Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing
Abstract The dispersion of the tidal volume and of the breathing frequency have been used to diagnose dysfunctional breathing during cardio‐pulmonary exercise testing. No validated methods to objectively describe this dispersion exist. We aimed to validate such a method. We used simulations based on...
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Wiley
2025-03-01
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| Series: | Physiological Reports |
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| Online Access: | https://doi.org/10.14814/phy2.70233 |
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| author | Léon Genecand Cyril Jaksic Roberto Desponds Gaëtan Simian Ivan Guerreiro Sara Thorens Marco Altarelli Isabelle Frésard Chloé Cantero Aurélien Bringard Antoine Beurnier Pierantonio Laveneziana David Montani Anne Bergeron Frédéric Lador Pierre‐Olivier Bridevaux |
| author_facet | Léon Genecand Cyril Jaksic Roberto Desponds Gaëtan Simian Ivan Guerreiro Sara Thorens Marco Altarelli Isabelle Frésard Chloé Cantero Aurélien Bringard Antoine Beurnier Pierantonio Laveneziana David Montani Anne Bergeron Frédéric Lador Pierre‐Olivier Bridevaux |
| author_sort | Léon Genecand |
| collection | DOAJ |
| description | Abstract The dispersion of the tidal volume and of the breathing frequency have been used to diagnose dysfunctional breathing during cardio‐pulmonary exercise testing. No validated methods to objectively describe this dispersion exist. We aimed to validate such a method. We used simulations based on real‐life parameters. Moving standard deviation (MSD) and residuals from locally estimated scatterplot smoothing (LOESS) were evaluated. The precision and the bias of each tested method at rest and during exercise simulations, with and without sighs, were measured. For LOESS, a 2nd degree polynomial was used, and different spans were tested (LOESS1, LOESS0.75, and LOESS0.5). For MSD, different number of points used for the calculation were tested (MSD7, MSD11, MSD15, and MSD19). The LOESS method was globally more precise, had less bias, and was less influenced by the trend as compared to MSD in almost all simulations except for extremely low dispersion combined with extreme trends. LOESS0.75 had intermediate bias and precision between LOESS0.5 and LOESS1 in all simulations. LOESS0.75 is a method that combines high precision, low bias, and low influenceability of trends. It could be considered as the method of choice to evaluate the dispersion of breathing parameters during cardiopulmonary exercise testing. |
| format | Article |
| id | doaj-art-3b48bd4d00fe4481bbc0f44e672c4c58 |
| institution | DOAJ |
| issn | 2051-817X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Physiological Reports |
| spelling | doaj-art-3b48bd4d00fe4481bbc0f44e672c4c582025-08-20T03:05:43ZengWileyPhysiological Reports2051-817X2025-03-01135n/an/a10.14814/phy2.70233Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathingLéon Genecand0Cyril Jaksic1Roberto Desponds2Gaëtan Simian3Ivan Guerreiro4Sara Thorens5Marco Altarelli6Isabelle Frésard7Chloé Cantero8Aurélien Bringard9Antoine Beurnier10Pierantonio Laveneziana11David Montani12Anne Bergeron13Frédéric Lador14Pierre‐Olivier Bridevaux15Service de Pneumologie, Département de Médecine Hôpitaux Universitaires de Genève Genève SwitzerlandCentre de Recherche Clinique Hôpitaux Universitaires de Genève Genève SwitzerlandFaculté de Mathématique Université de Genève Genève SwitzerlandFaculté de Mathématique Université de Genève Genève SwitzerlandService de Pneumologie, Département de Médecine Hôpitaux Universitaires de Genève Genève SwitzerlandFaculté de Médecine Université de Genève Genève SwitzerlandService de Pneumologie Hôpital du Valais, Centre Hospitalier du Valais Romand Sion SwitzerlandService de Pneumologie Hôpital du Valais, Centre Hospitalier du Valais Romand Sion SwitzerlandService de Pneumologie, Département de Médecine Hôpitaux Universitaires de Genève Genève SwitzerlandService de Pneumologie, Département de Médecine Hôpitaux Universitaires de Genève Genève SwitzerlandUniversité Paris‐Saclay, School of Medicine Le Kremlin‐Bicêtre FranceAP‐HP, Groupe Hospitalier Universitaire APHP‐Sorbonne Université, Hôpitaux Pitié‐Salpêtrière, Saint‐Antoine et Tenon, Service Des Explorations Fonctionnelles de la Respiration, de l'Exercice et de la Dyspnée (Département R3S) Paris FranceUniversité Paris‐Saclay, School of Medicine Le Kremlin‐Bicêtre FranceService de Pneumologie, Département de Médecine Hôpitaux Universitaires de Genève Genève SwitzerlandFaculté de Médecine Université de Genève Genève SwitzerlandFaculté de Médecine Université de Genève Genève SwitzerlandAbstract The dispersion of the tidal volume and of the breathing frequency have been used to diagnose dysfunctional breathing during cardio‐pulmonary exercise testing. No validated methods to objectively describe this dispersion exist. We aimed to validate such a method. We used simulations based on real‐life parameters. Moving standard deviation (MSD) and residuals from locally estimated scatterplot smoothing (LOESS) were evaluated. The precision and the bias of each tested method at rest and during exercise simulations, with and without sighs, were measured. For LOESS, a 2nd degree polynomial was used, and different spans were tested (LOESS1, LOESS0.75, and LOESS0.5). For MSD, different number of points used for the calculation were tested (MSD7, MSD11, MSD15, and MSD19). The LOESS method was globally more precise, had less bias, and was less influenced by the trend as compared to MSD in almost all simulations except for extremely low dispersion combined with extreme trends. LOESS0.75 had intermediate bias and precision between LOESS0.5 and LOESS1 in all simulations. LOESS0.75 is a method that combines high precision, low bias, and low influenceability of trends. It could be considered as the method of choice to evaluate the dispersion of breathing parameters during cardiopulmonary exercise testing.https://doi.org/10.14814/phy2.70233abnormal breathing patterncardio‐pulmonary exercise testingdispersiondysfunctional breathingsimulations |
| spellingShingle | Léon Genecand Cyril Jaksic Roberto Desponds Gaëtan Simian Ivan Guerreiro Sara Thorens Marco Altarelli Isabelle Frésard Chloé Cantero Aurélien Bringard Antoine Beurnier Pierantonio Laveneziana David Montani Anne Bergeron Frédéric Lador Pierre‐Olivier Bridevaux Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing Physiological Reports abnormal breathing pattern cardio‐pulmonary exercise testing dispersion dysfunctional breathing simulations |
| title | Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing |
| title_full | Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing |
| title_fullStr | Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing |
| title_full_unstemmed | Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing |
| title_short | Comparing methods to measure the dispersion of breathing parameters during exercise testing: A simulation study based on real‐life parameters from patients with dysfunctional breathing |
| title_sort | comparing methods to measure the dispersion of breathing parameters during exercise testing a simulation study based on real life parameters from patients with dysfunctional breathing |
| topic | abnormal breathing pattern cardio‐pulmonary exercise testing dispersion dysfunctional breathing simulations |
| url | https://doi.org/10.14814/phy2.70233 |
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