Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis
Background To explore the symptom clusters of patients undergoing maintenance hemodialysis and construct a symptom network to identify the core symptoms and core symptom clusters, to provide reference for precise symptom management.Methods Conveniently selected 354 patients with maintenance hemodial...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2449203 |
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| author | Yaxin Chang Ke Wang Mengjia Liu Zhifang Zhang Huiwen Ma Xinping Gao Zhaoxia Yang |
| author_facet | Yaxin Chang Ke Wang Mengjia Liu Zhifang Zhang Huiwen Ma Xinping Gao Zhaoxia Yang |
| author_sort | Yaxin Chang |
| collection | DOAJ |
| description | Background To explore the symptom clusters of patients undergoing maintenance hemodialysis and construct a symptom network to identify the core symptoms and core symptom clusters, to provide reference for precise symptom management.Methods Conveniently selected 354 patients with maintenance hemodialysis were surveyed cross-sectionally using the general information questionnaire, the Dialysis Symptom Index and the Kidney Disease Questionnaire. Symptom clusters were extracted using exploratory factor analysis, and core symptom clusters were identified using hierarchical regression and network analysis.Results The most common and severe symptoms were fatigue, dry skin and itching, and the most distressing symptoms were fatigue, itching and trouble falling asleep. Within the symptom network, worry (rs = 1.0) had the highest strength, trouble staying asleep(rc = 0.01) had the highest closeness, and fatigue had the highest betweenness (rb = 30) and bridge strength (rbs = 0.53). A total of four symptom clusters were extracted, namely psychological symptom cluster, sleep disorder symptom cluster, uremia-related symptom cluster, and neurological symptom cluster. Hierarchical regression results showed that the psychological symptom cluster had the greatest impact on patients’ quality of life.Conclusions Fatigue was the most severe symptom and the bridge symptom, the uremia-related symptom cluster caused the greatest distress for patients, worry was the core symptom, and the psychological symptom cluster was identified as the core cluster. Clinical staff can provide effective symptom management and improve patient symptom burden by establishing intervention strategies centered on these results. |
| format | Article |
| id | doaj-art-840f0b9a854745babb7990cd14fe5ac4 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-840f0b9a854745babb7990cd14fe5ac42025-01-14T06:04:56ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2024.2449203Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysisYaxin Chang0Ke Wang1Mengjia Liu2Zhifang Zhang3Huiwen Ma4Xinping Gao5Zhaoxia Yang6Shandong First Medical University, Jinan, ChinaThe Second Affiliated Hospital of Shandong First Medical University, Tai’an, ChinaShandong First Medical University, Jinan, ChinaThe Second Affiliated Hospital of Shandong First Medical University, Tai’an, ChinaShandong First Medical University, Jinan, ChinaThe Second Affiliated Hospital of Shandong First Medical University, Tai’an, ChinaThe Second Affiliated Hospital of Shandong First Medical University, Tai’an, ChinaBackground To explore the symptom clusters of patients undergoing maintenance hemodialysis and construct a symptom network to identify the core symptoms and core symptom clusters, to provide reference for precise symptom management.Methods Conveniently selected 354 patients with maintenance hemodialysis were surveyed cross-sectionally using the general information questionnaire, the Dialysis Symptom Index and the Kidney Disease Questionnaire. Symptom clusters were extracted using exploratory factor analysis, and core symptom clusters were identified using hierarchical regression and network analysis.Results The most common and severe symptoms were fatigue, dry skin and itching, and the most distressing symptoms were fatigue, itching and trouble falling asleep. Within the symptom network, worry (rs = 1.0) had the highest strength, trouble staying asleep(rc = 0.01) had the highest closeness, and fatigue had the highest betweenness (rb = 30) and bridge strength (rbs = 0.53). A total of four symptom clusters were extracted, namely psychological symptom cluster, sleep disorder symptom cluster, uremia-related symptom cluster, and neurological symptom cluster. Hierarchical regression results showed that the psychological symptom cluster had the greatest impact on patients’ quality of life.Conclusions Fatigue was the most severe symptom and the bridge symptom, the uremia-related symptom cluster caused the greatest distress for patients, worry was the core symptom, and the psychological symptom cluster was identified as the core cluster. Clinical staff can provide effective symptom management and improve patient symptom burden by establishing intervention strategies centered on these results.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2449203Maintenance hemodialysissymptom clustersnetwork analysiscore symptomssymptom managementworry |
| spellingShingle | Yaxin Chang Ke Wang Mengjia Liu Zhifang Zhang Huiwen Ma Xinping Gao Zhaoxia Yang Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis Renal Failure Maintenance hemodialysis symptom clusters network analysis core symptoms symptom management worry |
| title | Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis |
| title_full | Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis |
| title_fullStr | Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis |
| title_full_unstemmed | Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis |
| title_short | Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis |
| title_sort | identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis a cross sectional network analysis |
| topic | Maintenance hemodialysis symptom clusters network analysis core symptoms symptom management worry |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2449203 |
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