Risk prediction of cardiovascular events in peritoneal dialysis patients
The machine learning model, based on 12 key variables, offers an effective tool for predicting CVEs in PD patients, enabling early identification of high-risk cases. This model has been integrated into a web application.
Authors
- Daohai Zhang (ORCID: https://orcid.org/0000-0002-7760-5054)
- Binhua Liang (ORCID: https://orcid.org/0000-0002-1108-6407)
- Jinghong Zhao (ORCID: https://orcid.org/0000-0001-9750-3285)
- Liang Liu (ORCID: https://orcid.org/0000-0002-5430-2501)
- Liu Zhang (ORCID: https://orcid.org/0000-0003-3721-0071)
- Ting He (ORCID: https://orcid.org/0000-0002-6653-6356)
- Tao Guan (ORCID: https://orcid.org/0000-0002-2156-0192)
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Xinqiao Hospital (CN)
- First People's Hospital of Chongqing (CN)
Publication Details
- Journal
- BMC Nephrology
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1186/s12882-025-04091-6
- Citations
- 1
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Natural Science Foundation of China