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An updated clinical prediction model of protein-energy wasting for hemodialysis patients
BACKGROUND AND AIM: Protein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectiv...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764006/ https://www.ncbi.nlm.nih.gov/pubmed/36562038 http://dx.doi.org/10.3389/fnut.2022.933745 |
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author | Chen, Si Ma, Xiaoyan Zhou, Xun Wang, Yi Liang, WeiWei Zheng, Liang Zang, Xiujuan Mei, Xiaobin Qi, Yinghui Jiang, Yan Zhang, Shanbao Li, Jinqing Chen, Hui Shi, Yingfeng Hu, Yan Tao, Min Zhuang, Shougang Liu, Na |
author_facet | Chen, Si Ma, Xiaoyan Zhou, Xun Wang, Yi Liang, WeiWei Zheng, Liang Zang, Xiujuan Mei, Xiaobin Qi, Yinghui Jiang, Yan Zhang, Shanbao Li, Jinqing Chen, Hui Shi, Yingfeng Hu, Yan Tao, Min Zhuang, Shougang Liu, Na |
author_sort | Chen, Si |
collection | PubMed |
description | BACKGROUND AND AIM: Protein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model. MATERIALS AND METHODS: A total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model. RESULTS: Binary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799–0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models. CONCLUSION: In this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria. |
format | Online Article Text |
id | pubmed-9764006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97640062022-12-21 An updated clinical prediction model of protein-energy wasting for hemodialysis patients Chen, Si Ma, Xiaoyan Zhou, Xun Wang, Yi Liang, WeiWei Zheng, Liang Zang, Xiujuan Mei, Xiaobin Qi, Yinghui Jiang, Yan Zhang, Shanbao Li, Jinqing Chen, Hui Shi, Yingfeng Hu, Yan Tao, Min Zhuang, Shougang Liu, Na Front Nutr Nutrition BACKGROUND AND AIM: Protein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model. MATERIALS AND METHODS: A total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model. RESULTS: Binary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799–0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models. CONCLUSION: In this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria. Frontiers Media S.A. 2022-12-06 /pmc/articles/PMC9764006/ /pubmed/36562038 http://dx.doi.org/10.3389/fnut.2022.933745 Text en Copyright © 2022 Chen, Ma, Zhou, Wang, Liang, Zheng, Zang, Mei, Qi, Jiang, Zhang, Li, Chen, Shi, Hu, Tao, Zhuang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Nutrition Chen, Si Ma, Xiaoyan Zhou, Xun Wang, Yi Liang, WeiWei Zheng, Liang Zang, Xiujuan Mei, Xiaobin Qi, Yinghui Jiang, Yan Zhang, Shanbao Li, Jinqing Chen, Hui Shi, Yingfeng Hu, Yan Tao, Min Zhuang, Shougang Liu, Na An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title | An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title_full | An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title_fullStr | An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title_full_unstemmed | An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title_short | An updated clinical prediction model of protein-energy wasting for hemodialysis patients |
title_sort | updated clinical prediction model of protein-energy wasting for hemodialysis patients |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764006/ https://www.ncbi.nlm.nih.gov/pubmed/36562038 http://dx.doi.org/10.3389/fnut.2022.933745 |
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