@article {784, title = {Polysomnography performed in the unattended home versus the attended laboratory setting--Sleep Heart Health Study methodology.}, journal = {Sleep}, volume = {27}, year = {2004}, month = {2004 May 01}, pages = {536-40}, abstract = {

STUDY OBJECTIVE: To compare polysomnographic recordings obtained in the home and laboratory setting.

DESIGN AND SETTING: Multicenter study comparing unsupervised polysomnography performed in the participant{\textquoteright}s home with polysomnography supervised at an academic sleep disorders center, using a randomized sequence of study setting. Sleep Heart Health Study (SHHS) standardized polysomnographic recording and scoring techniques were used for both settings.

PARTICIPANTS: 64 of 76 non-SHHS participants recruited from 7 SHHS field sites who had both a laboratory and home polysomnogram meeting acceptable quality criteria.

MEASUREMENTS AND RESULTS: Median sleep duration was greater in the home than in the laboratory (375 vs 318 minutes, respectively, P < .0001) as was sleep efficiency (86\% vs 82\%, respectively, P < .0024). Very small, but significant increases in percentage of rapid eye movement sleep and decreases in stage 1 sleep were noted in the laboratory. Employing multiple definitions of respiratory disturbance index (RDI), median RDI was similar in both settings (for example, RDI with 3\% desaturation: home 12.4, range 0.6-67; laboratory 9.5, range 0.1-93.4, P = .41). Quartile analysis of laboratory RDI showed moderate agreement with home RDI measurements. Based on the mean of laboratory and home RDI and using a cutpoint of 20, there was a biphasic distribution, with the RDI 3\% above 20 being more common in the recordings performed in the laboratory than in the home and below 20 being more common in the recordings performed in the home than in the laboratory. These differences could not be attributed to quality of recording, age, sex, or body mass index.

CONCLUSIONS: Using SHHS methodology, median RDI was similar in the unattended home and attended laboratory setting with differences of small magnitude in some sleep parameters. Differences in RDI between settings resulted in a rate of disease misclassification that is similar to repeated studies in the same setting.

}, keywords = {Adult, Aged, Body Mass Index, Clinical Laboratory Techniques, Cohort Studies, Health Status, Home Care Services, Humans, Middle Aged, Polysomnography, Sleep Apnea Syndromes, Sleep, REM}, issn = {0161-8105}, doi = {10.1093/sleep/27.3.536}, author = {Iber, Conrad and Redline, Susan and Kaplan Gilpin, Adele M and Quan, Stuart F and Zhang, Lin and Gottlieb, Daniel J and Rapoport, David and Resnick, Helaine E and Sanders, Mark and Smith, Philip} } @article {1161, title = {Sleepiness, quality of life, and sleep maintenance in REM versus non-REM sleep-disordered breathing.}, journal = {Am J Respir Crit Care Med}, volume = {181}, year = {2010}, month = {2010 May 01}, pages = {997-1002}, abstract = {

RATIONALE: The impact of REM-predominant sleep-disordered breathing (SDB) on sleepiness, quality of life (QOL), and sleep maintenance is uncertain.

OBJECTIVE: To evaluate the association of SDB during REM sleep with daytime sleepiness, health-related QOL, and difficulty maintaining sleep, in comparison to their association with SDB during non-REM sleep in a community-based cohort.

METHODS: Cross-sectional analysis of 5,649 Sleep Heart Health Study participants (mean age 62.5 [SD = 10.9], 52.6\% women, 22.6\% ethnic minorities). SDB during REM and non-REM sleep was quantified using polysomnographically derived apnea-hypopnea index in REM (AHI(REM)) and non-REM (AHI(NREM)) sleep. Sleepiness, sleep maintenance, and QOL were respectively quantified using the Epworth Sleepiness Scale (ESS), the Sleep Heart Health Study Sleep Habit Questionnaire, and the physical and mental composites scales of the Medical Outcomes Study Short Form (SF)-36.

MEASUREMENTS AND MAIN RESULTS: AHI(REM) was not associated with the ESS scores or the physical and mental components scales scores of the SF-36 after adjusting for demographics, body mass index, and AHI(NREM) x AHI(REM) was not associated with frequent difficulty maintaining sleep or early awakening from sleep. AHI(NREM) was associated with the ESS score (beta = 0.25; 95\% confidence interval [CI], 0.16 to 0.34) and the physical (beta = -0.12; 95\% CI, -0.42 to -0.01) and mental (beta = -0.20; 95\% CI, -0.20 to -0.01) components scores of the SF-36 adjusting for demographics, body mass index, and AHI(REM).

CONCLUSIONS: In a community-based sample of middle-aged and older adults, REM-predominant SDB is not independently associated with daytime sleepiness, impaired health-related QOL, or self-reported sleep disruption.

}, keywords = {Aged, Disorders of Excessive Somnolence, Female, Humans, Male, Middle Aged, Polysomnography, Quality of Life, Sleep, Sleep Apnea Syndromes, Sleep, REM}, issn = {1535-4970}, doi = {10.1164/rccm.200908-1304OC}, author = {Chami, Hassan A and Baldwin, Carol M and Silverman, Angela and Zhang, Ying and Rapoport, David and Punjabi, Naresh M and Gottlieb, Daniel J} } @article {1542, title = {Probabilistic sleep architecture models in patients with and without sleep apnea.}, journal = {J Sleep Res}, volume = {21}, year = {2012}, month = {2012 Jun}, pages = {330-41}, abstract = {

Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep-wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co-morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non-rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight {\textquoteright}generator{\textquoteright} states of a first-order continuous-time Markov model were inferred from the observed ({\textquoteright}phenotypic{\textquoteright}) distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time-variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi-exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.

}, keywords = {Cohort Studies, Computer Simulation, Humans, Models, Theoretical, Polysomnography, Probability, Sleep Apnea Syndromes, Sleep Stages, Sleep, REM, Time Factors}, issn = {1365-2869}, doi = {10.1111/j.1365-2869.2011.00937.x}, author = {Bianchi, Matt T and Eiseman, Nathaniel A and Cash, Sydney S and Mietus, Joseph and Peng, Chung-Kang and Thomas, Robert J} }