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