ARTICLE: Dynamic Risk Prediction of Graft Failure After Deceased Donor Kidney Transplant
AUTHORS: Heather Thiessen Philbrook, David G Hu, Sumit Mohan, Peter P Reese, Mona Doshi, Divyanshu Malhotra, Sherry G Mansour, Isaac E Hall, Kathleen F Kerr, Chirag R Parikh
JOURNAL: Clin J Am Soc Nephrol. 2025 Oct 6. doi: 10.2215/CJN.0000000883. Online ahead of print.
Abstract
Background: The identification of kidney transplant recipients at high risk of graft failure enables timely interventions to improve outcomes. We developed and validated a risk prediction model that utilizes changes in estimated glomerular filtration rate (eGFR) to dynamically predict three-year graft failure.
Methods: The risk prediction model was developed in a prospective multi-center cohort of deceased donor kidney transplant recipients (2010-2013) and validated in three independent cohorts (a registry cohort and two Electronic Medical Record (EMR) cohorts). The two-stage approach first estimated eGFR trends using a linear mixed-effects model and then utilized these trends in a logistic regression model to predict three-year graft failure. eGFR was calculated using race-free equation, and graft failure was defined as the return to dialysis or re-transplantation, censored for death. The model can be used at any time within the first three years after transplant, and the predicted risk is dynamically updated with each additional eGFR measurement.
Results: In the development cohort (N=1,114), 94 (8%) experienced graft failure within three years of transplant. The model's predictive accuracy improved over time with the increase in available eGFR measurements. The optimism-corrected area under the curve (AUC) was 0.70 (95% confidence interval [CI] 0.63, 0.77) at three months post-transplant and reached an AUC of 0.90 (95% CI 0.85, 0.95) at 30 months post-transplant. Performance was attenuated in the validation cohorts (AUC range 0.60-0.64 at three months to 0.72-0.78 at 30 months), likely due to differences in data collection approaches in ascertaining eGFR.
Conclusion: This risk prediction model has the potential to enhance post-transplant care by identifying high-risk recipients who may benefit from closer monitoring and personalized interventions.
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JHM Newsroom: New Tool Predicts Graft Failure After Kidney Transplant
