Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis
BackgroundPrevious studies have analyzed symptom clusters in patients with coronavirus disease 2019 (COVID-19); however, evidence regarding the core symptom clusters and their influencing factors in patients with post-COVID-19 pulmonary fibrosis (PCPF) remains unclear, affecting the precision of sym...
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Frontiers Media S.A.
2025-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1538708/full |
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| author | Zhen Yang Zhen Yang Zhiqin Xie Zequan Wang Zequan Wang Linxia Yi Linxia Yi Shihan Chen Shihan Chen Yunyu Du Xuemei Tao Xuemei Tao Chao Xie Chao Xie Li Zhou Min Zhang Chaozhu He |
| author_facet | Zhen Yang Zhen Yang Zhiqin Xie Zequan Wang Zequan Wang Linxia Yi Linxia Yi Shihan Chen Shihan Chen Yunyu Du Xuemei Tao Xuemei Tao Chao Xie Chao Xie Li Zhou Min Zhang Chaozhu He |
| author_sort | Zhen Yang |
| collection | DOAJ |
| description | BackgroundPrevious studies have analyzed symptom clusters in patients with coronavirus disease 2019 (COVID-19); however, evidence regarding the core symptom clusters and their influencing factors in patients with post-COVID-19 pulmonary fibrosis (PCPF) remains unclear, affecting the precision of symptom interventions.ObjectivesThis study aimed to identify the symptom clusters and core symptom clusters in patients with PCPF. Demographic and disease-related factors associated with these symptom clusters were also analyzed.MethodsA total of 350 patients with PCPF were recruited from China between January 2023 and April 2024. A self-reported symptom assessment scale was used for this survey. Principal component analysis was used to identify symptom clusters. Network analysis was used to describe the relationships between the symptoms and symptom clusters. Multiple linear models were used to analyze the factors affecting the total symptom severity and each symptom cluster.ResultsSix symptom clusters were identified: Upper Respiratory Tract Symptom Cluster (USC), Lower Respiratory Tract Symptom Cluster (LSC), Somatic Symptom Cluster (SSC), Muscular and Joint Symptom Cluster (MSC), Neurological and Psychological Symptom Cluster (NSC), and Digestive Symptom Cluster (DSC). Fatigue was identified as the core and bridge symptom in the symptom network, whereas the upper respiratory symptom cluster was identified as the core and bridge symptom cluster. Gender, age, educational level, smoking history, and primary caregiver were associated with the scores of the six symptom clusters.ConclusionOur study suggests that there is a need to evaluate symptom clusters for the improvement of symptom management among PCPF. Specifically, the assessment and treatment of upper respiratory and fatigue symptoms as core targets of PCPF care is critical for the development of accurate and efficient symptom management strategies. |
| format | Article |
| id | doaj-art-19a72f98bb4c469a9b9ef0dfd513e810 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-19a72f98bb4c469a9b9ef0dfd513e8102025-08-20T03:13:39ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15387081538708Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysisZhen Yang0Zhen Yang1Zhiqin Xie2Zequan Wang3Zequan Wang4Linxia Yi5Linxia Yi6Shihan Chen7Shihan Chen8Yunyu Du9Xuemei Tao10Xuemei Tao11Chao Xie12Chao Xie13Li Zhou14Min Zhang15Chaozhu He16Department of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Medical Center for Critical Public Health Events, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Medical Center for Critical Public Health Events, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Medical Center for Critical Public Health Events, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaBackgroundPrevious studies have analyzed symptom clusters in patients with coronavirus disease 2019 (COVID-19); however, evidence regarding the core symptom clusters and their influencing factors in patients with post-COVID-19 pulmonary fibrosis (PCPF) remains unclear, affecting the precision of symptom interventions.ObjectivesThis study aimed to identify the symptom clusters and core symptom clusters in patients with PCPF. Demographic and disease-related factors associated with these symptom clusters were also analyzed.MethodsA total of 350 patients with PCPF were recruited from China between January 2023 and April 2024. A self-reported symptom assessment scale was used for this survey. Principal component analysis was used to identify symptom clusters. Network analysis was used to describe the relationships between the symptoms and symptom clusters. Multiple linear models were used to analyze the factors affecting the total symptom severity and each symptom cluster.ResultsSix symptom clusters were identified: Upper Respiratory Tract Symptom Cluster (USC), Lower Respiratory Tract Symptom Cluster (LSC), Somatic Symptom Cluster (SSC), Muscular and Joint Symptom Cluster (MSC), Neurological and Psychological Symptom Cluster (NSC), and Digestive Symptom Cluster (DSC). Fatigue was identified as the core and bridge symptom in the symptom network, whereas the upper respiratory symptom cluster was identified as the core and bridge symptom cluster. Gender, age, educational level, smoking history, and primary caregiver were associated with the scores of the six symptom clusters.ConclusionOur study suggests that there is a need to evaluate symptom clusters for the improvement of symptom management among PCPF. Specifically, the assessment and treatment of upper respiratory and fatigue symptoms as core targets of PCPF care is critical for the development of accurate and efficient symptom management strategies.https://www.frontiersin.org/articles/10.3389/fmed.2025.1538708/fullCOVID-19pulmonary fibrosislung diseasesinterstitialsyndromesocial network analysis |
| spellingShingle | Zhen Yang Zhen Yang Zhiqin Xie Zequan Wang Zequan Wang Linxia Yi Linxia Yi Shihan Chen Shihan Chen Yunyu Du Xuemei Tao Xuemei Tao Chao Xie Chao Xie Li Zhou Min Zhang Chaozhu He Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis Frontiers in Medicine COVID-19 pulmonary fibrosis lung diseases interstitial syndrome social network analysis |
| title | Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis |
| title_full | Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis |
| title_fullStr | Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis |
| title_full_unstemmed | Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis |
| title_short | Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis |
| title_sort | identifying central symptom clusters and correlates among post covid 19 pulmonary fibrosis patients a network analysis |
| topic | COVID-19 pulmonary fibrosis lung diseases interstitial syndrome social network analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1538708/full |
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