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Factors most strongly associated with breathlessness in a population aged 50–64 years

Olsson, Max LU orcid ; Björkelund, Anders j LU ; Sandberg, Jacob LU orcid ; Blomberg, Anders ; Börjesson, Mats ; Currow, David ; Malinovschi, Andrei ; Sköld, Magnus ; Wollmer, Per LU and Torén, Kjell , et al. (2024) In ERJ open research 10(2).
Abstract
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... (More)
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.
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
breathlessness, dyspnea, factors, machine learning
in
ERJ open research
volume
10
issue
2
article number
00582-2023
pages
38 pages
publisher
European Respiratory Society
external identifiers
  • scopus:85189107812
ISSN
2312-0541
DOI
10.1183/23120541.00582-2023
project
AIR Lund - Artificially Intelligent use of Registers
Breathe-AI: Evaluation of breathlessness and quality of life in the population using machine learning.
language
English
LU publication?
yes
id
043d2343-abbe-41f5-9c0c-b0bfe1924c22
date added to LUP
2024-03-25 11:09:53
date last changed
2024-04-17 13:25:29
@article{043d2343-abbe-41f5-9c0c-b0bfe1924c22,
  abstract     = {{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.<br/>Methods: a cross-sectional analysis of the multicentre population-based Swedish<br/>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<br/>factors and breathlessness were measured by SHapley Additive exPlanations (SHAP), with higher scores reflecting stronger associations.<br/>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).<br/>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.<br/>}},
  author       = {{Olsson, Max and Björkelund, Anders j and Sandberg, Jacob and Blomberg, Anders and Börjesson, Mats and Currow, David and Malinovschi, Andrei and Sköld, Magnus and Wollmer, Per and Torén, Kjell and Östgren, Carl johan and Engström, Gunnar and Ekström, Magnus}},
  issn         = {{2312-0541}},
  keywords     = {{breathlessness; dyspnea; factors; machine learning}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{European Respiratory Society}},
  series       = {{ERJ open research}},
  title        = {{Factors most strongly associated with breathlessness in a population aged 50–64 years}},
  url          = {{http://dx.doi.org/10.1183/23120541.00582-2023}},
  doi          = {{10.1183/23120541.00582-2023}},
  volume       = {{10}},
  year         = {{2024}},
}