Developing an explainable machine learning model using body composition to predict cardiovascular mortality in initial dialysis patients: a multicenter study
An explainable machine learning model incorporating CT-derived body composition features accurately predicts CVD-related mortality in initial dialysis patients. This model may facilitate early risk stratification and targeted prevention strategies at dialysis initiation.
Authors
- Jianlu Wei (ORCID: https://orcid.org/0000-0003-3166-5227)
- M. Li
- Shi-mei Hou
- Xia Yang (ORCID: https://orcid.org/0000-0002-3971-038X)
- Xiaoxu Wang (ORCID: https://orcid.org/0000-0003-0444-6950)
- Jingyuan Cao
- Tian-ke Yu
- Guo-hao Zheng
- Jian Xu
- Yao Wang
- Bin Wang
Institutions
- Soochow University (TW)
- Nantong University (CN)
- The First People's Hospital of Changzhou (CN)
- Zhongda Hospital Southeast University (CN)
- The Affiliated Taizhou People's Hospital of Nanjing Medical University (CN)
- Qilu Hospital of Shandong University (CN)
- Yangzhou University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2026-02-18
- DOI
- https://doi.org/10.3389/fphys.2026.1769240
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Government of Jiangsu Province
- Southeast University
- Changjiang Scholar Program of Chinese Ministry of Education