Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs

TL;DR

This paper introduces four novel biologically-relevant feature representations for HLA data to improve machine learning predictions of kidney transplant survival times, potentially enhancing donor-recipient matching and outcomes.

Contribution

The paper proposes four new HLA feature representations and evaluates their effectiveness in survival prediction models using a large transplant database.

Findings

HLA feature representations improve prediction accuracy by about 1%.

Enhanced features can lead to better donor-recipient matching.

Potential societal impact through reduced re-transplants.

Abstract

Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.