The proportion of sleep apnea syndrome (SAS) in the general adult population that goes undiagnosed was estimated from a sample of 4,925 employed adults. Questionnaire data on doctor-diagnosed sleep apnea were followed up to ascertain the prevalence of diagnosed sleep apnea. In-laboratory polysomnography on a subset of 1,090 participants was used to estimate screen-detected sleep apnea. In this population, without obvious barriers to health care for sleep disorders, we estimate that 93% of women and 82% of men with moderate to severe SAS have not been clinically diagnosed. These findings provide a baseline for assessing health care resource needs for sleep apnea.
"most debilitating medical conditions, including hypertension, cardiovascular disease, coronary artery disease, insulinresistance diabetes, depression, and sleepiness-related accidents . However despite being a common disease, OSAS is under recognized by most primary care physicians . In most laboratories, patients with sleep apnea are evaluated for an entire diagnostic night followed by a continuous positive airway pressure (CPAP) titration night. "
"In addition , the screening process removes patients from their normal sleeping environment , preventing repeatable unbiased studies . It is estimated that up to 90% of individuals with OSA are undiagnosed and untreated ( Young et al . , 1997 ) . Screening of OSA is particularly poor in developing countries , where the resources required for conventional screening and diagnosis are often unavailable . While conventional screening of OSA is expen - sive , some treatment for those diagnosed with the condition can be relatively cheap ; oral appliances , which attempt to enlarge"
[Show abstract][Hide abstract] ABSTRACT: Obstructive sleep apnea (OSA) is a disorder characterized by repeated pauses in breathing during sleep, which leads to deoxygenation and voiced chokes at the end of each episode. OSA is associated by daytime sleepiness and an increased risk of serious conditions such as cardiovascular disease, diabetes, and stroke. Between 2 and 7% of the adult population globally has OSA, but it is estimated that up to 90% of those are undiagnosed and untreated. Diagnosis of OSA requires expensive and cumbersome screening. Audio offers a potential non-contact alternative, particularly with the ubiquity of excellent signal processing on every phone. Previous studies have focused on the classification of snoring and apneic chokes. However, such approaches require accurate identification of events. This leads to limited accuracy and small study populations. In this work, we propose an alternative approach which uses multiscale entropy (MSE) coefficients presented to a classifier to identify disorder in vocal patterns indicative of sleep apnea. A database of 858 patients was used, the largest reported in this domain. Apneic choke, snore, and noise events encoded with speech analysis features were input into a linear classifier. Coefficients of MSE derived from the first 4 h of each recording were used to train and test a random forest to classify patients as apneic or not. Standard speech analysis approaches for event classification achieved an out-of-sample accuracy (Ac) of 76.9% with a sensitivity (Se) of 29.2% and a specificity (Sp) of 88.7% but high variance. For OSA severity classification, MSE provided an out-of-sample Ac of 79.9%, Se of 66.0%, and Sp = 88.8%. Including demographic information improved the MSE-based classification performance to Ac = 80.5%, Se = 69.2%, and Sp = 87.9%. These results indicate that audio recordings could be used in screening for OSA, but are generally under-sensitive.
Frontiers in Bioengineering and Biotechnology 08/2015; 3. DOI:10.3389/fbioe.2015.00114
"Obstructive sleep apnoea (OSA) is a common disease, with a prevalence of 3–5% symptomatic and 24–26% asymptomatic patients   . OSA is caused by episodic upper airway obstruction which occurs during sleep. "
[Show abstract][Hide abstract] ABSTRACT: Purpose: The severity of obstructive sleep apnoea (OSA) ranges from mild or moderate to severe sleep apnoea. However, there is no information available on the clinical characteristics associated with cases involving more than 100 events per hour. This is a preliminary report and our goal was to characterise the demographics and sleep characteristics of patients with Extreme OSA and compare with patients with sleep apnoea of lesser severity. We hypothesised that patients with Extreme OSA (AHI4100) is associated with an increased comorbidities and/or risk factors.
Methods: We carried out a case-control study on male patients with OSA who were seen in a private hospital in Lima, Peru between 2006 and 2012. Cases were identified if their apnoea/ hypopnea index (AHI) was higher than 100 (Extreme OSA), and four controls were selected per case: two with 15–29 AHI and two with 30–50 AHI, matched according to case diagnosis dates. We evaluated demographic, past medical history, and oxygen saturation variables
Results: We identified 19 cases that were matched with 54 controls. In the multivariate model, only arterial hypertension, neck circumference, age, and over 10% in SatO2Hbr90% in total sleep time (T90) were associated with Extreme OSA. Arterial hypertension had an OR¼6.31 (CI95%: 1.71–23.23) of Extreme OSA. Each 5-cm increment in neck circumference was associated with an increase of OR¼4.34 (CI95%: 1.32–14.33), while T90410% had an OR¼19.68 (CI95%: 4.33–
89.49). Age had a marginal relevance (OR¼0.95; CI95%: 0.92–0.99)
Conclusion: Our results suggest that arterial hypertension, neck circumference, and over 10% SatO2Hbr90% in total sleep time were associated with a higher probability of Extreme OSA. We recommend investigators to study this population of Extreme OSA looking for an early diagnosis and the identification of prognostic factors in comparison withmoderate to severe levels.
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