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Clusters of sleep apnoea phenotypes: A large pan-European study from the European Sleep Apnoea Database (ESADA).
Bailly, Sébastien; Grote, Ludger; Hedner, Jan; Schiza, Sofia; McNicholas, Walter T; Basoglu, Ozen K; Lombardi, Carolina; Dogas, Zoran; Roisman, Gabriel; Pataka, Athanasia; Bonsignore, Maria R; Pepin, Jean-Louis.
Afiliação
  • Bailly S; HP2 Laboratory, Grenoble Alpes University, INSERM U1042, Grenoble, France.
  • Grote L; EFCR Laboratory, Grenoble Alpes University Hospital, Grenoble, France.
  • Hedner J; Department of Sleep Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Schiza S; Sleep and Vigilance Laboratory, Internal Medicine, University of Gothenburg, Gothenburg, Sweden.
  • McNicholas WT; Department of Sleep Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Basoglu OK; Sleep and Vigilance Laboratory, Internal Medicine, University of Gothenburg, Gothenburg, Sweden.
  • Lombardi C; Sleep Disorders Unit, Department of Respiratory Medicine, Medical School, University of Crete, Crete, Greece.
  • Dogas Z; Department of Respiratory and Sleep Medicine, St. Vincent's Hospital Group, Dublin, Ireland.
  • Roisman G; Conway Research Institute, School of Medicine, University College Dublin, Dublin, Ireland.
  • Pataka A; Department of Chest Diseases, Ege University, Izmir, Turkey.
  • Bonsignore MR; Sleep Disorder Center, Cardiology Department, Istituto Auxologico Italiano IRCCS, Ospedale San Luca, University of Milano Bicocca, Milan, Italy.
  • Pepin JL; Split Sleep Medicine Centre and Department of Neuroscience, University of Split School of Medicine, Split, Croatia.
Respirology ; 26(4): 378-387, 2021 04.
Article em En | MEDLINE | ID: mdl-33140467
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndromes da Apneia do Sono Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndromes da Apneia do Sono Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article