Development and validation of an interpretable machine learning model for predicting low muscle mass in patients with rheumatoid arthritis: a multicenter study

作者：Fei Zhou, Bin Zhou, Yuan Qu, Shuai Zhong, Ting Liu, Yuan Liu, Xiaohu Zhao, Xuanhe Tian, Xiaojing Hao, Ping Jiang · 发表于：Frontiers in Medicine · 年份：2025 · DOI：10.3389/fmed.2025.1694320 · 被引用次数：2 · 研究领域：Nutrition and Health in Aging、Rheumatoid Arthritis Research and Therapies、Inflammatory Myopathies and Dermatomyositis

Background: This study aims to develop a predictive model for identifying rheumatoid arthritis (RA) patients at risk of low muscle mass using easily obtainable clinical indicators. The goal is to facilitate targeted screening for individuals at high risk of sarcopenia, optimize diagnostic strategies, reduce the burden of additional testing, and improve the efficiency of early identification and intervention. Methods: This study analyzed data from 1,260 RA patients obtained from the National Health and Nutrition Examination Survey (NHANES) database and the Affiliated Hospital of Shandong University of Traditional Chinese Medicine (SHUTCM). Eight machine learning models were developed, including Random Forest, LightGBM, XGBoost, CatBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, and a weighted ensemble model. Model performance was evaluated using metrics such as accuracy, area under the receiver operating characteristic curve (AUC), F1 score, Precision, Recall, and Brier score loss. The SHapley Additive exPlanation (SHAP) method was used to rank feature importance and interpret the final model. Results: Among all machine learning models, the tree-based weighted ensemble model demonstrated the best performance, achieving an AUC of 0.921, outperforming all individual models. The model exhibited good calibration and higher net clinical benefit in decision curve analysis, especially within the probability threshold range of 0.2 to 0.8, and achie...