Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF)

The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From...

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Published inPloS one Vol. 19; no. 2; p. e0292270
Main Authors Kraemer, Richard, Baty, Florent, Smith, Hans-Jürgen, Minder, Stefan, Gallati, Sabina, Brutsche, Martin H., Matthys, Heinrich
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 20.02.2024
Public Library of Science (PLoS)
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Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0292270

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Abstract The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV 1 ; FEV 1 /forced vital capacity: FEV 1 /FVC; forced expiratory flow between 25–75% FVC: FEF 25-75 ), and plethysmography (effective, resistive airway resistance: sR eff ; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sR eff IN , sR eff EX , sWOB in , sWOB ex ) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRC pleth ; residual volume: RV), the control of breathing (mouth occlusion pressure: P 0.1 ; mean inspiratory flow: V T /T I ; the inspiratory to total time ratio: T I /T tot ) and the inspiratory impedance (Z in pleth = P 0.1 /V T /T I ) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P 0.1 , sR tot , and V T /T I differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOB ex and Z in pleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Z in pleth may well serve as promising functional marker in the field of precision medicine.
AbstractList The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV 1 ; FEV 1 /forced vital capacity: FEV 1 /FVC; forced expiratory flow between 25–75% FVC: FEF 25-75 ), and plethysmography (effective, resistive airway resistance: sR eff ; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sR eff IN , sR eff EX , sWOB in , sWOB ex ) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRC pleth ; residual volume: RV), the control of breathing (mouth occlusion pressure: P 0.1 ; mean inspiratory flow: V T /T I ; the inspiratory to total time ratio: T I /T tot ) and the inspiratory impedance (Z in pleth = P 0.1 /V T /T I ) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P 0.1 , sR tot , and V T /T I differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOB ex and Z in pleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Z in pleth may well serve as promising functional marker in the field of precision medicine.
The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.
The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV.sub.1 ; FEV.sub.1 /forced vital capacity: FEV.sub.1 /FVC; forced expiratory flow between 25-75% FVC: FEF.sub.25-75 ), and plethysmography (effective, resistive airway resistance: sR.sub.eff ; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sR.sub.eff .sup.IN, sR.sub.eff .sup.EX, sWOB.sub.in, sWOB.sub.ex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRC.sub.pleth ; residual volume: RV), the control of breathing (mouth occlusion pressure: P.sub.0.1 ; mean inspiratory flow: V.sub.T /T.sub.I ; the inspiratory to total time ratio: T.sub.I /T.sub.tot) and the inspiratory impedance (Z.sub.in .sup.pleth = P.sub.0.1 /V.sub.T /T.sub.I) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P.sub.0.1, sR.sub.tot, and V.sub.T /T.sub.I differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOB.sub.ex and Z.sub.in .sup.pleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Z.sub.in .sup.pleth may well serve as promising functional marker in the field of precision medicine.
The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.
Audience Academic
Author Kraemer, Richard
Baty, Florent
Minder, Stefan
Smith, Hans-Jürgen
Gallati, Sabina
Brutsche, Martin H.
Matthys, Heinrich
AuthorAffiliation 3 School of Biomedical and Precision Engineering (SBPE), University of Bern, Bern, Switzerland
University of Western Ontario, CANADA
2 Department of Paediatrics, University of Bern, Bern, Switzerland
6 Hirslanden Precise, Genomic Medicine, Hirslanden Hospital Group, Zollikon/Zürich, Switzerland
1 Centre of Pulmonary Medicine, Hirslanden Hospital Group, Salem-Hospital, Bern, Switzerland
7 Department of Pneumology, University Hospital of Freiburg, Freiburg, Germany
5 Medical Development, Research in Respiratory Diagnostics, Berlin, Germany
4 Department of Pneumology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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SSID ssj0053866
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Snippet The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the...
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pubmedcentral
proquest
gale
pubmed
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SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
StartPage e0292270
SubjectTerms Analysis
Asthma
Asthma - diagnosis
Biology and Life Sciences
Care and treatment
Cystic Fibrosis
Development and progression
Diagnosis
Forced Expiratory Volume
Humans
Lung
Lung diseases, Obstructive
Medicine and Health Sciences
Phenotype
Physical Sciences
Pulmonary Disease, Chronic Obstructive - diagnosis
Pulmonary function tests
Research and Analysis Methods
Spirometry
Vital Capacity
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Title Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF)
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