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Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool

INTRODUCTION: A complex relationship between donor and recipient characteristics influences kidney transplant (KT) success. A tool developed by Bae S. et al. (Survival Benefit Estimator, SBE) helps estimate post-KT survival. We aim to evaluate the predictive performance of the SBE tool in terms of 5...

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Autores principales: Nino-Torres, Laura, García-Lopez, Andrea, Patino-Jaramillo, Nasly, Giron-Luque, Fernando, Nino-Murcia, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456165/
https://www.ncbi.nlm.nih.gov/pubmed/37624758
http://dx.doi.org/10.1371/journal.pone.0290162
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author Nino-Torres, Laura
García-Lopez, Andrea
Patino-Jaramillo, Nasly
Giron-Luque, Fernando
Nino-Murcia, Alejandro
author_facet Nino-Torres, Laura
García-Lopez, Andrea
Patino-Jaramillo, Nasly
Giron-Luque, Fernando
Nino-Murcia, Alejandro
author_sort Nino-Torres, Laura
collection PubMed
description INTRODUCTION: A complex relationship between donor and recipient characteristics influences kidney transplant (KT) success. A tool developed by Bae S. et al. (Survival Benefit Estimator, SBE) helps estimate post-KT survival. We aim to evaluate the predictive performance of the SBE tool in terms of 5-year patient survival after a kidney transplant. METHODS: A retrospective cohort study of all deceased-donor KT recipients between January 2009 to December 2021. A descriptive analysis of clinical and sociodemographic characteristics was performed. The SBE online tool was used to calculate the predicted patient survival (PPS) and the survival benefit at five years post-KT. Comparisons between predictive vs. actual patient survival were made using quintile subgroups. Three Cox regression models were built using PPS, EPTS, and KDPI. RESULTS: A total of 1145 recipients were evaluated. Mortality occurred in 157 patients. Patient survival was 86.2%. Predictive survival for patients if they remained on the waiting list was 70.6%. The PPS was 89.3%, which results in a survival benefit (SB) of 18.7% for our population. Actual survival rates were lower than the predicted ones across all the quintiles. In unadjusted analysis, PPS was a significant protective factor for mortality (HR 0.66), whereas EPTS (HR 8.9) and KDPI (HR 3.25) scores were significant risk factors. The discrimination of KDPI, PPS, and EPTS scores models were 0.59, 0.65, and 0.66, respectively. CONCLUSION: SBE score overestimated actual survival rates in our sample. The discrimination power of the score was moderate, although the utility of this tool may be limited in this specific population.
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spelling pubmed-104561652023-08-26 Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool Nino-Torres, Laura García-Lopez, Andrea Patino-Jaramillo, Nasly Giron-Luque, Fernando Nino-Murcia, Alejandro PLoS One Research Article INTRODUCTION: A complex relationship between donor and recipient characteristics influences kidney transplant (KT) success. A tool developed by Bae S. et al. (Survival Benefit Estimator, SBE) helps estimate post-KT survival. We aim to evaluate the predictive performance of the SBE tool in terms of 5-year patient survival after a kidney transplant. METHODS: A retrospective cohort study of all deceased-donor KT recipients between January 2009 to December 2021. A descriptive analysis of clinical and sociodemographic characteristics was performed. The SBE online tool was used to calculate the predicted patient survival (PPS) and the survival benefit at five years post-KT. Comparisons between predictive vs. actual patient survival were made using quintile subgroups. Three Cox regression models were built using PPS, EPTS, and KDPI. RESULTS: A total of 1145 recipients were evaluated. Mortality occurred in 157 patients. Patient survival was 86.2%. Predictive survival for patients if they remained on the waiting list was 70.6%. The PPS was 89.3%, which results in a survival benefit (SB) of 18.7% for our population. Actual survival rates were lower than the predicted ones across all the quintiles. In unadjusted analysis, PPS was a significant protective factor for mortality (HR 0.66), whereas EPTS (HR 8.9) and KDPI (HR 3.25) scores were significant risk factors. The discrimination of KDPI, PPS, and EPTS scores models were 0.59, 0.65, and 0.66, respectively. CONCLUSION: SBE score overestimated actual survival rates in our sample. The discrimination power of the score was moderate, although the utility of this tool may be limited in this specific population. Public Library of Science 2023-08-25 /pmc/articles/PMC10456165/ /pubmed/37624758 http://dx.doi.org/10.1371/journal.pone.0290162 Text en © 2023 Nino-Torres et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Nino-Torres, Laura
García-Lopez, Andrea
Patino-Jaramillo, Nasly
Giron-Luque, Fernando
Nino-Murcia, Alejandro
Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title_full Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title_fullStr Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title_full_unstemmed Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title_short Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool
title_sort predicting 5-year survival after kidney transplantation in colombia using the survival benefit estimator tool
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456165/
https://www.ncbi.nlm.nih.gov/pubmed/37624758
http://dx.doi.org/10.1371/journal.pone.0290162
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