Young, T., Evans, L., Finn, L. & Palta, M. Estimation of the clinicallcy diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep 20, 705-706
Department of Preventive Medicine, University of Wisconsin-Madison 53705, USA. Sleep
(Impact Factor: 4.59).
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.
Available from: Xun Zhang
- "Sleep disorders are common in the adult general population. Research showed that insomnia concerns one adult out of five for the general population of the US, France, Germany, Italy, Spain, the UK and Japan (Leger et al., 2007; Leger and Poursain, 2005) and sleep apnea 5–10% in Spain (Marin et al., 1997; Young et al., 1997). Many drivers are also patients affected by sleep disorders who must face altered driving habits (Sagaspe et al., 2007). "
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ABSTRACT: The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a "silent killer." Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006-2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.
Available from: Andrew Vakulin
- "Obstructive sleep apnea (OSA) is a common sleep disorder affecting up to 10–30% of the middle-aged population with recent evidence suggesting a rise in the prevalence in line with increased obesity (Bearpark et al., 1995;Young et al., 1997b;Tufik et al., 2010;Heinzer et al., 2015). OSA is characterized by repeated cessation and/or reduction of air flow due to upper airway narrowing during sleep leading to intermittent sleep fragmentation and hypoxemia (Eckert and Malhotra, 2008). "
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To improve identification of obstructive sleep apnea (OSA) patients at risk of driving impairment, this study explored predictors of driving performance impairment in untreated OSA patients using clinical PSG metrics, sleepiness questionnaires and quantitative EEG markers from routine sleep studies.
Seventy-six OSA patients completed sleepiness questionnaires and driving simulator tests in the evening of their diagnostic sleep study. All sleep EEGs were subjected to quantitative power spectral analysis. Correlation and multivariate linear regression were used to identify the strongest predictors of driving simulator performance.
Absolute EEG spectral power across all frequencies (0.5-32Hz) throughout the entire sleep period and separately in REM and NREM sleep, (r range 0.239-0.473, all p<0.05), as well as sleep onset latency (r=0.273, p<0.017) positively correlated with driving simulator steering deviation. Regression models revealed that amongst clinical and qEEG variables, the significant predictors of worse steering deviation were greater total EEG power during NREM and REM sleep, greater beta EEG power in NREM and greater delta EEG power in REM (range of variance explained 5-17%, t range 2.29-4.0, all p<0.05) and sleep onset latency (range of variance explained 4-9%, t range 2.15-2.5, all p<0.05).
In OSA patients, increased EEG power, especially in the faster frequency (beta) range during NREM sleep and slower frequency (delta) range in REM sleep were associated with worse driving performance, while no relationships were observed with clinical metrics e.g. apnea, arousal or oxygen indices.
Quantitative EEG analysis in OSA may provide useful markers of driving impairment risk. Future studies are necessary to confirm these findings and assess the clinical significance of quantitative EEG as predictors of driving impairment in OSA.
Available from: Lamia Shaaban
- "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. "
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ABSTRACT: Split-night polysomnography was introduced to obtain diagnosis and determine an effective CPAP on a single night that would be convenient and cost effective.
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