A common model for the breathlessness experience across cardiorespiratory disease. Issue 2 (28th June 2021)

• Record Type: Journal Article
• Authors: Finnegan, Sarah L.; Pattinson, Kyle T.S.; Sundh, Josefin; Sköld, Magnus; Janson, Christer; Blomberg, Anders; Sandberg, Jacob; Ekström, Magnus
• Is Part Of: ERJ open research. Volume 7:Issue 2(2021)
• Publication Date: 2021-06-28
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; Respiration; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Respiratory Tract Diseases; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.2005
• DOI: 10.1183/23120541.00818-2020
• Languages: English
• ISSNs: 2312-0541

Abstract

Chronic breathlessness occurs across many different conditions, often independently of disease severity. Yet, despite being strongly linked to adverse outcomes, the consideration of chronic breathlessness as a stand-alone therapeutic target remains limited. Here we use data-driven techniques to identify and confirm the stability of underlying features (factors) driving breathlessness across different cardiorespiratory diseases. Questionnaire data on 182 participants with main diagnoses of asthma (21.4%), COPD (24.7%), heart failure (19.2%), idiopathic pulmonary fibrosis (18.7%), other interstitial lung disease (2.7%), and "other diagnoses" (13.2%) were entered into an exploratory factor analysis (EFA). Participants were stratified based on their EFA factor scores. We then examined model stability using 6-month follow-up data and established the most compact set of measures describing the breathlessness experience. In this dataset, we have identified four stable factors that underlie the experience of breathlessness. These factors were assigned the following descriptive labels: 1) body burden, 2) affect/mood, 3) breathing burden and 4) anger/frustration. Stratifying patients by their scores across the four factors revealed two groups corresponding to high and low burden. These two groups were not related to the primary disease diagnosis and remained stable after 6 months. In this work, we identified and confirmed the stability of underlying features of breathlessness. Previous work in this domain has been largely limited to single-diagnosis patient groups without subsequent re-testing of model stability. This work provides further evidence supporting disease independent approaches to assess breathlessness. Using machine learning techniques, four underlying factors of breathlessness in cardiorespiratory disease were identified. These underlying factors of the breathlessness experience were similar across diseases and remained stable over time. https://bit.ly/3brtD8c