Abstract
275 Predicting Deceased Donor Kidney Transplant Outcomes: Comparing KDRI/KDPI with Machine Learning
Highlights
Kidney transplantation is a cost-effective treatment for end-stage renal failure patients that provides a significant survival benefit and improves their quality of life compared to other forms of renal replacement
Machine learning methods can provide a significant improvement over kidney donor risk index (KDRI) for the assessment of kidney offers
This work lays the foundation for the use of machine learning methodologies in transplantation and describes the steps to measure, analyze, and validate future models
Summary
Kidney transplantation is a cost-effective treatment for end-stage renal failure patients that provides a significant survival benefit and improves their quality of life compared to other forms of renal replacement. A machine learning method (random forest) was compared to KDRI for predicting graft failure at 12, 24, and 36 months after transplantation. Methods: Random forest was trained and evaluated with the same deceased donor kidney transplant data (n=70242) initially used to develop KDRI (1995-2005) and included four readily available recipient variables from the estimated post-transplant survival score. Kidney transplantation is a cost-effective treatment for end-stage renal disease (ESRD) patients that provides a significant survival benefit and improves their quality of life compared to other forms of renal replacement; patients gain an estimated six quality-adjusted life years beyond other treatment methods. Approximately 10,000 waiting patients die each year (~22 per day).[7,8] The numbers of patients removed from the waitlist due to death and being too sick to undergo transplantation were 4,830 and 4,411 respectively in 2016.6 The unacceptably high number of deaths from the waitlist could be prevented by increasing deceased donor kidney transplantation (DDKT)
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