A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation
This work addresses graft failure prediction in kidney transplantation for patients with end-stage renal disease, representing an incremental improvement in modeling HLA compatibility.
The paper tackled the problem of predicting kidney transplant graft survival times by modeling HLA compatibility as a weighted and signed network, proposing a latent space model that improved accuracy in estimating HLA compatibilities and enhanced graft survival time predictions.
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.