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Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model

Previously we established a prediction model for graft intolerance syndrome requiring graft nephrectomy in patients with late kidney graft failure. The aim of this study is to determine generalizability of this model in an independent cohort. The validation cohort included patients with late kidney...

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Autores principales: Bunthof, Kim, Saboerali, Khalid, Wetering, Jacqueline Van De, Nurmohamed, Azam, Bemelman, Frederike, Zuilen, Arjan Van, Brand, Jan Van Den, Baas, Marije, Hilbrands, Luuk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195885/
https://www.ncbi.nlm.nih.gov/pubmed/37213489
http://dx.doi.org/10.3389/ti.2023.11147
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author Bunthof, Kim
Saboerali, Khalid
Wetering, Jacqueline Van De
Nurmohamed, Azam
Bemelman, Frederike
Zuilen, Arjan Van
Brand, Jan Van Den
Baas, Marije
Hilbrands, Luuk
author_facet Bunthof, Kim
Saboerali, Khalid
Wetering, Jacqueline Van De
Nurmohamed, Azam
Bemelman, Frederike
Zuilen, Arjan Van
Brand, Jan Van Den
Baas, Marije
Hilbrands, Luuk
author_sort Bunthof, Kim
collection PubMed
description Previously we established a prediction model for graft intolerance syndrome requiring graft nephrectomy in patients with late kidney graft failure. The aim of this study is to determine generalizability of this model in an independent cohort. The validation cohort included patients with late kidney graft failure between 2008 and 2018. Primary outcome is the prognostic performance of our model, expressed as the area under the receiver operating characteristic curve (ROC-AUC), in the validation cohort. In 63 of 580 patients (10.9%) a graft nephrectomy was performed because of graft intolerance. The original model, which included donor age, graft survival and number of acute rejections, performed poorly in the validation cohort (ROC-AUC 0.61). After retraining of the model using recipient age at graft failure instead of donor age, the model had an average ROC-AUC of 0.70 in the original cohort and of 0.69 in the validation cohort. Our original model did not accurately predict the graft intolerance syndrome in a validation cohort. However, a retrained model including recipient age at graft failure instead of donor age performed moderately well in both the development and validation cohort enabling identification of patients with the highest and lowest risk of graft intolerance syndrome.
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spelling pubmed-101958852023-05-20 Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model Bunthof, Kim Saboerali, Khalid Wetering, Jacqueline Van De Nurmohamed, Azam Bemelman, Frederike Zuilen, Arjan Van Brand, Jan Van Den Baas, Marije Hilbrands, Luuk Transpl Int Health Archive Previously we established a prediction model for graft intolerance syndrome requiring graft nephrectomy in patients with late kidney graft failure. The aim of this study is to determine generalizability of this model in an independent cohort. The validation cohort included patients with late kidney graft failure between 2008 and 2018. Primary outcome is the prognostic performance of our model, expressed as the area under the receiver operating characteristic curve (ROC-AUC), in the validation cohort. In 63 of 580 patients (10.9%) a graft nephrectomy was performed because of graft intolerance. The original model, which included donor age, graft survival and number of acute rejections, performed poorly in the validation cohort (ROC-AUC 0.61). After retraining of the model using recipient age at graft failure instead of donor age, the model had an average ROC-AUC of 0.70 in the original cohort and of 0.69 in the validation cohort. Our original model did not accurately predict the graft intolerance syndrome in a validation cohort. However, a retrained model including recipient age at graft failure instead of donor age performed moderately well in both the development and validation cohort enabling identification of patients with the highest and lowest risk of graft intolerance syndrome. Frontiers Media S.A. 2023-05-05 /pmc/articles/PMC10195885/ /pubmed/37213489 http://dx.doi.org/10.3389/ti.2023.11147 Text en Copyright © 2023 Bunthof, Saboerali, Wetering, Nurmohamed, Bemelman, Zuilen, Brand, Baas and Hilbrands. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Health Archive
Bunthof, Kim
Saboerali, Khalid
Wetering, Jacqueline Van De
Nurmohamed, Azam
Bemelman, Frederike
Zuilen, Arjan Van
Brand, Jan Van Den
Baas, Marije
Hilbrands, Luuk
Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title_full Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title_fullStr Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title_full_unstemmed Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title_short Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
title_sort can we predict graft intolerance syndrome after kidney transplant failure? external validation of a previously developed model
topic Health Archive
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195885/
https://www.ncbi.nlm.nih.gov/pubmed/37213489
http://dx.doi.org/10.3389/ti.2023.11147
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