Predicting kidney transplant outcomes with partial knowledge of HLA mismatch

Charles Manski, Anat R. Tambur and Michael Gmeiner
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Anat R. Tambur: Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
Michael Gmeiner: Department of Economics, Northwestern University, Evanston, IL 60208

Proceedings of the National Academy of Sciences, 2019, vol. 116, issue 41, 20339-20345

Abstract: We consider prediction of graft survival when a kidney from a deceased donor is transplanted into a recipient, with a focus on the variation of survival with degree of human leukocyte antigen (HLA) mismatch. Previous studies have used data from the Scientific Registry of Transplant Recipients (SRTR) to predict survival conditional on partial characterization of HLA mismatch. Whereas earlier studies assumed proportional hazards models, we used nonparametric regression methods. These do not make the unrealistic assumption that relative risks are invariant as a function of time since transplant, and hence should be more accurate. To refine the predictions possible with partial knowledge of HLA mismatch, it has been suggested that HaploStats statistics on the frequencies of haplotypes within specified ethnic/national populations be used to impute complete HLA types. We counsel against this, showing that it cannot improve predictions on average and sometimes yields suboptimal transplant decisions. We show that the HaploStats frequency statistics are nevertheless useful when combined appropriately with the SRTR data. Analysis of the ecological inference problem shows that informative bounds on graft survival probabilities conditional on refined HLA typing are achievable by combining SRTR and HaploStats data with immunological knowledge of the relative effects of mismatch at different HLA loci.

Keywords: transplant risk assessment; HLA matching; nonparametric prediction; ecological inference (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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