First nomogram for predicting interstitial lung disease and pulmonary arterial hypertension in SLE: a machine learning approach
We developed the first nomogram incorporating age, CRP, anti-dsDNA, pericarditis, and SLEDAI to predict SLE-ILD-PAH risk. This machine learning-enhanced tool leverages routine clinical data, enabling early risk stratification and personalized monitoring. Future studies should validate its utility in guiding therapies and improving outcomes.
Authors
- Linjie Song
- Lingyan Xiao (ORCID: https://orcid.org/0000-0001-7455-9868)
- Qian Niu (ORCID: https://orcid.org/0000-0001-7139-384X)
- Qian Li (ORCID: https://orcid.org/0000-0001-8827-939X)
- Meng Wang (ORCID: https://orcid.org/0000-0002-4970-8590)
- Jing Luo (ORCID: https://orcid.org/0000-0002-8057-4068)
- Shuaijun Chen (ORCID: https://orcid.org/0000-0002-0217-1001)
Institutions
- Union Hospital (HK)
- Shanxi Medical University (CN)
- Jining Traditional Chinese Medicine Hospital (CN)
- Central Hospital of Wuhan (CN)
- Shanxi Academy of Medical Sciences (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Respiratory Research
- Published
- 2025-05-24
- DOI
- https://doi.org/10.1186/s12931-025-03273-y
- Citations
- 2
- Primary Topic
- Pulmonary Hypertension Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- National Natural Science Foundation of China