Exploring Machine Learning Algorithms to Revise the Kidney Donor Risk Index

Document Type

Conference Proceeding

Publication Date

1-1-2025

Publication Title

Am J Transplant

Abstract

Introduction: The Kidney Donor Risk Index (KDRI), originally based on patients transplanted between 1995-2005, has been demonstrated as a poor predictor of graft failure (C-statistic: 0.62). Recent policy developments have removed race and Hepatitis C virus from the model with recalculation of the variable coefficients, but without re-analysis of the variables included. We sought to develop an updated KDRI in a modern cohort of kidney transplant recipients using both conventional and machine learning algorithms. Methods: Kidney transplant alone recipients transplanted between 2016-2023 were included. To minimize the impact of recipient factors on death-censored graft failure, recipients who were <35 or >65 years, had diabetes, peripheral vascular disease or prior transplant were excluded. The remaining recipients were randomized into development (80%) and testing (20%) cohorts. Regular Cox, Lasso Cox, Elastic Net Cox models, and Random Forest, XgBoost, and Neural Network models were fitted to predict the risk of 1-year death-censored graft failure with their respective C-statistics compared. Shapley plots were generated post-hoc from machine learning models for feature interpretability. Results: In this modern cohort, the original KDRI was a poor predictor of graft failure with a C-statistic of 0.594 but higher than the race-neutral KDRI (C-statistic 0.589). Regular Cox, Lasso Cox, Elastic Net Cox models, and Random Forest all performed equally well, with C-statistic of 0.62. XgBoost and Neural Net survival analysis performed less well (C-statistic 0.55 and 0.59, respectively). Variables included in the new model overlapped with original KDRI (donor age, height, weight, creatinine, diabetes, hypertension) but also included new variables (donor pH and >20% glomerulosclerosis on biopsy), while some models excluded CVA as a cause of death. Conclusion: The discriminatory power

Volume

25

Issue

1

First Page

S96

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