Document Type
Journal Article
Publication Title
Clinical Kidney Journal
Volume
17
Issue
11
Publisher
Oxford Academic
School
School of Medical and Health Sciences
RAS ID
77138
Funders
National Health and Medical Research Council
Grant Number
NHMRC Number : 1195414
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 cohort, eGFR trajectory was inversely associated with allograft loss [every 10 ml/min/1.73 m2 reduction in eGFR, adjusted hazard ratio [HR, 95% credible intervals (95%CI) 1.31 (1.23-1.39)] and death [1.12 (1.10-1.14)]. Similar estimates were observed in the validation cohort. The respective dynamic area under curve (AUC) (95%CI) estimates for predicting allograft loss at 5-years post-Transplantation were 0.83 (0.75-0.91) and 0.81 (0.68-0.93) for the derivation and validation cohorts. Conclusion: 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.
DOI
10.1093/ckj/sfae314
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Bakar, K. S., Teixeira-Pinto, A., Gately, R., Boroumand, F., Lim, W. H., & Wong, G. (2024). Dynamic prediction of kidney allograft and patient survival using post-transplant estimated glomerular filtration rate trajectory. Clinical Kidney Journal, 17(11). https://doi.org/10.1093/ckj/sfae314