Dynamic prediction of kidney allograft and patient survival using post-transplant estimated glomerular filtration rate trajectory
This straightforward model, using a single metric of eGFR trajectory, shows good model performance, and effectively distinguish transplant recipients who are at risk of death and allograft loss from those who are not. This simple bedside tool may facilitate early identification of individuals at risk of allograft loss and death.
Authors
- Ryan Gately (ORCID: https://orcid.org/0000-0002-1951-9836)
- K. Shuvo Bakar (ORCID: https://orcid.org/0000-0003-3215-4496)
- Germaine Wong (ORCID: https://orcid.org/0000-0001-8422-7269)
- Wai H. Lim (ORCID: https://orcid.org/0000-0002-3410-3572)
- Farzaneh Boroumand (ORCID: https://orcid.org/0000-0003-2972-3969)
- Armando Teixeira-Pinto
Institutions
- Edith Cowan University (AU)
- The University of Sydney (AU)
- The University of Western Australia (AU)
- Princess Alexandra Hospital (AU)
- Sir Charles Gairdner Hospital (AU)
- Children's Hospital at Westmead (AU)
- Westmead Hospital (AU)
- Princess Alexandra Hospital (GB)
Publication Details
- Journal
- Clinical Kidney Journal
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1093/ckj/sfae314
- Citations
- 1
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.51
Funders
- Medical Research Council
- National Health and Medical Research Council