Journal
RESPIROLOGY
Volume 26, Issue 4, Pages 378-387Publisher
WILEY
DOI: 10.1111/resp.13969
Keywords
cluster analysis; international database; personalized medicine; phenotypes; sleep apnoea
Categories
Funding
- French National Research Agency in the framework of the 'Investissements d'avenir' program [ANR-15-IDEX-02]
- 'e-health and integrated care and trajectories medicine and MIAI artificial intelligence' Chairs of excellence from the Grenoble Alpes University Foundation
- MIAI @ Grenoble Alpes [ANR-19-P3IA-0003]
- European Respiratory Society
- ResMed
- Philips
- Bayer Pharmaceuticals
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Eight distinct clinical OSA phenotypes were identified in a large pan-European database. Four clusters were gender-based, while the remaining four clusters mainly consisted of men with various combinations of age range, BMI, AHI, and comorbidities.
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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