Dataset Information

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis.

ABSTRACT:

Background

The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies.

Objectives

This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea.

Methods

An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression.

Results

Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors.

Conclusions

Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies.

SUBMITTER: Bailly S

PROVIDER: S-EPMC4912165 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis.

Bailly Sébastien S Destors Marie M Grillet Yves Y Richard Philippe P Stach Bruno B Vivodtzev Isabelle I Timsit Jean-Francois JF Lévy Patrick P Tamisier Renaud R Pépin Jean-Louis JL

PloS one 20160617 6

<h4>Background</h4>The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies.<h4>Objectives</h4>This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea.<h4>Methods</h4>An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor ex ...[more]

PMID: 27314230

Similar Datasets

Project description:Study objectivesWe investigated the diagnostic accuracy for the identification of obstructive sleep apnea (OSA) and its severity of a noninvasive technology based on image processing (SleepWise).MethodsThis is an observational, prospective study to evaluate the degree of agreement between polysomnography (PSG) and SleepWise. We recruited 56 consecutive subjects with suspected OSA who were referred as outpatients to the Sleep Unit of the Hospital Universitari Germans Trias i Pujol (HUGTiP) from January 2013 to January 2014. All patients underwent laboratory PSG and image processing with SleepWise simultaneously the same night. Both PSG and SleepWise analyses were carried independently and blindly.ResultsWe analyzed 50 of the 56 patients recruited. OSA was diagnosed through PSG in a total of 44 patients (88%) with a median apnea-hypopnea index (AHI) of 25.35 (24.9). According to SleepWise, 45 patients (90%) met the criteria for a diagnosis of OSA, with a median AHI of 22.8 (22.03). An analysis of the ability of PSG and SleepWise to classify patients by severity on the basis of their AHI shows that the two diagnostic systems distribute the different groups similarly. According to PSG, 23 patients (46%) had a diagnosis of severe OSA, 11 patients (22%) moderate OSA, and 10 patients (20%) mild OSA. According to SleepWise, 20, 13, and 12 patients (40%, 26%, and 24%, respectively) had a diagnosis of severe, moderate, and mild OSA respectively. For OSA diagnosis, SleepWise was found to have sensitivity of 100% and specificity of 83% in relation to PSG. The positive predictive value was 97% and the negative predictive value was 100%. The Bland-Altman plot comparing the mean AHI values obtained through PSG and SleepWise shows very good agreement between the two diagnostic techniques, with a bias of -3.85, a standard error of 12.18, and a confidence interval of -0.39 to -7.31.ConclusionsSleepWise was reasonably accurate for noninvasive and automatic diagnosis of OSA in outpatients. SleepWise determined the severity of OSA with high reliability. The current study including simultaneous laboratory PSG and SleepWise processing image is proposed as a reasonable validation standard.

Dataset Information

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis.

Background

Objectives

Methods

Results

Conclusions

Publications

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis.

Similar Datasets

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