Faculty Bibliography

Obstructive sleep apnea (OSA) is a highly prevalent condition which remains under-diagnosed and under-treated. Untreated OSA is associated with several chronic medical conditions, a reduction in quality of life and increases in health care costs. Therefore, early identification of undiagnosed cases is important. Implementation of a screening measure in a primary care environment for populations at high-risk for OSA could improve patient outcomes and reduce the health care burden of untreated OSA.

BACKGROUND:Obstructive sleep apnea is a prevalent yet underdiagnosed condition associated with cardiovascular morbidity and mortality. Home sleep testing offers an efficient means for diagnosing obstructive sleep apnea but has been deployed primarily in clinical samples with a high pretest probability. The present study sought to assess whether obstructive sleep apnea can be diagnosed with home sleep testing in a nonreferred sample without involvement of a sleep medicine specialist. METHODS:A study of community-based adults with untreated obstructive sleep apnea was undertaken. Misclassification of disease severity according to home sleep testing with and without involvement of a sleep medicine specialist was assessed, and agreement was characterized using scatter plots, Pearson's correlation coefficient, Bland-Altman analysis, and the κ statistic. Analyses were also conducted to assess whether any observed differences varied as a function of pretest probability of obstructive sleep apnea or subjective sleepiness. RESULTS:The sample consisted of 191 subjects, with more than half (56.5%) having obstructive sleep apnea. Without involvement of a sleep medicine specialist, obstructive sleep apnea was not identified in only 5.8% of the sample. Analyses comparing the categorical assessment of disease severity with and without a sleep medicine specialist showed that in total, 32 subjects (16.8%) were misclassified. Agreement in the disease severity with and without a sleep medicine specialist was not influenced by the pretest probability or daytime sleep tendency. CONCLUSION:Obstructive sleep apnea can be reliably identified with home sleep testing in a nonreferred sample, irrespective of the pretest probability of the disease.

An update of the 2012 systematic review and meta-analyses were performed and a modified-GRADE approach was used to update the recommendation for the use of adaptive servo-ventilation (ASV) for the treatment of central sleep apnea syndrome (CSAS) related to congestive heart failure (CHF). Meta-analyses demonstrated an improvement in LVEF and a normalization of AHI in all patients. Analyses also demonstrated an increased risk of cardiac mortality in patients with an LVEF of ≤ 45% and moderate or severe CSA predominant sleep-disordered breathing. These data support a Standard level recommendation against the use of ASV to treat CHF-associated CSAS in patients with an LVEF of ≤ 45% and moderate or severe CSAS, and an Option level recommendation for the use of ASV in the treatment CHF-associated CSAS in patients with an LVEF > 45% or mild CHF-related CSAS. The application of these recommendations is limited to the target patient populations; the ultimate judgment regarding propriety of any specific care must be made by the clinician.

STUDY OBJECTIVES/OBJECTIVE:Manual scoring of polysomnograms is a time-consuming and tedious process. To expedite the scoring of polysomnograms, several computerized algorithms for automated scoring have been developed. The overarching goal of this study was to determine the validity of the Somnolyzer system, an automated system for scoring polysomnograms. DESIGN/METHODS:The analysis sample comprised of 97 sleep studies. Each polysomnogram was manually scored by certified technologists from four sleep laboratories and concurrently subjected to automated scoring by the Somnolyzer system. Agreement between manual and automated scoring was examined. Sleep staging and scoring of disordered breathing events was conducted using the 2007 American Academy of Sleep Medicine criteria. SETTING/METHODS:Clinical sleep laboratories. MEASUREMENTS AND RESULTS/RESULTS:A high degree of agreement was noted between manual and automated scoring of the apnea-hypopnea index (AHI). The average correlation between the manually scored AHI across the four clinical sites was 0.92 (95% confidence interval: 0.90-0.93). Similarly, the average correlation between the manual and Somnolyzer-scored AHI values was 0.93 (95% confidence interval: 0.91-0.96). Thus, interscorer correlation between the manually scored results was no different than that derived from manual and automated scoring. Substantial concordance in the arousal index, total sleep time, and sleep efficiency between manual and automated scoring was also observed. In contrast, differences were noted between manually and automated scored percentages of sleep stages N1, N2, and N3. CONCLUSION/CONCLUSIONS:Automated analysis of polysomnograms using the Somnolyzer system provides results that are comparable to manual scoring for commonly used metrics in sleep medicine. Although differences exist between manual versus automated scoring for specific sleep stages, the level of agreement between manual and automated scoring is not significantly different than that between any two human scorers. In light of the burden associated with manual scoring, automated scoring platforms provide a viable complement of tools in the diagnostic armamentarium of sleep medicine.

Obstructive sleep apnea (OSA) is a prevalent disorder associated with a multitude of adverse outcomes when left untreated. There is significant heterogeneity in the evaluation and management of OSA resulting in variation in cost and outcomes. Thus, the goal for developing these measures was to have a way to evaluate the outcomes and reliability of the processes involved with the standard care approaches used in the diagnosis and management of OSA. The OSA quality care measures presented here focus on both outcomes and processes. The AASM commissioned the Adult OSA Quality Measures Workgroup to develop quality care measures aimed at optimizing care for adult patients with OSA. These quality care measures developed by the Adult OSA Quality Measures Workgroup are an extension of the original Centers for Medicare & Medicaid Services (CMS) approved Physician Quality Reporting System (PQRS) measures group for OSA. The measures are based on the available scientific evidence, focus on public safety, and strive to improve quality of life and cardiovascular outcomes for individual OSA patients. The three outcomes that were selected were as follows: (1) improve disease detection and categorization; (2) improve quality of life; and (3) reduce cardiovascular risk. After selecting these relevant outcomes, a total of ten process measures were chosen that could be applied and assessed for the purpose of accomplishing these outcomes. In the future, the measures described in this document may be reported through the PQRS in addition to, or as a replacement for, the current OSA measures group. The overall objective for the development of these measures is that implementation of these quality measures will result in improved patient outcomes, reduce the public health burden of OSA, and provide a measurable standard for evaluating and managing OSA.

BACKGROUND:The advent of home sleep testing has allowed for the development of an ambulatory care model for OSA that most health-care providers can easily deploy. Although automated algorithms that accompany home sleep monitors can identify and classify disordered breathing events, it is unclear whether manual scoring followed by expert review of home sleep recordings is of any value. Thus, this study examined the agreement between automated and manual scoring of home sleep recordings. METHODS:Two type 3 monitors (ApneaLink Plus [ResMed] and Embletta [Embla Systems]) were examined in distinct study samples. Data from manual and automated scoring were available for 200 subjects. Two thresholds for oxygen desaturation (≥ 3% and ≥ 4%) were used to define disordered breathing events. Agreement between manual and automated scoring was examined using Pearson correlation coefficients and Bland-Altman analyses. RESULTS:Automated scoring consistently underscored disordered breathing events compared with manual scoring for both sleep monitors irrespective of whether a ≥ 3% or ≥ 4% oxygen desaturation threshold was used to define the apnea-hypopnea index (AHI). For the ApneaLink Plus monitor, Bland-Altman analyses revealed an average AHI difference between manual and automated scoring of 6.1 (95% CI, 4.9-7.3) and 4.6 (95% CI, 3.5-5.6) events/h for the ≥ 3% and ≥ 4% oxygen desaturation thresholds, respectively. Similarly for the Embletta monitor, the average difference between manual and automated scoring was 5.3 (95% CI, 3.2-7.3) and 8.4 (95% CI, 7.2-9.6) events/h, respectively. CONCLUSIONS:Although agreement between automated and manual scoring of home sleep recordings varies based on the device used, modest agreement was observed between the two approaches. However, manual review of home sleep test recordings can decrease the misclassification of OSA severity, particularly for those with mild disease. TRIAL REGISTRY/BACKGROUND:ClinicalTrials.gov; No.: NCT01503164; www.clinicaltrials.gov.

Both obstructive sleep apnea (OSA) and type 2 diabetes mellitus are commonly seen in older adults. Over the last decade, there has been increasing recognition that OSA is highly prevalent in persons with type 2 diabetes and related metabolic conditions such as insulin resistance and glucose intolerance. Intermittent hypoxemia and recurrent arousals in OSA trigger a repertoire of pathophysiological events, which can in turn alter glucose homeostasis and possibly increase the risk for type 2 diabetes. Conversely, there is evidence that type 2 diabetes may alter the progression and expression of sleep-disordered breathing.

PRIMARY OBJECTIVE: To characterize sleep architecture and self-reported sleep quality, fatigue and daytime sleepiness in individuals with TBI. Possible relationships between sleep architecture and self-reported sleep quality, fatigue and daytime sleepiness were examined. METHODS: Forty-four community-dwelling adults with TBI completed the Pittsburgh Sleep Quality Index (PSQI), Multidimensional Assessment of Fatigue (MAF) and Epworth Sleepiness Scale (ESS). They underwent two nights of in-laboratory nocturnal polysomnography (NPSG). Pearson product-moment correlation coefficients and hierarchical linear regression was used to analyse the data. RESULTS: Based on the PSQI cut-off score of >/= 10, 22 participants were characterized as poor sleepers. Twenty-seven participants met criteria for clinically significant fatigue as measured by the GFI of the MAF. Fourteen participants met criteria for excessive daytime sleepiness as measured by the ESS. Poor sleep quality was associated with poor sleep efficiency, short duration of stage 2 sleep and long duration of rapid eye movement sleep. There was little-to-no association between high levels of fatigue or daytime sleepiness with NPSG sleep parameters. CONCLUSIONS: A high proportion of the sample endorsed poor sleep quality, fatigue and daytime sleepiness. Those who reported poorer sleep quality evidenced a shorter proportion of time spent in stage 2 sleep. These findings suggest that disruptions in stage 2 sleep might underlie the symptoms of sleep disturbance experienced following TBI.

PURPOSE/OBJECTIVE:Portable sleep monitors may offer a convenient method to expand detection of obstructive sleep apnea (OSA), yet few studies have evaluated this technology in vulnerable populations. We therefore aimed to assess the feasibility and acceptability of portable sleep monitors for detection of OSA in a prediabetic, urban minority population. METHODS:We recruited a convenience sample of participants at their 12-month follow-up for a community-partnered, peer-led lifestyle intervention aimed to prevent diabetes in prediabetic and overweight patients in this prospective mixed-methods pilot study. All participants wore portable sleep monitors overnight at home. We qualitatively explored perceptions about OSA and portable monitors in a subset of participants. RESULTS:We tested 72 people, predominantly non-White, female, Spanish speaking, uninsured, and of low income. Use of portable sleep monitors was feasible: 100% of the monitors were returned and all participants received results. We detected OSA in 49% (defined as an Apnea-Hypopnea Index [AHI] >5) and moderate-severe OSA in 14% (AHI >15) requiring treatment in 14%. In 21 qualitative interviews, participants supported increased use of portable sleep monitors in their community, were appropriately concerned that OSA could cause progression to diabetes, and thought weight loss could prevent or improve OSA. CONCLUSIONS:Portable sleep monitors may represent a feasible method for detecting OSA in high-risk urban minority populations.