Predictive Models for Recurrent Membranous Nephropathy After Kidney Transplantation

Recurrent membranous nephropathy (MN) posttransplantation affects 35% to 50% of kidney transplant recipients (KTRs) and accounts for 50% allograft loss 5 y after diagnosis. Predictive factors for recurrent MN may include HLA-D risk alleles, but other factors have not been explored with certainty.

The Australian and New Zealand Dialysis and Transplant registry was used to develop 3 prediction models for recurrent MN (Group Least Absolute Shrinkage and Selection Operator [LASSO], penalized Cox regression, and random forest), which were tuned using tenfold cross-validation in a derivation cohort with complete HLA data. KTRs with MN but incomplete HLA data formed the validation cohort. Model performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC).

One hundred ninety-nine KTRs with MN were included, and 25 (13%) had recurrent MN (median follow-up 5.9 y). The AUC-ROCs for Group LASSO, penalized Cox regression, and random forest models were 0.85 (95% confidence interval, 0.76-0.94), 0.91 (0.85-0.96), and 0.62 (0.57-0.69), respectively, in the derivation cohort, with moderate agreement in selected variables between the models (55%-70%). In their validation cohorts, the AUC-ROCs for Group LASSO and penalized Cox regression were 0.60 (0.49-0.70) and 0.73 (0.59-0.86), respectively. Variables of importance chosen by all models included recipient HLA-A2, donor HLA-DR12, donor-recipient HLA-B65, and HLA-DR12 match.

A penalized Cox regression performed reasonably for predicting recurrent MN and was superior to Group LASSO and random forest models. These models highlighted the importance of donor-recipient HLA characteristics to recurrent MN, although validation in larger datasets is required.