Anders Björkelund
Researcher
Factors most strongly associated with breathlessness in a population aged 50–64 years
Author
Summary, in English
Background: Breathlessness is a troublesome and prevalent symptom in the population, but knowledge of related factors is scarce. The aim of this study was to identify the factors most strongly associated with breathlessness in the general population and to describe the shapes of the associations between the main factors and breathlessness.
Methods: a cross-sectional analysis of the multicentre population-based Swedish
CArdioPulmonary bioImage Study (SCAPIS) of adults aged 50 to 64 years. Breathlessness was defined as a modified Medical Research Council (mMRC) breathlessness rating ≥2. The machine-learning algorithm extreme gradient boosting (XGBoost) was used to classify participants as either breathless or nonbreathless using 449 factors, including physiological measurements, blood samples, computer tomography cardiac and lung measurements, lifestyle, health conditions, and socioeconomics. The strength of the associations between the
factors and breathlessness were measured by SHapley Additive exPlanations (SHAP), with higher scores reflecting stronger associations.
Results: A total of 28,730 participants (52% women) were included in the study. The strongest associated factors for breathlessness were (in order of magnitude): body mass index (BMI; [SHAP score] 0.39), forced expiratory volume in 1 second (FEV1; 0.32), physical activity measured by accelerometery (0.27), sleep apnoea (0.22), diffusing lung capacity for carbon monoxide (0.21), self-reported physical activity (0.17), chest pain when hurrying (0.17), high-sensitivity C-reactive protein (hs-CRP) (0.17), recent weight change (0.14), and cough (0.13).
Conclusion: This large population-based study of men and women aged 50 - 64 years identified the main factors related to breathlessness that may be prevented or amenable to public health interventions.
Methods: a cross-sectional analysis of the multicentre population-based Swedish
CArdioPulmonary bioImage Study (SCAPIS) of adults aged 50 to 64 years. Breathlessness was defined as a modified Medical Research Council (mMRC) breathlessness rating ≥2. The machine-learning algorithm extreme gradient boosting (XGBoost) was used to classify participants as either breathless or nonbreathless using 449 factors, including physiological measurements, blood samples, computer tomography cardiac and lung measurements, lifestyle, health conditions, and socioeconomics. The strength of the associations between the
factors and breathlessness were measured by SHapley Additive exPlanations (SHAP), with higher scores reflecting stronger associations.
Results: A total of 28,730 participants (52% women) were included in the study. The strongest associated factors for breathlessness were (in order of magnitude): body mass index (BMI; [SHAP score] 0.39), forced expiratory volume in 1 second (FEV1; 0.32), physical activity measured by accelerometery (0.27), sleep apnoea (0.22), diffusing lung capacity for carbon monoxide (0.21), self-reported physical activity (0.17), chest pain when hurrying (0.17), high-sensitivity C-reactive protein (hs-CRP) (0.17), recent weight change (0.14), and cough (0.13).
Conclusion: This large population-based study of men and women aged 50 - 64 years identified the main factors related to breathlessness that may be prevented or amenable to public health interventions.
Department/s
- EpiHealth: Epidemiology for Health
- Breathlessness and chronic respiratory failure
- Respiratory Medicine, Allergology, and Palliative Medicine
- Department of Clinical Sciences, Lund
- Computational Science for Health and Environment
- Centre for Environmental and Climate Science (CEC)
- LTH Profile Area: Aerosols
- Clinical Physiology and Nuclear Medicine, Malmö
- Department of Translational Medicine
- Cardiovascular Research - Epidemiology
- Department of Clinical Sciences, Malmö
Publishing year
2024
Language
English
Publication/Series
ERJ open research
Volume
10
Issue
2
Document type
Journal article
Publisher
European Respiratory Society
Topic
- Respiratory Medicine and Allergy
- Public Health, Global Health, Social Medicine and Epidemiology
Keywords
- breathlessness
- dyspnea
- factors
- machine learning
Status
Published
Project
- AIR Lund - Artificially Intelligent use of Registers
- Breathe-AI: Evaluation of breathlessness and quality of life in the population using machine learning.
Research group
- Breathlessness and chronic respiratory failure
- Computational Science for Health and Environment
- Clinical Physiology and Nuclear Medicine, Malmö
- Cardiovascular Research - Epidemiology
ISBN/ISSN/Other
- ISSN: 2312-0541