Assessing Deceased Donor Kidneys Through Post-Transplant Survival Prediction Algorithms
Assessing Deceased Donor Kidneys Through Post-Transplant Survival Prediction Algorithms
AST welcomes Drs. Potluri, Reese, Basu, and Adler to discuss improving kidney allograft survival prediction models by including recipient characteristics, and how enhanced models have potential to improve the system of kidney allocation.
- 2:00 PM - 3:00 PM EST
- Virtual
"Assessing Deceased Donor Kidneys Through Post-Transplant Survival Prediction Algorithms"
(Am J Kidney Dis. 2025 Oct 22:S0272-6386(25)01101-1. doi: 10.1053/j.ajkd.2025.07.016.)
In this article:
The Kidney Donor Risk Index (KDRI) is widely used to rank the quality of deceased-donor kidneys and is integrated into the U.S. kidney allograft allocation system. However, the KDRI has modest predictive accuracy for allograft survival, and recent revisions to the KDRI, which removed donor race and hepatitis C virus status, also revealed model calibration problems. This study aimed to evaluate novel approaches for predicting posttransplant allograft survival. [The authors believe that] improving the discrimination and calibration of kidney allograft survival prediction models is achievable by including recipient characteristics. These enhanced models have potential to improve the system of kidney allocation. Read more.
Speakers:
- Peter Reese, MD, PhD, FAST • Vanderbilt University Medical Center, Nashville, TN
- Vishnu Potluri, MD, MPH • Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
Moderator:
Arpita Basu, MD, MPH, FAST • Emory University School of Medicine, Atlanta, GA
Discussant:
Joel Adler, MD • University of Texas at Austin Dell Medical School, Austin, TX
This AST Journal Club Series webinar is hosted by the AST Education Committee and supported by the AST Kidney Pancreas Community of Practice (KPCOP). All AST Journal Clubs are free but registration is required to attend live.
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