Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes
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 popul...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1511368/full |
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| author | Christoph F. Mahler Felix Friedl Christian Nusshag Claudius Speer Louise Benning Daniel Göth Matthias Schaier Claudia Sommerer Markus Mieth Arianeb Mehrabi Christoph Michalski Lutz Renders Quirin Bachmann Uwe Heemann Markus Krautter Vedat Schwenger Fabian Echterdiek Fabian Echterdiek Martin Zeier Christian Morath Florian Kälble |
| author_facet | Christoph F. Mahler Felix Friedl Christian Nusshag Claudius Speer Louise Benning Daniel Göth Matthias Schaier Claudia Sommerer Markus Mieth Arianeb Mehrabi Christoph Michalski Lutz Renders Quirin Bachmann Uwe Heemann Markus Krautter Vedat Schwenger Fabian Echterdiek Fabian Echterdiek Martin Zeier Christian Morath Florian Kälble |
| author_sort | Christoph F. Mahler |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8798a73bccbf4f42afa7e0df14b583f4 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-8798a73bccbf4f42afa7e0df14b583f42025-01-07T06:46:20ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15113681511368Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomesChristoph F. Mahler0Felix Friedl1Christian Nusshag2Claudius Speer3Louise Benning4Daniel Göth5Matthias Schaier6Claudia Sommerer7Markus Mieth8Arianeb Mehrabi9Christoph Michalski10Lutz Renders11Quirin Bachmann12Uwe Heemann13Markus Krautter14Vedat Schwenger15Fabian Echterdiek16Fabian Echterdiek17Martin Zeier18Christian Morath19Florian Kälble20Department of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, GermanyDepartment of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, GermanyDepartment of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, Klinikum Rechts der Isar, Technische Universität München (TUM), Munich, GermanyDepartment of Nephrology, Klinikum Rechts der Isar, Technische Universität München (TUM), Munich, GermanyDepartment of Nephrology, Klinikum Rechts der Isar, Technische Universität München (TUM), Munich, GermanyDepartment of Nephrology, Hospital Stuttgart, Stuttgart, GermanyDepartment of Nephrology, Hospital Stuttgart, Stuttgart, GermanyDepartment of Nephrology, Klinikum Rechts der Isar, Technische Universität München (TUM), Munich, GermanyDepartment of Nephrology, Hospital Stuttgart, Stuttgart, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyDepartment of Nephrology, University Hospital Heidelberg, Heidelberg, GermanyIn 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.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1511368/fullkidney transplantationdonor selection criteriagraft lossdonor scorekidney donor risk index (KDRI) |
| spellingShingle | Christoph F. Mahler Felix Friedl Christian Nusshag Claudius Speer Louise Benning Daniel Göth Matthias Schaier Claudia Sommerer Markus Mieth Arianeb Mehrabi Christoph Michalski Lutz Renders Quirin Bachmann Uwe Heemann Markus Krautter Vedat Schwenger Fabian Echterdiek Fabian Echterdiek Martin Zeier Christian Morath Florian Kälble Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes Frontiers in Immunology kidney transplantation donor selection criteria graft loss donor score kidney donor risk index (KDRI) |
| title | Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| title_full | Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| title_fullStr | Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| title_full_unstemmed | Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| title_short | Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| title_sort | evaluation of deceased donor kidney offers development and validation of novel data driven and expert based prediction models for early transplant outcomes |
| topic | kidney transplantation donor selection criteria graft loss donor score kidney donor risk index (KDRI) |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1511368/full |
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