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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Diabetes Association
2012
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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. |
format | Online Article Text |
id | pubmed-3322708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | American Diabetes Association |
record_format | MEDLINE/PubMed |
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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