Objective: To develop machine-learning-models with national registry data to predict factors associated with favourable outcomes using supra-marginal ( DRI-Donor Risk Index >1.5) deceased donor kidneys
Design: A retrospective cohort study using UK Transplant Registry data from 2000–2019
Setting: National registry administered by NHS Blood and Transplant in the United Kingdom.
Participants: Adult recipients (n = 6254) of first kidney-alone transplants from very supra-marginal deceased-donors (DRI≥1.5)
Main Outcome Measures: Death-censored graft failure and patient mortality
Predictors: Comprehensive recipient, donor, and transplant characteristics.
Statistical Analysis: Bayesian neural networks, gradient boosting machines, random forest, and SMOTE-balanced bagging classifiers tuned using Bayesian optimisation Cox regression, competing risk analysis, and calibration plots
Results: Overall 5-year graft survival was 81%. The random-forest-model had excellent predictive performance for graft failure (AUC 0.88, 95% CI 0.87–0.89; RMSE 0.29). recipient age > 75-years  (SHR 1.02, 95% CI 1.01–1.03), recipient-BMI >30 (SHR 1.04, 95% CI 1.02-1.07), HLA mismatches >4 (SHR 1.09, 95% CI 1.01–1.17), donor-creatinine > 120 mmol/L (SHR 1.002, 95% CI 1.001–1.003), and rejection within 3 months (SHR 1.56, 95% CI 1.32-1.85) were key determinants of poor survival. Prolonged cold-ischemia-time >14 hours (SHR 1.01, 95% CI 1.007-1.015) was detrimental
Conclusions: Supra-marginal deceased-donor-kidneys can achieve excellent 5-year outcomes with careful recipient selection. Machine-learning accurately predicted factors associated with success.