Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Nino-Torres L

PROVIDER: S-EPMC10456165 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

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

Nino-Torres Laura L García-Lopez Andrea A Patino-Jaramillo Nasly N Giron-Luque Fernando F Nino-Murcia Alejandro A

PloS one 20230825 8

<h4>Introduction</h4>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.<h4>Methods</h4>A retrospective cohort study of all deceased-donor KT recipients between January 2009 to December 2021. A descriptive analysis o ...[more]

PMID: 37624758

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Project description:Background Immune tolerance and persistent mixed chimerism can be achieved reproducibly after combined organ and hematopoietic cell transplantation in mice conditioned with total lymphoid irradiation plus anti-thymocyte globulin. We studied the safety and reproducibility of this approach in a cohort of kidney transplant patients, and tried to identify immune monitoring procedures that can predict tolerance and guide complete immunosuppressive drug withdrawal. Methods Ten patients conditioned with 10 doses of total lymphoid irradiation and 5 doses of anti-thymocyte globulin were given kidney transplants and an injection of CD34+ hematopoietic progenitor cells and T cells from HLA matched donors. Blood cell monitoring included changes in chimerism, balance of T cell subsets, gene expression, and responses to donor alloantigens. Results Nine of 10 patients developed multi-lineage chimerism without graft versus host disease (GVHD), and all had excellent graft function at the last observation point. Five of these with chimerism persisting for at least 12 months were completely withdrawn from immunosuppressive drugs for 6 to 35 months. Blood cells from patients off drugs showed development of specific unresponsiveness to donor alloantigens, M-bM-^@M-^\toleranceM-bM-^@M-^] profiles on gene microarrays, early high ratios of regulatory versus conventional naM-CM-/ve T cells, and early high levels of chimerism among NK cells. Conclusions Total lymphoid irradiation, and anti-thymocyte globulin promoted the development of persistent chimerism and tolerance in a cohort of patients given kidney transplants and donor cell injections. Assays were identified that can assist in the safe withdrawal of immunosuppressive drugs. 45 Agilent Microarray samples were conducted, including 16 tolerance patients, 10 chronic rejection patients, 5 healthy normal controls, and 7 paired pre and post-transplant induced tolerance patients. The aim is to see whether induced tolerance patients show operational tolerance gene expression signature and can withdraw or minimize the immunosuppression regimens.

Project description:BackgroundThe variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival.MethodsThis review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot.ResultsA total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses.ConclusionPrediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.

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Dataset Information

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

Introduction

Methods

Results

Conclusion

Publications

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

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