Dynamic prediction of kidney allograft and patient survival using post-transplant estimated glomerular filtration rate trajectory.

ABSTRACT:

Background

Allograft loss is the most feared outcome of kidney transplant recipients. We aimed to develop a dynamic Bayesian model using estimated glomerular filtration rate (eGFR) trajectories to predict long-term allograft and patient survivals.

Methods

We used data from the Australian and New Zealand Dialysis and Transplant registry and included all adult kidney transplant recipients (1980-2017) in Australia (derivation cohort) and New Zealand (NZ, validation cohort). Using a joint model, the temporal changes of eGFR trajectories were used to predict patient and allograft survivals.

Results

The cohort composed of 14 915 kidney transplant recipients [12 777 (86%) from Australia and 2138 (14%) from NZ] who were followed for a median of 8.9 years. In the derivation co

SUBMITTER: Bakar KS 

PROVIDER: S-EPMC11551522 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature