Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort
Introduction The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment. Method Prospective OSA patient data were recruited from 35 sleep clinics in 21 Europe...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Respiratory Society
2022-10-01
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| Series: | ERJ Open Research |
| Online Access: | http://openres.ersjournals.com/content/8/4/00132-2022.full |
| _version_ | 1797809413477629952 |
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| author | Ashraf Yassen Katrin Coboeken Sébastien Bailly Rolf Burghaus Jitka Buskova Zoran Dogas Marta Drummond Haralampos Gouveris Pavol Joppa Joerg Lippert Carolina Lombardi Stefan Mihaicuta Jean Louis Pépin Ding Zou Jan Hedner Ludger Grote |
| author_facet | Ashraf Yassen Katrin Coboeken Sébastien Bailly Rolf Burghaus Jitka Buskova Zoran Dogas Marta Drummond Haralampos Gouveris Pavol Joppa Joerg Lippert Carolina Lombardi Stefan Mihaicuta Jean Louis Pépin Ding Zou Jan Hedner Ludger Grote |
| author_sort | Ashraf Yassen |
| collection | DOAJ |
| description | Introduction
The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment.
Method
Prospective OSA patient data were recruited from 35 sleep clinics in 21 European countries. Unsupervised cluster analysis (anthropometrics, clinical variables) was performed in a random sample (n=5000). Subsequently, all patients were assigned to the clusters using a conditional inference tree classifier. Responses to PAP treatment change in apnoea severity and Epworth sleepiness scale (ESS) were assessed in relation to baseline patient clusters and at short- and long-term follow-up.
Results
At baseline, 20 164 patients were assigned (mean age 54.1±12.2 years, 73% male, median apnoea–hypopnoea index (AHI) 27.3 (interquartile range (IQR) 14.1–49.3) events·h−1, and ESS 9.8±5.3) to seven distinct clusters based on anthropometrics, comorbidities and symptoms. At PAP follow-up (median 210 [IQR 134–465] days), the observed AHI reduction (n=1075) was similar, whereas the ESS response (n=3938) varied: largest reduction in cluster 3 (young healthy symptomatic males) and 6 (symptomatic males with psychiatric disorders, −5.0 and −5.1 units, respectively (all p<0.01), limited reduction in clusters 2 (obese males with systemic hypertension) and 5 (elderly multimorbid obese males, −4.2 (p<0.05) and −3.7 (p<0.001), respectively). Residual sleepiness in cluster 5 was particularly evident at long-term follow-up (p<0.05).
Conclusion
OSA patients can be classified into clusters based on clinically identifiable features. Importantly, these clusters may be useful for prediction of both short- and long-term responses to PAP intervention. |
| first_indexed | 2024-03-13T06:52:21Z |
| format | Article |
| id | doaj.art-13685d8465664bd099479d05e2d3f6f1 |
| institution | Directory Open Access Journal |
| issn | 2312-0541 |
| language | English |
| last_indexed | 2024-03-13T06:52:21Z |
| publishDate | 2022-10-01 |
| publisher | European Respiratory Society |
| record_format | Article |
| series | ERJ Open Research |
| spelling | doaj.art-13685d8465664bd099479d05e2d3f6f12023-06-07T13:30:36ZengEuropean Respiratory SocietyERJ Open Research2312-05412022-10-018410.1183/23120541.00132-202200132-2022Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohortAshraf Yassen0Katrin Coboeken1Sébastien Bailly2Rolf Burghaus3Jitka Buskova4Zoran Dogas5Marta Drummond6Haralampos Gouveris7Pavol Joppa8Joerg Lippert9Carolina Lombardi10Stefan Mihaicuta11Jean Louis Pépin12Ding Zou13Jan Hedner14Ludger Grote15 Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany University Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble, France Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany National Institute of Mental Health, Klecany, Czech Republic Department of Neuroscience, Sleep Medicine Center, University of Split School of Medicine, Split, Croatia Sleep and Non-Invasive Ventilation Unit of University Hospital São João, Medicine Faculty of Porto, Porto, Portugal ENT Department at Mainz University Hospital, Mainz, Germany Department of Respiratory Medicine and Tuberculosis, Faculty of Medicine, P.J. Safarik University and L. Pasteur University Hospital, Kosice, Slovakia Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany Istituto Auxologico Italiano IRCCS, Sleep Disorders Center, San Luca Hospital, Milan, Italy Centre for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy, CardioPrevent Foundation, Timisoara, Romania University Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble, France Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Wake Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Wake Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Wake Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden Introduction The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment. Method Prospective OSA patient data were recruited from 35 sleep clinics in 21 European countries. Unsupervised cluster analysis (anthropometrics, clinical variables) was performed in a random sample (n=5000). Subsequently, all patients were assigned to the clusters using a conditional inference tree classifier. Responses to PAP treatment change in apnoea severity and Epworth sleepiness scale (ESS) were assessed in relation to baseline patient clusters and at short- and long-term follow-up. Results At baseline, 20 164 patients were assigned (mean age 54.1±12.2 years, 73% male, median apnoea–hypopnoea index (AHI) 27.3 (interquartile range (IQR) 14.1–49.3) events·h−1, and ESS 9.8±5.3) to seven distinct clusters based on anthropometrics, comorbidities and symptoms. At PAP follow-up (median 210 [IQR 134–465] days), the observed AHI reduction (n=1075) was similar, whereas the ESS response (n=3938) varied: largest reduction in cluster 3 (young healthy symptomatic males) and 6 (symptomatic males with psychiatric disorders, −5.0 and −5.1 units, respectively (all p<0.01), limited reduction in clusters 2 (obese males with systemic hypertension) and 5 (elderly multimorbid obese males, −4.2 (p<0.05) and −3.7 (p<0.001), respectively). Residual sleepiness in cluster 5 was particularly evident at long-term follow-up (p<0.05). Conclusion OSA patients can be classified into clusters based on clinically identifiable features. Importantly, these clusters may be useful for prediction of both short- and long-term responses to PAP intervention.http://openres.ersjournals.com/content/8/4/00132-2022.full |
| spellingShingle | Ashraf Yassen Katrin Coboeken Sébastien Bailly Rolf Burghaus Jitka Buskova Zoran Dogas Marta Drummond Haralampos Gouveris Pavol Joppa Joerg Lippert Carolina Lombardi Stefan Mihaicuta Jean Louis Pépin Ding Zou Jan Hedner Ludger Grote Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort ERJ Open Research |
| title | Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort |
| title_full | Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort |
| title_fullStr | Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort |
| title_full_unstemmed | Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort |
| title_short | Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort |
| title_sort | baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients longitudinal data from the european sleep apnea database cohort |
| url | http://openres.ersjournals.com/content/8/4/00132-2022.full |
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