Machine learning–derived multivariate renal function trajectories in acute kidney injury in critically ill patients: a multicentre retrospective study
This study differentiated and stratified AKI subphenotypes among ICU patients by leveraging multivariate renal function trajectories, laying a foundation for personalized therapeutic strategies.
Authors
- Jiaxi Lin (ORCID: https://orcid.org/0000-0003-4171-7457)
- Jinzhou Zhu (ORCID: https://orcid.org/0000-0003-0544-9248)
- Lihe Liu (ORCID: https://orcid.org/0009-0007-5269-8438)
- Lu Liu (ORCID: https://orcid.org/0000-0002-3033-0655)
- Xiaolin Liu (ORCID: https://orcid.org/0000-0002-0664-6027)
- Rui Li (ORCID: https://orcid.org/0000-0001-5448-6868)
- Hao Hong (ORCID: https://orcid.org/0000-0003-1653-4026)
- Shiqi Zhu
- Jingwen Gao (ORCID: https://orcid.org/0000-0002-9772-4608)
- Jing Yang (ORCID: https://orcid.org/0000-0003-3530-3207)
- Yao Wei (ORCID: https://orcid.org/0000-0001-9329-0278)
Institutions
- Soochow University (CN)
- Center for Digestive and Liver Diseases (US)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Clinical Kidney Journal
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1093/ckj/sfaf142
- Citations
- 1
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Jiangsu Commission of Health
- National Natural Science Foundation of China