Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study
Abstract The symptoms of stroke jeopardize patients’ health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-cent...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84642-3 |
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| author | Siyu Zhou Yuan Zhang Huijuan He Xiangrong Wang Mengying Li Na Zhang Jiali Song |
| author_facet | Siyu Zhou Yuan Zhang Huijuan He Xiangrong Wang Mengying Li Na Zhang Jiali Song |
| author_sort | Siyu Zhou |
| collection | DOAJ |
| description | Abstract The symptoms of stroke jeopardize patients’ health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-centered precision medicine. However, the symptom network paradigm, as a novel research approach, addresses the limitations of traditional symptom management by identifying core symptoms and determining intervention targets, thereby enhancing the efficiency and precision of symptom management. This study. aims to explore the symptom network and core symptoms of acute-phase stroke patients. A convenience sample of 505 stroke patients was selected for this study. Symptoms were assessed by the Stroke Symptom Experience Scale.Exploratory factor analysis was utilized to extract symptom clusters, and network analysis was conducted to construct the symptom network and characterize its nodes. In this study, four symptom clusters were extracted through exploratory factor analysis. Based on the results of node predictability(re) and node centrality such as strength centrality (rs), it was found that the symptoms of “No interest in surroundings” (rs = 1.299, re = 1.081), “Be disappointed about future” (rs = 0.922, re = 0.901), and “Unable to maintain body balance” (rs = 0.747, re = 0.744) had the highest centrality and predictability values, indicating their core positions within the symptom network. No interest in surroundings, Be disappointed about future, and Unable to maintain body balance are core symptoms in the symptom network. In the future, intervention methods for core symptoms can be constructed and validated for their intervention effects to further demonstrate the benefits of core symptoms. |
| format | Article |
| id | doaj-art-f0e5bae77a7d43fb9d0bb24ee58bb7b7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f0e5bae77a7d43fb9d0bb24ee58bb7b72025-01-26T12:24:18ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-84642-3Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional studySiyu Zhou0Yuan Zhang1Huijuan He2Xiangrong Wang3Mengying Li4Na Zhang5Jiali Song6School of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineSchool of Nursing, Hubei University of Chinese MedicineAbstract The symptoms of stroke jeopardize patients’ health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-centered precision medicine. However, the symptom network paradigm, as a novel research approach, addresses the limitations of traditional symptom management by identifying core symptoms and determining intervention targets, thereby enhancing the efficiency and precision of symptom management. This study. aims to explore the symptom network and core symptoms of acute-phase stroke patients. A convenience sample of 505 stroke patients was selected for this study. Symptoms were assessed by the Stroke Symptom Experience Scale.Exploratory factor analysis was utilized to extract symptom clusters, and network analysis was conducted to construct the symptom network and characterize its nodes. In this study, four symptom clusters were extracted through exploratory factor analysis. Based on the results of node predictability(re) and node centrality such as strength centrality (rs), it was found that the symptoms of “No interest in surroundings” (rs = 1.299, re = 1.081), “Be disappointed about future” (rs = 0.922, re = 0.901), and “Unable to maintain body balance” (rs = 0.747, re = 0.744) had the highest centrality and predictability values, indicating their core positions within the symptom network. No interest in surroundings, Be disappointed about future, and Unable to maintain body balance are core symptoms in the symptom network. In the future, intervention methods for core symptoms can be constructed and validated for their intervention effects to further demonstrate the benefits of core symptoms.https://doi.org/10.1038/s41598-024-84642-3StrokeAcute-phasePatient-centered careSymptom networkSymptom clusterCore symptom |
| spellingShingle | Siyu Zhou Yuan Zhang Huijuan He Xiangrong Wang Mengying Li Na Zhang Jiali Song Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study Scientific Reports Stroke Acute-phase Patient-centered care Symptom network Symptom cluster Core symptom |
| title | Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study |
| title_full | Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study |
| title_fullStr | Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study |
| title_full_unstemmed | Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study |
| title_short | Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study |
| title_sort | symptom clusters and networks analysis in acute phase stroke patients a cross sectional study |
| topic | Stroke Acute-phase Patient-centered care Symptom network Symptom cluster Core symptom |
| url | https://doi.org/10.1038/s41598-024-84642-3 |
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