Predictive Models for Recurrent Membranous Nephropathy After Kidney Transplantation
| Autor: | Edmund Y. M. Chung, MD, Katrina Blazek, BMedSci, Armando Teixeira-Pinto, PhD, Ankit Sharma, PhD, Siah Kim, PhD, Yingxin Lin, PhD, Karen Keung, PhD, Bhadran Bose, MBBS, Lukas Kairaitis, PhD, Hugh McCarthy, PhD, Pierre Ronco, PhD, Stephen I. Alexander, MD, Germaine Wong, PhD |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Transplantation Direct, Vol 8, Iss 9, p e1357 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2373-8731 00000000 |
| DOI: | 10.1097/TXD.0000000000001357 |
| Popis: | Background. 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. Methods. 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). Results. 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. Conclusions. 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. |
| Databáze: | Directory of Open Access Journals |
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