Enhancing predictions of subclinical cardiac dysfunction in SLE patients through integrative machine learning analysis
This case-control study revealed that 2D-STE parameters can be used to predict subclinical cardiac dysfunction in SLE patients, and anti-U1RNP antibodies may be an essential predictive clinical factor. Machine learning may further assist in preliminary screening and quantifying left ventricular systolic dysfunction reasons in SLE patients.
Authors
- Zhiming Lin (ORCID: https://orcid.org/0000-0002-7027-7449)
- Y. Liu (ORCID: https://orcid.org/0009-0001-3030-8327)
- Changlin Zhao (ORCID: https://orcid.org/0000-0002-8668-1075)
- Siwei Xie (ORCID: https://orcid.org/0009-0000-1602-2296)
Institutions
- Johns Hopkins University (US)
- Sun Yat-sen University (CN)
- Johns Hopkins Medicine (US)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Lupus Science & Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1136/lupus-2025-001616
- Primary Topic
- Atherosclerosis and Cardiovascular Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Guangzhou Municipal Science and Technology Project