中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (08): 700-704.doi: 10.3969/j.issn.1671-4091.2025.08.016

• 护理研究 • 上一篇    

基于潜类别增长模型的维持性血液透析患者透析后疲乏轨迹及影响因素研究

陈 莉   王心语    王晓闪    蔡小霞   

  1. 571199 海口，1海南医科大学国际护理学院
    570311 海口，2海南医科大学第二附属医院肾内科
  • 收稿日期:2025-02-25 修回日期:2025-06-12 出版日期:2025-08-12 发布日期:2025-08-12
  • 通讯作者: 蔡小霞 E-mail:896318445@qq.com
  • 基金资助:
    海南省自然科学基金(820MS047)

Study on the fatigue trajectory and influencing factors of maintenance hemodialysis patients after dialysis based on the latent category growth model

CHEN Li, WANG Xin-yu, WANG Xiao-shan, CAI Xiao-xia   

  1. International School of Nursing, Hainan Medical University, Haikou 571199, China; 2Department of Nephrology, The Second Affiliated Hospital of Hainan Medical University, Haikou 570311, China
  • Received:2025-02-25 Revised:2025-06-12 Online:2025-08-12 Published:2025-08-12
  • Contact: 571199 海口，1海南医科大学国际护理学院 E-mail:896318445@qq.com

摘要： 目的 探讨维持性血液透析(maintenance hemodialysis，MHD)患者透析后疲乏动态变化轨迹及其影响因素。 方法  采用便利抽样法，于2024年1月─6月选取海南医科大学第二附属医院血液净化中心的373例MHD患者为研究对象，使用一般资料调查表、简易疲乏量表、匹兹堡睡眠指数量表于透析后即刻(T0)进行基线调查，并在透析后1 h(T1)、透析后3 h(T2)、透析晚睡前(T3)、次日(T4)使用简易疲乏量表进行重复测量；使用潜类别增长模型(latent class growth modeling，LCGM)识别透析后疲乏的变化轨迹，并通过多分类Logistic回归分析其影响因素。 结果 潜类别增长模型分析结果显示：3类别模型最佳。根据轨迹特点分别命名为：轻度疲乏快速缓解组(C1组，n=125)、中度疲乏中速缓解组(C2组,n=88)和高度疲乏慢速缓解组(C3组,n=160)。Logistic回归分析显示，均以C1组为参照，C2组中睡眠障碍(OR=0.141，95% CI：0.075～0.265，P＜0.001)、透析中低血压(OR=0.472，95% CI：0.246～0.904，P=0.024)、透析龄(OR=1.278，95% CI：1.130～1.446，P＜0.001)、血红蛋白水平(OR=0.939，95% CI：0.977～1.009，P＜0.001)表现出显著影响；C3组中性别(OR=2.246，95% CI：1.070～4.713，P=0.032)、睡眠障碍(OR=0.064，95% CI：0.027～0.148，P＜0.001)、透析中低血压(OR=0.275，95% CI：0.122～0.616，P=0.002)、透析龄(OR=1.289，95% CI：1.099～1.510，P=0.002)、血红蛋白水平(OR=0.939，95% CI：0.918～0.961，P＜0.001)表现出显著影响。 结论 维持性血液透析患者透析后疲乏整体变化轨迹呈下降趋势，但存在群体异质性，提示需基于轨迹特征考虑多因素优化干预措施，实施个体化干预和管理。

关键词: 潜类别增长模型, 维持性血液透析, 透析后疲乏, 纵向轨迹

Abstract: Objective  To explore the dynamic trajectories of post-dialysis fatigue and its influencing factors in maintenance hemodialysis (MHD) patients. Methods Using convenience sampling, 373 patients from a hemodialysis center were enrolled from January to June 2024. General information, the Brief Fatigue Inventory (BFI), and the Pittsburgh Sleep Quality Index (PSQI) were collected immediately after dialysis (T1), with BFI repeated at 1h (T2), 3h (T3), bedtime (T4), and the next day (T5). Latent class growth modeling (LCGM) identified fatigue trajectories, and multinomial logistic regression analyzed influencing factors. Results A 3-class LCGM model best fit the data: mild-rapid recovery (33.5%, C1), moderate-medium recovery (42.9%, C2), and severe-slow recovery (23.6%, C3). Logistic regression (reference: C1) showed C2 was significantly associated with sleep disorders (OR=0.141, P＜0.001), hypotension (OR=0.472, P=0.024), dialysis vintage (OR=1.278, P＜0.001), and hemoglobin (OR=0.939, P＜0.001); C3 was linked to female sex (OR=2.246, P=0.032), sleep disorders (OR=0.064, P＜0.001), hypotension (OR=0.275, P=0.002), dialysis vintage (OR=1.289, P=0.002), and hemoglobin (OR=0.939, P＜0.001). Conclusion Post-dialysis fatigue shows a declining trend but with population heterogeneity, suggesting personalized interventions targeting trajectory-specific factors.

Key words: Latent category growth model, Maintenance hemodialysis, Fatigue after dialysis, Longitudinal trajectory

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