KLEAR Enables Pre-Transplant Forecasting of Acute Rejection in Deceased-Donor Kidney Transplant Recipients

Kidney transplantation remains the gold standard for treating end-stage renal disease, yet acute T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) significantly compromise early graft outcomes and long-term survival. Current risk assessment relies on low-resolution Human Leukocyte Antigen (HLA) matching, which fails to capture the molecular nuances of the donor-recipient immune interface. This submission introduces KLEAR (Kidney Likelihood Estimator for Acute Rejection), a machine learning framework designed for pre-transplant risk stratification. KLEAR leverages high-resolution HLA typing to derive novel molecular features (E3-MoFs) capturing physiochemical mismatches. To address the challenges of high-dimensional tabular clinical data and severe class imbalance, we propose a two-stage methodology: first, a generative oversampling strategy using a Variational Autoencoder integrated with a Bayesian Gaussian Mixture Model (VAE-GMM) to align the latent manifolds of rejection phenotypes; and second, a hybrid architecture that feeds tree-based leaf embeddings from eXtreme Gradient Boosting (XGBoost) into a dense neural network (DNN). Using a multi-site cohort of 2,013 patients from Mayo Clinic, the largest integrated transplant provider in the United States, KLEAR was trained and validated in a pilot prospective study. KLEAR demonstrates a 5.0-fold increase in AUPRC for TCMR and a 7.5-fold increase for ABMR compared to random classifiers. A small prospective pilot cohort provides feasibility evidence, but its event count is too limited to support deployment claims. Overall, this study highlights how molecular features of HLA mismatches, combined with clinical variables, can improve prediction of TCMR and ABMR and proposes a novel modeling framework for pre-transplant risk stratification. Once further validated in a large-scale, multi-center, prospective study, KLEAR may potentially inform clinical decision-making and support trial enrichment in transplantation.