S. Houben-Wilke, Qichen Deng, D. Janssen, F. Franssen, M. Spruit
{"title":"Symptom burden and its associations with clinical characteristics in patients with COPD: a clustering approach","authors":"S. Houben-Wilke, Qichen Deng, D. Janssen, F. Franssen, M. Spruit","doi":"10.1183/23120541.01052-2023","DOIUrl":null,"url":null,"abstract":"Symptom burden in people with COPD is often under-recognized. In this cross-sectional analysis, we aimed to study the severity of a variety of (non-)respiratory symptoms in people with COPD and non-COPD subjects and to explore the associations between clusters based on symptom severity and other clinical characteristics.Characteristics were assessed in 538 people with COPD from primary, secondary, tertiary care and 116 non-COPD subjects. Severity of 20 symptoms was measured using a Visual Analogue Scale (VAS), ranging from 0 (no symptom) to 100 mm (maximum severity). K-means cluster analysis was applied on symptoms’ severity in the patient sample only.Patients were comparable with non-COPD subjects in terms of gender (58% vs. 55% male, p=0.132) and age (64 [9]versus63 [6] years, p=0.552) and had a reduced FEV1(57 [23]%versus111 [17]% pred, p<0.001). Patients had higher VAS scores for most symptoms (p<0.05). Most severe symptoms in patients with COPD were dyspnea, fatigue, and muscle weakness while non-COPD subjects mainly experienced insomnia and micturition. Three clusters were identified in the patient sample. Health status and care dependency differed between all clusters, while functional mobility, exacerbation history and lung function differed between clusters 1 and the other two clusters (p<0.05).People with COPD report a high burden of respiratory as well as non-respiratory symptoms. Cluster analysis demonstrated a co-occurrence of different levels of symptom severity highlighting the heterogeneity of symptoms experience. Identifying clusters of patients with shared symptom experiences can help to understand the impact of the disease and define integrated, multidimensional treatment strategies.","PeriodicalId":504874,"journal":{"name":"ERJ Open Research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERJ Open Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1183/23120541.01052-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Symptom burden in people with COPD is often under-recognized. In this cross-sectional analysis, we aimed to study the severity of a variety of (non-)respiratory symptoms in people with COPD and non-COPD subjects and to explore the associations between clusters based on symptom severity and other clinical characteristics.Characteristics were assessed in 538 people with COPD from primary, secondary, tertiary care and 116 non-COPD subjects. Severity of 20 symptoms was measured using a Visual Analogue Scale (VAS), ranging from 0 (no symptom) to 100 mm (maximum severity). K-means cluster analysis was applied on symptoms’ severity in the patient sample only.Patients were comparable with non-COPD subjects in terms of gender (58% vs. 55% male, p=0.132) and age (64 [9]versus63 [6] years, p=0.552) and had a reduced FEV1(57 [23]%versus111 [17]% pred, p<0.001). Patients had higher VAS scores for most symptoms (p<0.05). Most severe symptoms in patients with COPD were dyspnea, fatigue, and muscle weakness while non-COPD subjects mainly experienced insomnia and micturition. Three clusters were identified in the patient sample. Health status and care dependency differed between all clusters, while functional mobility, exacerbation history and lung function differed between clusters 1 and the other two clusters (p<0.05).People with COPD report a high burden of respiratory as well as non-respiratory symptoms. Cluster analysis demonstrated a co-occurrence of different levels of symptom severity highlighting the heterogeneity of symptoms experience. Identifying clusters of patients with shared symptom experiences can help to understand the impact of the disease and define integrated, multidimensional treatment strategies.