A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation

TL;DR

This paper introduces a latent space network model for HLA compatibility in kidney transplantation, improving the estimation of compatibility and predicting graft survival times more accurately.

Contribution

It presents a novel latent space model for indirectly observed weighted and signed HLA compatibility networks, enhancing compatibility estimation and graft survival prediction.

Findings

More accurate HLA compatibility estimates

Improved graft survival time predictions

Effective modeling of signed, weighted networks

Abstract

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.