Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors
Integrating traditional risk factors and biomarkers improves prediction and risk stratification of new-onset MACE. The BRS shows promise as a tool for identifying high-risk individuals, with the potential to support personalized CVD prevention and management strategies.
Authors
- Ruiyan Liu (ORCID: https://orcid.org/0000-0001-5456-0423)
- Zhihao Li (ORCID: https://orcid.org/0000-0002-1597-2676)
- Jianxi Gao (ORCID: https://orcid.org/0000-0002-3952-208X)
- Jingxiang Fu (ORCID: https://orcid.org/0000-0003-3009-5269)
- Xi-Ru Zhang
- Dan Liu (ORCID: https://orcid.org/0000-0001-6791-1704)
- Yan He (ORCID: https://orcid.org/0009-0006-6857-351X)
- Peidong Zhang (ORCID: https://orcid.org/0000-0003-3243-691X)
- Junwei Huang (ORCID: https://orcid.org/0000-0003-2005-0630)
- Hongwei Zhou (ORCID: https://orcid.org/0000-0003-2472-8541)
- Wen-Fang Zhong
- Zhuang Li (ORCID: https://orcid.org/0000-0001-5039-8617)
Institutions
- Zhujiang Hospital (CN)
- Guangdong Provincial Center for Disease Control and Prevention (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-04-02
- DOI
- https://doi.org/10.1186/s12933-025-02711-x
- Citations
- 17
- Primary Topic
- Adipokines, Inflammation, and Metabolic Diseases
- Type
- article
- Field-Weighted Citation Impact
- 23.02
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- National Key Research and Development Program of China