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Predicting Mortality in Patients with Diabetes Starting Dialysis

BACKGROUND: While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these re...

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Autores principales: van Diepen, Merel, Schroijen, Marielle A., Dekkers, Olaf M., Rotmans, Joris I., Krediet, Raymond T., Boeschoten, Elisabeth W., Dekker, Friedo W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3942369/
https://www.ncbi.nlm.nih.gov/pubmed/24594735
http://dx.doi.org/10.1371/journal.pone.0089744
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author van Diepen, Merel
Schroijen, Marielle A.
Dekkers, Olaf M.
Rotmans, Joris I.
Krediet, Raymond T.
Boeschoten, Elisabeth W.
Dekker, Friedo W.
author_facet van Diepen, Merel
Schroijen, Marielle A.
Dekkers, Olaf M.
Rotmans, Joris I.
Krediet, Raymond T.
Boeschoten, Elisabeth W.
Dekker, Friedo W.
author_sort van Diepen, Merel
collection PubMed
description BACKGROUND: While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model. METHODS: Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping. RESULTS: A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results. CONCLUSIONS: A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary.
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spelling pubmed-39423692014-03-06 Predicting Mortality in Patients with Diabetes Starting Dialysis van Diepen, Merel Schroijen, Marielle A. Dekkers, Olaf M. Rotmans, Joris I. Krediet, Raymond T. Boeschoten, Elisabeth W. Dekker, Friedo W. PLoS One Research Article BACKGROUND: While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model. METHODS: Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping. RESULTS: A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results. CONCLUSIONS: A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary. Public Library of Science 2014-03-04 /pmc/articles/PMC3942369/ /pubmed/24594735 http://dx.doi.org/10.1371/journal.pone.0089744 Text en © 2014 van Diepen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
van Diepen, Merel
Schroijen, Marielle A.
Dekkers, Olaf M.
Rotmans, Joris I.
Krediet, Raymond T.
Boeschoten, Elisabeth W.
Dekker, Friedo W.
Predicting Mortality in Patients with Diabetes Starting Dialysis
title Predicting Mortality in Patients with Diabetes Starting Dialysis
title_full Predicting Mortality in Patients with Diabetes Starting Dialysis
title_fullStr Predicting Mortality in Patients with Diabetes Starting Dialysis
title_full_unstemmed Predicting Mortality in Patients with Diabetes Starting Dialysis
title_short Predicting Mortality in Patients with Diabetes Starting Dialysis
title_sort predicting mortality in patients with diabetes starting dialysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3942369/
https://www.ncbi.nlm.nih.gov/pubmed/24594735
http://dx.doi.org/10.1371/journal.pone.0089744
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