Clusters of sleep apnoea phenotypes: A large pan‐European study from the European Sleep Apnoea Database (ESADA)

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.

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

“…Regarding this last aspect, in our study, both subjective excessive daytime sleepiness and Epworth Sleepiness Scale were not considered in the cluster analysis. In 2020, a study by Bailly et al [ 21 ] applied latent class analysis to identify OSA phenotypes while reflecting geographical variations, resulting in 8 distinct clusters that were divided into 2 main categories: gender-based phenotypes (clusters 2 and 6 with only men and clusters 7 and 8 with only women) and men with various combinations (clusters 1, 3, 4, and 5), with which we can compare results. Cluster 3 of the study by Bailly et al [ 21 ] is described as obese comorbid patients, being the most similar to our low severity OSA cluster, presenting almost the same percentage of males (69% vs 73%) and higher levels of metabolic comorbidities.…”

“…Aspiring to a more personalized approach to evaluate patients with OSA and targeting to recognize high pretest probability for OSA, cluster analysis (a statistical approach for studying the relationship present among groups of patients or variables [7]) was applied to distinguish whether there are different subgroups of patients with different clinical presentations, that is phenotypes. Clustering has been widely used in health research, particularly in the analysis of gene expression [15], asthma [16], chronic obstructive pulmonary disease [17], fibromyalgia [18], Parkinson disease [19], and sleep apnea [20][21][22]. The aim is to identify clusters of patients who are similar among themselves, although significantly different from patients of other clusters [7].…”

Getting More from the Sleep Recording

“…This aspect has been recognized for many years, 9,10 and more recently, there is strong evidence that clusters of different clinical phenotypes can be identified among the broad population of patients presenting for assessment. 11 Furthermore, certain pathophysiological traits that are very common in OSA such as loss of nocturnal dipping of blood pressure (BP) have significant implications for the development of associated comorbidity. 12 Thus, whatever sleep study is used in the assessment of suspected OSA, the findings must be integrated into the overall assessment of the patient as regards clinical significance, and management should be linked to the underlying clinical and pathophysiological phenotypes where additional factors to the AHI such as acute systemic effects and associated relevant comorbidity are factored into the decision-making process.…”

Obstructive sleep apnoea phenotypes: The many faces of a public health monolith

“…In a recent publication in Respirology, the European Sleep Apnoea Database (ESADA) collaborators extend from their previous work 11 to introduce the largest cluster analysis to date of over 23 000 clinical subjects with a diagnosis of OSA, from 34 specialist sleep centre across Europe and Israel. 15 From 20 input variables, this analysis identifies eight phenotypically distinct clusters. Four of these were gender aligned with two exclusively including women separated by middle and older age categories and corresponding allocations into milder and more severe OSA, obesity levels, sleepiness scores and comorbidity burden.…”

“…In a recent publication in Respirology , the European Sleep Apnoea Database (ESADA) collaborators extend from their previous work 11 to introduce the largest cluster analysis to date of over 23 000 clinical subjects with a diagnosis of OSA, from 34 specialist sleep centre across Europe and Israel 15 . From 20 input variables, this analysis identifies eight phenotypically distinct clusters.…”