中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (10): 823-827.doi: 10.3969/j.issn.1671-4091.2025.10.007

• 临床研究 • 上一篇    下一篇

维持性血液透析患者社会衰弱现状及影响因素可解释性分析

李 瑛    谌晓安    梁炜炜   杨 斌    潘 伟    吴海波   

  1. 416000 吉首，1吉首大学体育科学学院
    200123 上海，2上海市东方医院血液净化室
    256600 滨州，3滨州医学院附属医院血液透析室
  • 收稿日期:2025-02-24 修回日期:2025-04-25 出版日期:2025-10-12 发布日期:2025-10-12
  • 通讯作者: 吴海波 E-mail:1499617456@qq.com
  • 基金资助:
    国家社会科学基金项目(24XTY003)

A study on social frailty status and interpretable analysis of influencing factors in maintenance hemodialysis patients

LI Ying, CHEN Xiao-an, LIANG Wei-wei, YANG Bin, PAN Wei, WU Hai-bo   

  1. College of Sports Science, Jishou University, Jishou 416000, China;  2Blood Purification Room, Shanghai East Hospital, Shanghai 200123, China; 3Blood Purification Room, Affiliated Hospital of Binzhou Medical College, Binzhou 256600, China
  • Received:2025-02-24 Revised:2025-04-25 Online:2025-10-12 Published:2025-10-12
  • Contact: 200123 上海，2上海市东方医院血液净化室 E-mail:1499617456@qq.com

摘要： 目的  探讨维持性血液透析(maintenance hemodialysis,MHD)患者社会衰弱的发生率及影响因素，为预防MHD患者社会衰弱的发生提供理论依据。 方法  2023年11月—2024年4月，选取滨州医学院附属医院及上海市东方医院血液净化室MHD患者为研究对象，基于单因素分析筛选变量，利用极端梯度提升库构建梯度提升(eXtreme gradient boosting,XG Boost)树模型，以预测社会衰弱程度。 结果  共纳入354例患者，社会衰弱发生率为26.27%(93/354)。基于最优特征子集，构建优化的XGBoost分类模型。沙普利可加性特征解释(Shapley additive explanations,SHAP)重要性排序(平均绝对SHAP值)依次为：体育锻炼(0.447)、参与社会活动情况(0.358)、睡眠情况(0.328)等。 结论  本研究调查的MHD患者社会衰弱发生率较高，医护人员可根据MHD患者社会衰弱发生的因素及早识别高危人群并实施有效的干预措施。

关键词: 维持性血液透析, 社会衰弱, 影响因素

Abstract: Objective  To investigate the prevalence and determinants of social frailty among maintenance hemodialysis (MHD) patients, and to establish a theoretical framework for its prevention. Methods  From November 2023 to April 2024, MHD patients from the hemodialysis centers of Binzhou Medical University Hospital and Shanghai East Hospital were selected as study subjects. Variables were screened based on univariate analysis, and an optimized eXtreme Gradient Boosting (XGBoost) tree model was constructed using the XGBoost library to predict the degree of social frailty.  Results  A total of 354 patients were included, with a social frailty prevalence of 26.27% (93/354). Based on the optimal feature subset, an optimized XGBoost classification model was constructed. The Shapley Additive exPlanations (SHAP) importance ranking (mean absolute SHAP values) was as follows: physical exercise（0.447）, social participation(0.358), sleep conditions (0.328), among others. Conclusion  The prevalence of social frailty among MHD patients in this study was relatively high. Clinical healthcare providers can early identify high-risk groups and implement effective interventions based on the factors influencing social frailty in MHD patients.

Key words: Maintenance hemodialysis, Social frailty, Influencing factor

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