Risk Prediction Models for Sarcopenia in Dialysis Patients: A Systematic Review.

Autor:	Leng YJ; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Wang GR; West China School of Public Health and West China Fourth Hospital, West China Nursing School, Sichuan University, Chengdu, China. Electronic address: guorong_wang@uestc.edu.cn., Xie RN; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Jiang X; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Li CX; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Nie ZM; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Li T; Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jazyk:	angličtina
Zdroj:	Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation [J Ren Nutr] 2024 Jun 05. Date of Electronic Publication: 2024 Jun 05.
DOI:	10.1053/j.jrn.2024.05.009
Abstrakt:	Nowadays, numerous studies have developed risk prediction models for sarcopenia in dialysis patients. However, the quality and performance of these models have not been integrated. The purpose of our study is to provide a comprehensive overview of the current risk prediction models for sarcopenia in dialysis patients and to offer a reference for the development of high-quality prediction models. Ten electronic databases were searched from inception to March 8, 2024. Two researchers independently assessed the risk of bias and applicability of the studies, and used Revman, 5.4, software to conduct a meta-analysis of common predictors in the models. A total of 12 studies described 13 risk prediction models for dialysis patients with sarcopenia. In dialysis patients, the prevalence of sarcopenia ranged from 6.60% to 63.73%. The area under curve (AUC) of the 13 models ranged from 0.776 to 0.945. Only six models (AUC ranging from 0.73 to 0.832) were internally validated, while two were externally evaluated (AUC ranging from 0.913 to 0.955). Most studies had a high risk of bias. The most common effective predictors in the models were age, body mass index, muscle circumference, and C-reactive protein. Our study suggests that developing a prediction model for the onset of sarcopenia in dialysis patients requires a rigorous design scheme, and future verification methods will necessitate multicenter external validation. (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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