UNASSIGNED: Phenotyping @entity27 's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of @entity27 @entity1 employing a data-driven approach. Data from 1472 consecutive @entity1 records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild @entity27 without significant comorbidities'; C1 : 'moderate @entity27 , @entity28 , without significant comorbidities'; C2 : 'moderate @entity27 with severe comorbidity, @entity28 and the exclusive inclusion of @entity64 '; D1 : 'severe @entity27 and @entity28 without comorbidity and a 33.8% prevalence of @entity101 '; and D2 : 'severe @entity27 with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in @entity27 index, @entity26 desaturation index; arousal index; age, body mass index, minimum @entity26 saturation and daytime @entity26 saturation (one-way analysis of variance P  <  0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime @entity26 saturation and @entity101 were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of @entity27 and its comorbidities were identified. Mapping the heterogeneity of the @entity27 may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when @entity27 treatment options.

Phenotypes of comorbidity in OSAS XXXX : combining categorical principal component analysis with cluster analysis.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity64 :: ('MESH:D020521', 'Disease') :: ['stroke'] @entity101 :: ('MESH:D006973', 'Disease') :: ['hypertension'] @entity28 :: ('MESH:D009765', 'Disease') :: ['obesity'] @entity26 :: ('MESH:D010100', 'Chemical') :: ['oxygen'] @entity27 :: ('MESH:D020181', 'Disease') :: ['apnea-hypopnea', 'considering obstructive sleep apnea syndrome', 'obstructive sleep apnea syndrome', 'comorbidity in obstructive sleep apnea syndrome']
@entity1 :: (9606,Species) :: ['patients']
