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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Clinical Nutrition
Volume 11 - 2024 | doi: 10.3389/fnut.2024.1351503

External validation of a novel nomogram for diagnosis of Protein Energy Wasting in adult hemodialysis patients

Provisionally accepted
Danying Yan Danying Yan 1Yi Wang Yi Wang 1Jing Hu Jing Hu 2Renhua Lu Renhua Lu 3Chaoyang Ye Chaoyang Ye 4Nanmei Liu Nanmei Liu 5Dongping Chen Dongping Chen 4Weiwei Liang Weiwei Liang 1Liang Zheng Liang Zheng 6Wenrui Liu Wenrui Liu 2Tianying Lan Tianying Lan 4Naiying Lan Naiying Lan 5Qing Shao Qing Shao 5Shougang Zhuang Shougang Zhuang 1,7Xiaoyan Ma Xiaoyan Ma 1*Na Liu Na Liu 8*
  • 1 Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
  • 2 Department of Nephrology, Seventh People's Hospital of Shanghai, Shanghai, Shanghai Municipality, China
  • 3 Department of Nephrology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
  • 4 Department of Nephrology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
  • 5 Naval Medical Center, Shanghai, China
  • 6 Key Laboratory of Arrhythmias of the Ministry of Education of China, Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
  • 7 Warren Alpert Medical School, Brown University, Providence, United States
  • 8 Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

The final, formatted version of the article will be published soon.

    Protein Energy Wasting (PEW) has high incidence in adult hemodialysis patients and refers to a state of decreased protein and energy substance. It has been demonstrated that PEW highly affects the quality of survival and increases the risk of death.Nevertheless, its diagnostic criteria are complex in clinic. To simplify the diagnosis method of PEW in adult hemodialysis patients, we previously established a novel clinical prediction model that was well-validated internally using bootstrapping. In this multicenter cross-sectional study, we aimed to externally validate this nomogram in a new cohort of adult hemodialysis patients. The novel prediction model was built by combining 4 independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and body mass index (BMI). We evaluated the performance of the new model using discrimination (Concordance Index), calibration plots, and Clinical Impact Curve to assess its predictive utility. From September 1 st , 2022 to August 31 st , 2023, 1158 patients were screened in five medical centers in Shanghai. 622 (53.7%) hemodialysis patients were included for analysis. The PEW predictive model was acceptable discrimination with the area under the curve of 0.777 (95%CI 0.741-0.814).Additionally, the model revealed well-fitted calibration curves. The McNemar test showed the novel model had similar diagnostic efficacy with the gold standard diagnostic method (P > 0.05). Taken together, our results from this cross-sectional external validation study further demonstrate that the novel model is a valid tool to identify PEW in adult hemodialysis patients effectively.

    Keywords: Protein energy wasting, diagnosis, Prediction model, External validation, hemodialysis

    Received: 06 Dec 2023; Accepted: 31 Jul 2024.

    Copyright: © 2024 Yan, Wang, Hu, Lu, Ye, Liu, Chen, Liang, Zheng, Liu, Lan, Lan, Shao, Zhuang, Ma and Liu. 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) or licensor 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.

    * Correspondence:
    Xiaoyan Ma, Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
    Na Liu, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

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