Sébastien Bailly1,2, Ludger Grote3,4, Jan Hedner3,4, Sofia Schiza5, Walter T McNicholas6,7, Ozen K Basoglu8, Carolina Lombardi9, Zoran Dogas10, Gabriel Roisman11, Athanasia Pataka12, Maria R Bonsignore13, Jean-Louis Pepin1,2. 1. HP2 Laboratory, Grenoble Alpes University, INSERM U1042, Grenoble, France. 2. EFCR Laboratory, Grenoble Alpes University Hospital, Grenoble, France. 3. Department of Sleep Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden. 4. Sleep and Vigilance Laboratory, Internal Medicine, University of Gothenburg, Gothenburg, Sweden. 5. Sleep Disorders Unit, Department of Respiratory Medicine, Medical School, University of Crete, Crete, Greece. 6. Department of Respiratory and Sleep Medicine, St. Vincent's Hospital Group, Dublin, Ireland. 7. Conway Research Institute, School of Medicine, University College Dublin, Dublin, Ireland. 8. Department of Chest Diseases, Ege University, Izmir, Turkey. 9. Sleep Disorder Center, Cardiology Department, Istituto Auxologico Italiano IRCCS, Ospedale San Luca, University of Milano Bicocca, Milan, Italy. 10. Split Sleep Medicine Centre and Department of Neuroscience, University of Split School of Medicine, Split, Croatia. 11. Sleep Disorders Centre, Antoine Béclère Hospital, Clamart, France. 12. Respiratory Failure Unit, G Papanikolaou Hospital, Aristotle University, Thessaloniki, Greece. 13. Respiratory Medicine, PROMISE Department, University of Palermo and IRIB-CNR, Palermo, Italy.
Abstract
BACKGROUND AND OBJECTIVE: To personalize OSA management, several studies have attempted to better capture disease heterogeneity by clustering methods. The aim of this study was to conduct a cluster analysis of 23 000 OSA patients at diagnosis using the multinational ESADA. METHODS: Data from 34 centres contributing to ESADA were used. An LCA was applied to identify OSA phenotypes in this European population representing broad geographical variations. Many variables, including symptoms, comorbidities and polysomnographic data, were included. Prescribed medications were classified according to the ATC classification and this information was used for comorbidity confirmation. RESULTS: Eight clusters were identified. Four clusters were gender-based corresponding to 54% of patients, with two clusters consisting only of men and two clusters only of women. The remaining four clusters were mainly men with various combinations of age range, BMI, AHI and comorbidities. The preferred type of OSA treatment (PAP or mandibular advancement) varied between clusters. CONCLUSION: Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.
BACKGROUND AND OBJECTIVE: To personalize OSA management, several studies have attempted to better capture disease heterogeneity by clustering methods. The aim of this study was to conduct a cluster analysis of 23 000 OSA patients at diagnosis using the multinational ESADA. METHODS: Data from 34 centres contributing to ESADA were used. An LCA was applied to identify OSA phenotypes in this European population representing broad geographical variations. Many variables, including symptoms, comorbidities and polysomnographic data, were included. Prescribed medications were classified according to the ATC classification and this information was used for comorbidity confirmation. RESULTS: Eight clusters were identified. Four clusters were gender-based corresponding to 54% of patients, with two clusters consisting only of men and two clusters only of women. The remaining four clusters were mainly men with various combinations of age range, BMI, AHI and comorbidities. The preferred type of OSA treatment (PAP or mandibular advancement) varied between clusters. CONCLUSION: Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.
Authors: Dries Testelmans; M A Spruit; B Vrijsen; M Sastry; C Belge; A Kalkanis; S Gaffron; E F M Wouters; B Buyse Journal: Sleep Breath Date: 2021-05-03 Impact factor: 2.816
Authors: Xiao Lei Zhang; Li Zhang; Yi Ming Li; Bo Yun Xiang; Teng Han; Yan Wang; Chen Wang Journal: J Clin Sleep Med Date: 2022-07-01 Impact factor: 4.324