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Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis
Author(s) -
Vavougios George D.,
Natsios George,
Pastaka Chaido,
Zarogiannis Sotirios G.,
Gourgoulianis Konstantinos I.
Publication year - 2016
Publication title -
journal of sleep research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 117
eISSN - 1365-2869
pISSN - 0962-1105
DOI - 10.1111/jsr.12344
Subject(s) - obstructive sleep apnea , comorbidity , medicine , body mass index , epworth sleepiness scale , sleep apnea , apnea , polysomnography , univariate analysis , physical therapy , multivariate analysis
Summary Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data‐driven approach. Data from 1472 consecutive patient 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 obstructive sleep apnea syndrome without significant comorbidities’; C 1 : ‘moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities’; C 2 : ‘moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke’; D 1 : ‘severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension’; and D 2 : ‘severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index’. Clusters differed significantly in apnea–hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one‐way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome 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 considering obstructive sleep apnea syndrome treatment options.