Predicting kidney graft function and failure among kidney transplant recipients
The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.
Authors
- Brad C. Astor (ORCID: https://orcid.org/0000-0002-0876-0069)
- Tom Greene (ORCID: https://orcid.org/0000-0002-3706-7570)
- Liang Li (ORCID: https://orcid.org/0000-0001-5453-3839)
- Wei Peter Yang (ORCID: https://orcid.org/0000-0001-8984-4389)
- Yi Yao (ORCID: https://orcid.org/0000-0003-2601-9423)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- University of Wisconsin–Madison (US)
- University of Utah (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-30
- DOI
- https://doi.org/10.1186/s12874-024-02445-6
- Citations
- 1
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.51
Funders
- National Institutes of Health