Breaking binary in cardiovascular disease risk prediction

被引：0
|
作者：
Yichi Zhang [1 ]
Akl C. Fahed [2 ]
机构：
[1] Massachusetts General Hospital,Department of Medicine
[2] Massachusetts General Hospital,Cardiovascular Research Center
[3] Broad Institute of MIT and Harvard,Cardiovascular Disease Initiative
来源：
npj Cardiovascular Health | / 2卷 / 1期
关键词：
D O I：
10.1038/s44325-024-00041-7
中图分类号：
学科分类号：
摘要：
Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death in the world. However, advances in genetics, omics research, machine learning (ML), and precision medicine have inspired revolutionary new tools in ASCVD risk stratification. Together, polygenic risk scores (PRS) and composite ML-based algorithms help shift the paradigm away from binary predictions towards more comprehensive continuum models. Continued efforts are needed to address socioeconomic and racial disparities in the PRS space.
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