Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach
The presence of tophi, positive PD signals, and increased number of gout flares are significantly associated with CP vulnerability in patients with gout. The proposed machine learning model, integrating gout-specific and cardiovascular factors, provides a novel and effective approach for personalized risk stratification and management in gout patients, bridging the gap between rheumatic inflammation and cardiovascular risk assessment.
Authors
- Kaiyi Yang (ORCID: https://orcid.org/0000-0002-6705-4818)
- Xinyu Gao (ORCID: https://orcid.org/0000-0001-5340-4816)
- Jinshu Zeng
- Yiran Gong (ORCID: https://orcid.org/0000-0002-7434-0265)
- Xiang Xu (ORCID: https://orcid.org/0000-0001-8293-3776)
- Jing Xu (ORCID: https://orcid.org/0000-0003-3368-7673)
- Shuqiang Chen (ORCID: https://orcid.org/0000-0002-7706-6062)
- Lei Yan (ORCID: https://orcid.org/0000-0002-2250-6585)
- Yaxin Deng (ORCID: https://orcid.org/0009-0005-7945-0120)
- Yabin Fang (ORCID: https://orcid.org/0009-0000-3603-4876)
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fmed.2025.1556387
- Primary Topic
- Gout, Hyperuricemia, Uric Acid
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fujian Provincial Department of Science and Technology
- Fujian Medical University