Machine learning-based integration of pericoronary adipose tissue and clinical risk factors for cardiovascular risk prediction in type 2 diabetes: a retrospective cohort study
This study demonstrates the independent predictive value of PCAT parameters for long-term cardiovascular risk in patients with T2DM. The XGBoost model showed promise as a potential clinical decision support tool. Integrating PCAT parameters with conventional risk factors may improve the identification of high-risk individuals and enhance the ability to predict MACE in this population. Clinical trial registration ChiCTR2400079869.
Authors
- Qi-yuan Luo
- Xiaoyan Zheng (ORCID: https://orcid.org/0000-0003-4983-5503)
- Meiyi Su
- Xiaofei Yang (ORCID: https://orcid.org/0000-0002-9847-5009)
- Huiqi Chen (ORCID: https://orcid.org/0000-0002-4608-4495)
- Hong‐Qin Wang
- Yuqing Tang (ORCID: https://orcid.org/0000-0002-5919-1804)
- Guoqing Liu (ORCID: https://orcid.org/0000-0003-4496-8200)
- Sien Guo
- Yue Liu (ORCID: https://orcid.org/0000-0003-1759-9147)
- Lei Wang
- Zhou Wu (ORCID: https://orcid.org/0009-0008-0245-5283)
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Beijing University of Chinese Medicine (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Guangdong Provincial Hospital of Traditional Chinese Medicine (CN)
- Dongguan People’s Hospital (CN)
- Xiyuan Hospital (CN)
- Shenzhen University Health Science Center (CN)
- Guangdong Provincial People's Hospital (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1186/s40001-025-03237-4
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Guangdong Provincial Hospital of Traditional Chinese Medicine
- National Natural Science Foundation of China