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A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes
Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463255/ https://www.ncbi.nlm.nih.gov/pubmed/32873885 http://dx.doi.org/10.1038/s41598-020-71456-2 |
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author | Feng, Lu-Huai Su, Tingting Bu, Kun-Peng Ren, Shuang Yang, Zhenhua Deng, Cheng-En Li, Bi-Xun Wei, Wei-Yuan |
author_facet | Feng, Lu-Huai Su, Tingting Bu, Kun-Peng Ren, Shuang Yang, Zhenhua Deng, Cheng-En Li, Bi-Xun Wei, Wei-Yuan |
author_sort | Feng, Lu-Huai |
collection | PubMed |
description | Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer–Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670–0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes. |
format | Online Article Text |
id | pubmed-7463255 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74632552020-09-03 A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes Feng, Lu-Huai Su, Tingting Bu, Kun-Peng Ren, Shuang Yang, Zhenhua Deng, Cheng-En Li, Bi-Xun Wei, Wei-Yuan Sci Rep Article Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer–Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670–0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes. Nature Publishing Group UK 2020-09-01 /pmc/articles/PMC7463255/ /pubmed/32873885 http://dx.doi.org/10.1038/s41598-020-71456-2 Text en © The Author(s) 2020 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Feng, Lu-Huai Su, Tingting Bu, Kun-Peng Ren, Shuang Yang, Zhenhua Deng, Cheng-En Li, Bi-Xun Wei, Wei-Yuan A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title | A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title_full | A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title_fullStr | A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title_full_unstemmed | A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title_short | A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
title_sort | clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463255/ https://www.ncbi.nlm.nih.gov/pubmed/32873885 http://dx.doi.org/10.1038/s41598-020-71456-2 |
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