中华护理杂志 ›› 2026, Vol. 61 ›› Issue (11): 1512-1519.DOI: 10.3761/j.issn.0254-1769.2026.11.010

• 专科护理实践与研究 • 上一篇    下一篇

多囊卵巢综合征患者症状网络分析及护理启示

刘京辉1(), 刘芳丽2, 李思媛1, 徐文琪1, 张露文2, 田清蜜2, 杜李百合1, 章明阳1()   

  1. 1.中山市人民医院护理部 中山市 528403
    2.河南大学护理与健康学院 开封市 475004
  • 收稿日期:2025-07-03 出版日期:2026-06-10 发布日期:2026-06-10
  • *通讯作者: 章明阳,E-mail:jdzmy2010@163.com
  • 作者简介:刘京辉:女,本科(硕士在读),E-mail:15637394798@163.com
    作者贡献声明

    刘京辉:研究设计、问卷调查/数据收集、数据整理、统计学分析、论文撰写;刘芳丽:研究指导;李思媛、徐文琪:数据收集;张露文、田清蜜、杜李百合:数据整理;章明阳:研究设计、研究指导、论文审阅与修改

Symptom network analysis and nursing implications in patients with polycystic ovary syndrome

LIU Jing-hui1(), LIU Fangli2, LI Siyuan1, XU Wenqi1, ZHANG Luwen2, TIAN Qingmi2, DU Libaihe1, ZHANG Mingyang1()   

  1. 1. Nursing Department,Zhongshan City People’s Hospital,Zhongshan 528403,China
    2. School of Nursing and Health,Henan University,Kaifeng 475004,China
  • Received:2025-07-03 Online:2026-06-10 Published:2026-06-10
  • * Corresponding author: ZHANG Mingyang,E-mail:jdzmy2010@163.com

摘要:

目的 调查多囊卵巢综合征(polycystic ovary syndrome,PCOS)患者的症状发生状况,构建同期症状网络,识别症状群和核心症状,旨在为制订针对性的症状管理策略提供依据。 方法 采用便利抽样法,于2024年11月—2025年5月,选取来自国内不同地区的400例PCOS患者作为调查对象,使用自行编制的一般资料调查表和PCOS患者常见症状问卷进行调查。通过探索性因子分析提取症状群并应用R软件构建网络模型,计算中心性指标,确定核心症状及桥梁症状。 结果 最终纳入378例PCOS患者。共提取高雄激素性皮肤表现症状群、排卵及月经异常症状群、代谢紊乱症状群、情绪障碍伴疲劳症状群、经前期综合征症状群5个症状群,累计方差贡献率为56.1%;月经周期异常(97.9%)是PCOS患者最常见的症状。在症状网络中,感到痛苦(rS=8.19)和感到悲伤(rS=8.16)是最核心的症状;易激惹(rbs=4.78)是桥梁强度最大的症状。 结论 PCOS患者常表现多种症状及症状群。医护人员可基于网络分析识别出的核心症状,制订精准、有效的干预措施,从而改善患者治疗期间的生活质量。

关键词: 多囊卵巢综合征, 核心症状, 症状群, 网络分析, 症状管理, 护理

Abstract:

Objective To investigate the occurrence of symptoms in patients with polycystic ovary syndrome (PCOS),construct a contemporaneous symptom network,and identify symptom clusters and core symptoms,in order to provide a basis for developing targeted symptom management strategies. Methods A convenience sampling method was used to survey 400 patients with PCOS from different regions of China between November 2024 and May 2025. Data were collected using a self-designed general information questionnaire and a PCOS common symptoms questionnaire. Exploratory factor analysis was employed to identify symptom clusters,and a network model was constructed using R software. Centrality indices were calculated to determine core and bridge symptoms. Results A total of 378 subjects were ultimately included in the study. A total of 5 symptom clusters were extracted,including hyperandrogenic skin manifestations,ovulatory and menstrual dysfunction,metabolic disorders,mood disorders with fatigue,and premenstrual syndrome,with a cumulative variance contribution rate of 56.1%. The most prevalent symptom among PCOS patients was irregular menstrual cycles(97.9%). Within the symptom network,“feeling distressed”(rS=8.19) and “feeling sad”(rS=8.16) were identified as the most central symptoms. Irritability(rbs=4.78) exhibited the highest bridge strength. Conclusion PCOS patients often present with a diverse range of symptoms and symptom clusters. Healthcare providers can use the core symptoms identified through network analysis to develop precise and effective interventions,thereby enhancing patients’ quality of life during treatment.

Key words: Polycystic Ovary Syndrome, Core Symptoms, Symptom Clusters, Network Analysis, Symptom Management, Nursing Care