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

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).