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Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes
The aim of this study is to develop a prediction model for ESRD in patients with type 2 diabetes. A retrospective cohort study was conducted, consisting of 24,104 Chinese patients with type 2 diabetes. We adopted the procedures proposed by the Framingham Heart Study to develop a prediction model for...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579050/ https://www.ncbi.nlm.nih.gov/pubmed/28860599 http://dx.doi.org/10.1038/s41598-017-09243-9 |
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author | Lin, Cheng-Chieh Li, Chia-Ing Liu, Chiu-Shong Lin, Wen-Yuan Lin, Chih-Hsueh Yang, Sing-Yu Li, Tsai-Chung |
author_facet | Lin, Cheng-Chieh Li, Chia-Ing Liu, Chiu-Shong Lin, Wen-Yuan Lin, Chih-Hsueh Yang, Sing-Yu Li, Tsai-Chung |
author_sort | Lin, Cheng-Chieh |
collection | PubMed |
description | The aim of this study is to develop a prediction model for ESRD in patients with type 2 diabetes. A retrospective cohort study was conducted, consisting of 24,104 Chinese patients with type 2 diabetes. We adopted the procedures proposed by the Framingham Heart Study to develop a prediction model for ESRD. Participants were randomly assigned to the derivation and validation sets at a 2:1 ratio. The Cox proportional hazard regression model was used for model development. A total of 813 and 402 subjects (5.06% and 5.00%, respectively) developed ESRD in the derivation and validation sets over a mean follow-up period of 8.3 years. The risk-scoring systems included age, gender, age of diabetes onset, combined statuses of blood pressure and anti-hypertensive medication use, creatinine, variation in HbA1c, variation in systolic blood pressure, diabetes retinopathy, albuminuria, anti-diabetes medications, and combined statuses of hyperlipidemia and anti-hyperlipidemia medication use. The area under curves of 3-year, 5-year, and 8-year ESRD risks were 0.90, 0.86, and 0.81 in the derivation set, respectively. This risk score model can be used as screening for early prevention. The risk prediction for 3-year, 5-year, and 8-year period demonstrated good predictive accuracy and discriminatory ability. |
format | Online Article Text |
id | pubmed-5579050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55790502017-09-06 Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes Lin, Cheng-Chieh Li, Chia-Ing Liu, Chiu-Shong Lin, Wen-Yuan Lin, Chih-Hsueh Yang, Sing-Yu Li, Tsai-Chung Sci Rep Article The aim of this study is to develop a prediction model for ESRD in patients with type 2 diabetes. A retrospective cohort study was conducted, consisting of 24,104 Chinese patients with type 2 diabetes. We adopted the procedures proposed by the Framingham Heart Study to develop a prediction model for ESRD. Participants were randomly assigned to the derivation and validation sets at a 2:1 ratio. The Cox proportional hazard regression model was used for model development. A total of 813 and 402 subjects (5.06% and 5.00%, respectively) developed ESRD in the derivation and validation sets over a mean follow-up period of 8.3 years. The risk-scoring systems included age, gender, age of diabetes onset, combined statuses of blood pressure and anti-hypertensive medication use, creatinine, variation in HbA1c, variation in systolic blood pressure, diabetes retinopathy, albuminuria, anti-diabetes medications, and combined statuses of hyperlipidemia and anti-hyperlipidemia medication use. The area under curves of 3-year, 5-year, and 8-year ESRD risks were 0.90, 0.86, and 0.81 in the derivation set, respectively. This risk score model can be used as screening for early prevention. The risk prediction for 3-year, 5-year, and 8-year period demonstrated good predictive accuracy and discriminatory ability. Nature Publishing Group UK 2017-08-31 /pmc/articles/PMC5579050/ /pubmed/28860599 http://dx.doi.org/10.1038/s41598-017-09243-9 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lin, Cheng-Chieh Li, Chia-Ing Liu, Chiu-Shong Lin, Wen-Yuan Lin, Chih-Hsueh Yang, Sing-Yu Li, Tsai-Chung Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title | Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title_full | Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title_fullStr | Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title_full_unstemmed | Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title_short | Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
title_sort | development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579050/ https://www.ncbi.nlm.nih.gov/pubmed/28860599 http://dx.doi.org/10.1038/s41598-017-09243-9 |
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