Predicting kidney transplant survival using multiple feature representations for HLAs

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.

Automated summary

This study proposes four new biologically-relevant feature representations for incorporating HLA information into survival analysis algorithms for kidney transplantation. Through evaluation on a database of over 100,000 transplants, it is found that these representations improve prediction accuracy modestly. This could have societal significance in improving transplant survival outcomes and reducing the need for re-transplants.