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; et al. (Frontiers in Nutrition, 2022)
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 in order to propose a novel PEW prediction model. 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. 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: 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 ISRNM diagnostic criteria including albumin, total cholesterol and BMI, while 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. Conclusions: In this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objectively than traditional diagnostic criteria.