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Recolector de Ciencia Abierta, RECOLECTA
Bachelor thesis . 2020
License: CC BY NC ND
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Recolector de Ciencia Abierta, RECOLECTA
Bachelor thesis . 2020
License: CC BY NC ND
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
UCrea
Bachelor thesis . 2020
License: CC BY NC ND
Data sources: UCrea
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¿Se puede predecir la supervivencia del injerto renal antes de su realización?

Can we predict the kidney graft survival before its performance?
Authors: Villar Gutiérrez, Raquel;

¿Se puede predecir la supervivencia del injerto renal antes de su realización?

Abstract

RESUMEN : El trasplante renal es tratamiento de elección en los pacientes que padecen insuficiencia renal crónica, por lo que es fundamental conocer los factores predictores de muerte y de fracaso del injerto, así como su aplicación en la práctica clínica para que el injerto dure el mayor tiempo posible y con la mejor calidad de vida. Diversos factores pretrasplante y post-trasplante pueden influir en una peor supervivencia del injerto renal. Se han desarrollado múltiples modelos que combinan estos factores para predecir el riesgo de pérdida de los injertos renales. Entre estos modelos, Molnar et al desarrollaron uno en población norteamericana utilizando variables pretrasplante. El objetivo de nuestro estudio fue validar dicho modelo en una población española. Realizamos un estudio retrospectivo de todos los trasplantes renales realizados en el Hospital Universitario Marqués de Valdecilla entre 2000 y 2015. En nuestra población se demostró que el modelo predictivo de Molnar se asociaba con un mayor riesgo de pérdida del injerto a uno, tres y cinco años, pero no estaba bien calibrado ni presentaba una capacidad discrimintaviva significativa como para considerarlo útil en nuestra población.

ABSTRACT : Kidney transplantation is the treatment of choice in patients with chronic kidney failure, so it is essential to study the factors predictive of death and failure of the grafts, as well as its application in clinical practice, so that the graft lasts the longest possible time and with the best quality of life. Both pretransplant and posttransplant risk factors influence the outcome of the kidney graft. Several risk prediction models have been developed to estimate the risk of graft loss. Among them, Molnar et al developed a model to predict graft survival in a US population taking into account only pretransplant variables. Our objective was to validate this model in a Spanish kidney transplant population. We carried out a retrospective study including all the kidney transplants performed in University Hospital Marqués de Valdecilla between 2000 and 2015. In our population of kidney transplant recipients, Molnar`s model related to a higher risk of graft failure at one, three and five years after transplantation, but this model did not allow to discriminate those patients who were going to lose the graft and it was not well calibrated to predict graft failure.

Grado en Medicina

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Spain
Related Organizations
Keywords

Kidney transplantation, Supervivencia del injerto, Trasplante renal, Risk factors, Mortalidad, Graft survival, Mortality, Factores de riesgo

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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