Development and validation of a predictive model assessing the risk of sarcopenia in rheumatoid arthritis patients

作者：Yuan Qu, Lili Zhang, Yuan Liu, Yang Fu, Mengjie Wang, Chuanguo Liu, Xinyu Wang, Yakun Wan, Bing Xu, Qian Zhang, Yancun Li, Ping Jiang · 发表于：Frontiers in Immunology · 年份：2024 · DOI：10.3389/fimmu.2024.1437980 · 被引用次数：14 · 研究领域：Nutrition and Health in Aging、Rheumatoid Arthritis Research and Therapies、Body Composition Measurement Techniques

Background: Sarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients. Methods: Patients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA). Results: A total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminate...