Yang Yujie, Liao Hualong, Chen Yang, Qiu Ying, Yan Fei, Fu Ping, Yue Jirong, Chen Yu, Yuan Huaihong
Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
West China School of Nursing, Sichuan University, Chengdu, 610041, China.
BMC Nephrol. 2025 May 19;26(1):247. doi: 10.1186/s12882-025-04176-2.
Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, affecting lower limb muscle strength and increasing the risk of falls and mortality. This study aims to develop an auxiliary screening model for sarcopenia in MHD patients based on machine learning methods, utilizing lower limb muscle strength indicators, while paying attention to the gender difference and exploring its value in sarcopenia screening.
This cross-sectional study collected data from MHD patients at a hemodialysis center in China. Sarcopenia was assessed using the 2019 Asian Working Group for Sarcopenia update. A self-developed lower limb muscle strength testing device was used. Other physiological indicators, including basic information and lab findings, were collected. Participants were grouped into sarcopenia and control groups, with gender-specific binary classification models developed. Stratified shuffling and synthetic minority oversampling techniques were used to build screening classifiers.
Data from 164 MHD patients were ultimately collected, including 83 males (41 with possible sarcopenia or sarcopenia) and 81 females (53 with possible sarcopenia or sarcopenia). Gender-specific binary classification models were developed using lower limb muscle strength indicators, with the male model having an AUC of 79% and the female model an AUC of 80%, respectively. Combining lower limb muscle strength with other physiological indicators improved the female model's screening capability, achieving an AUC of 90%.
This study demonstrates that the auxiliary screening model for sarcopenia, developed using machine learning methods, highlights the significant value of lower limb muscle strength indicators in identifying sarcopenia in MHD patients. The gender-specific screening models show good discriminatory ability across different genders, providing effective tools for the early screening and management of sarcopenia in MHD patients.
Chinese Clinical Trial Registry (ChiCTR2100051111), registered on 2021-09-13.
维持性血液透析（MHD）患者常患肌肉减少症，影响下肢肌肉力量，增加跌倒和死亡风险。本研究旨在基于机器学习方法，利用下肢肌肉力量指标，开发一种针对MHD患者肌肉减少症的辅助筛查模型，同时关注性别差异并探索其在肌肉减少症筛查中的价值。
本横断面研究收集了中国一家血液透析中心MHD患者的数据。采用2019年亚洲肌肉减少症工作组更新标准评估肌肉减少症。使用自行研发的下肢肌肉力量测试装置。收集包括基本信息和实验室检查结果在内的其他生理指标。将参与者分为肌肉减少症组和对照组，建立性别特异性二元分类模型。采用分层洗牌和合成少数过采样技术构建筛查分类器。
最终收集了164例MHD患者的数据，其中男性83例（41例可能患有或确诊为肌肉减少症），女性81例（53例可能患有或确诊为肌肉减少症）。利用下肢肌肉力量指标建立了性别特异性二元分类模型，男性模型的曲线下面积（AUC）为79%，女性模型的AUC为80%。将下肢肌肉力量与其他生理指标相结合提高了女性模型的筛查能力，AUC达到90%。
本研究表明，利用机器学习方法开发的肌肉减少症辅助筛查模型突出了下肢肌肉力量指标在识别MHD患者肌肉减少症中的重要价值。性别特异性筛查模型在不同性别中均显示出良好的鉴别能力，为MHD患者肌肉减少症的早期筛查和管理提供了有效工具。
中国临床试验注册中心（ChiCTR2100051111），于2021年9月13日注册。
