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Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models

OBJECTIVE: Our aim was to analyze the performance of two scores developed for predicting diabetes in nontransplant populations for identifying kidney transplant recipients with a higher new-onset diabetes mellitus after transplantation (NODAT) risk beyond the first year after transplantation. RESEAR...

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Autores principales: Rodrigo, Emilio, Santos, Lidia, Piñera, Celestino, Ruiz San Millán, Juan Carlos, Quintela, Maria Estrella, Toyos, Carmen, Allende, Natalia, Gómez-Alamillo, Carlos, Arias, Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Diabetes Association 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3322708/
https://www.ncbi.nlm.nih.gov/pubmed/22279030
http://dx.doi.org/10.2337/dc11-2071
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author Rodrigo, Emilio
Santos, Lidia
Piñera, Celestino
Ruiz San Millán, Juan Carlos
Quintela, Maria Estrella
Toyos, Carmen
Allende, Natalia
Gómez-Alamillo, Carlos
Arias, Manuel
author_facet Rodrigo, Emilio
Santos, Lidia
Piñera, Celestino
Ruiz San Millán, Juan Carlos
Quintela, Maria Estrella
Toyos, Carmen
Allende, Natalia
Gómez-Alamillo, Carlos
Arias, Manuel
author_sort Rodrigo, Emilio
collection PubMed
description OBJECTIVE: Our aim was to analyze the performance of two scores developed for predicting diabetes in nontransplant populations for identifying kidney transplant recipients with a higher new-onset diabetes mellitus after transplantation (NODAT) risk beyond the first year after transplantation. RESEARCH DESIGN AND METHODS: We analyzed 191 kidney transplants, which had at least 1-year follow-up posttransplant. First-year posttransplant variables were collected to estimate the San Antonio Diabetes Prediction Model (SADPM) and Framingham Offspring Study–Diabetes Mellitus (FOS-DM) algorithm. RESULTS: Areas under the receiver operating characteristic curve of FOS-DM and SADPM scores to predict NODAT were 0.756 and 0.807 (P < 0.001), respectively. FOS-DM and SADPM scores over 75 percentile (hazard ratio 5.074 and 8.179, respectively, P < 0.001) were associated with NODAT. CONCLUSIONS: Both scores can be used to identify kidney recipients at higher risk for NODAT beyond the first year. SADPM score detects some 25% of kidney transplant patients with an eightfold risk for NODAT.
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spelling pubmed-33227082013-03-01 Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models Rodrigo, Emilio Santos, Lidia Piñera, Celestino Ruiz San Millán, Juan Carlos Quintela, Maria Estrella Toyos, Carmen Allende, Natalia Gómez-Alamillo, Carlos Arias, Manuel Diabetes Care Original Research OBJECTIVE: Our aim was to analyze the performance of two scores developed for predicting diabetes in nontransplant populations for identifying kidney transplant recipients with a higher new-onset diabetes mellitus after transplantation (NODAT) risk beyond the first year after transplantation. RESEARCH DESIGN AND METHODS: We analyzed 191 kidney transplants, which had at least 1-year follow-up posttransplant. First-year posttransplant variables were collected to estimate the San Antonio Diabetes Prediction Model (SADPM) and Framingham Offspring Study–Diabetes Mellitus (FOS-DM) algorithm. RESULTS: Areas under the receiver operating characteristic curve of FOS-DM and SADPM scores to predict NODAT were 0.756 and 0.807 (P < 0.001), respectively. FOS-DM and SADPM scores over 75 percentile (hazard ratio 5.074 and 8.179, respectively, P < 0.001) were associated with NODAT. CONCLUSIONS: Both scores can be used to identify kidney recipients at higher risk for NODAT beyond the first year. SADPM score detects some 25% of kidney transplant patients with an eightfold risk for NODAT. American Diabetes Association 2012-03 2012-02-10 /pmc/articles/PMC3322708/ /pubmed/22279030 http://dx.doi.org/10.2337/dc11-2071 Text en © 2012 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
spellingShingle Original Research
Rodrigo, Emilio
Santos, Lidia
Piñera, Celestino
Ruiz San Millán, Juan Carlos
Quintela, Maria Estrella
Toyos, Carmen
Allende, Natalia
Gómez-Alamillo, Carlos
Arias, Manuel
Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title_full Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title_fullStr Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title_full_unstemmed Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title_short Prediction at First Year of Incident New-Onset Diabetes After Kidney Transplantation by Risk Prediction Models
title_sort prediction at first year of incident new-onset diabetes after kidney transplantation by risk prediction models
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3322708/
https://www.ncbi.nlm.nih.gov/pubmed/22279030
http://dx.doi.org/10.2337/dc11-2071
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