Background: Post-transplant glomerulonephritis (PTGN) has been associated with inferior long-term allograft survival, and its incidence varies widely in the literature.
Methods: This is a cohort study of 7,623 patients transplanted between 2005 and 2016 at four major transplant UK centres. The diagnosis of glomerulonephritis (GN) in the allograft was extracted from histology reports aided by the use of text-mining software. The incidence of the four most common GN post-transplantation was calculated, and the risk factors for disease and allograft outcomes were analyzed.
Results: In total, 214 patients (2.8%) presented with PTGN. IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and membranoproliferative/mesangiocapillary GN (MPGN/MCGN) were the four most common forms of post-transplant GN. Living donation, HLA DR match, mixed race, and other ethnic minority groups were associated with an increased risk of developing a PTGN. Patients with PTGN showed a similar allograft survival to those without in the first 8 years of post-transplantation, but the results suggest that they do less well after that timepoint. IgAN was associated with the best allograft survival and FSGS with the worst allograft survival.
Conclusions: PTGN has an important impact on long-term allograft survival. Significant challenges can be encountered when attempting to analyze large-scale data involving unstructured or complex data points, and the use of computational analysis can assist.
Keywords: end-stage renal disease; graft failure; kidney transplantation; machine learning; recurrent glomerulonephritis.
Copyright © 2022 Aguiar, Bourmpaki, Bunce, Coker, Delaney, de Jongh, Oliveira, Weir, Higgins, Spiridou, Hasan, Smith, Mulla, Glampson, Mercuri, Montero, Hernandez-Fuentes, Roufosse, Simmonds, Clatworthy, McLean, Ploeg, Davies, Várnai, Woods, Lord, Pruthi, Breen and Chowdhury.