TY - JOUR
T1 - Evaluation of deceased-donor kidney offers
T2 - development and validation of novel data driven and expert based prediction models for early transplant outcomes
AU - Mahler, Christoph F.
AU - Friedl, Felix
AU - Nusshag, Christian
AU - Speer, Claudius
AU - Benning, Louise
AU - Göth, Daniel
AU - Schaier, Matthias
AU - Sommerer, Claudia
AU - Mieth, Markus
AU - Mehrabi, Arianeb
AU - Michalski, Christoph
AU - Renders, Lutz
AU - Bachmann, Quirin
AU - Heemann, Uwe
AU - Krautter, Markus
AU - Schwenger, Vedat
AU - Echterdiek, Fabian
AU - Zeier, Martin
AU - Morath, Christian
AU - Kälble, Florian
N1 - Publisher Copyright:
Copyright © 2025 Mahler, Friedl, Nusshag, Speer, Benning, Göth, Schaier, Sommerer, Mieth, Mehrabi, Michalski, Renders, Bachmann, Heemann, Krautter, Schwenger, Echterdiek, Zeier, Morath and Kälble.
PY - 2024
Y1 - 2024
N2 - In the face of growing transplant waitlists and aging donors, sound pre-transplant evaluation of organ offers is paramount. However, many transplant centres lack clear criteria on organ acceptance. Often, previous scores for donor characterisation have not been validated for the Eurotransplant population and are not established to support graft acceptance decisions. Here, we investigated 1353 kidney transplantations at three different German centres to develop and validate novel statistical models for the prediction of early adverse graft outcome (EAO), defined as graft loss or CKD ≥4 within three months. The predictive models use generalised estimating equations (GEE) accounting for potential correlations between paired grafts from the same donor. Discriminative accuracy and calibration were determined via internal and external validation in the development (935 recipients, 309 events) and validation cohort (418 recipients, 162 events) respectively. The expert model is based on predictor ratings by senior transplant nephrologists, while for the data-driven model variables were selected via high-dimensional lasso generalised estimating equations (LassoGee). Both models show moderate discrimination for EAO (C-statistic expert model: 0,699, data-driven model 0,698) with good calibration. In summary, we developed novel statistical models that represent current clinical consensus and are tailored to the older deceased donor population. Compared to KDRI, our described models are sparse with only four and three predictors respectively and account for paired grafts from the same donor, while maintaining a discriminative accuracy equal or better than the established KDRI-score.
AB - In the face of growing transplant waitlists and aging donors, sound pre-transplant evaluation of organ offers is paramount. However, many transplant centres lack clear criteria on organ acceptance. Often, previous scores for donor characterisation have not been validated for the Eurotransplant population and are not established to support graft acceptance decisions. Here, we investigated 1353 kidney transplantations at three different German centres to develop and validate novel statistical models for the prediction of early adverse graft outcome (EAO), defined as graft loss or CKD ≥4 within three months. The predictive models use generalised estimating equations (GEE) accounting for potential correlations between paired grafts from the same donor. Discriminative accuracy and calibration were determined via internal and external validation in the development (935 recipients, 309 events) and validation cohort (418 recipients, 162 events) respectively. The expert model is based on predictor ratings by senior transplant nephrologists, while for the data-driven model variables were selected via high-dimensional lasso generalised estimating equations (LassoGee). Both models show moderate discrimination for EAO (C-statistic expert model: 0,699, data-driven model 0,698) with good calibration. In summary, we developed novel statistical models that represent current clinical consensus and are tailored to the older deceased donor population. Compared to KDRI, our described models are sparse with only four and three predictors respectively and account for paired grafts from the same donor, while maintaining a discriminative accuracy equal or better than the established KDRI-score.
KW - donor score
KW - donor selection criteria
KW - graft loss
KW - kidney donor risk index (KDRI)
KW - kidney transplantation
UR - http://www.scopus.com/inward/record.url?scp=85215526090&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2024.1511368
DO - 10.3389/fimmu.2024.1511368
M3 - Article
AN - SCOPUS:85215526090
SN - 1664-3224
VL - 15
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 1511368
ER -