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Purpose: Home sleep apnea tests (HSATs) are an alternative to attended polysomnograms (PSGs) when the pre-test probability for moderate to severe OSA is high. However, insurers often mandate use anytime OSA is suspected regardless of the pre-test probability. Our objective was to determine the ability of HSATs to rule in OSA when the pre-test probability of an apnea hypopnea index (AHI) in the moderate to severe range is low., Methods: Patients who underwent HSATs were characterized as low or high pre-test probability based on the presence of two symptoms of the STOP instrument plus either BMI > 35 or male gender. The odds of HSAT diagnostic for OSA dependent on pre-test probability was calculated. Stepwise selection determined predictors of non-diagnostic HSAT. As PSG is performed after HSATs that do not confirm OSA, false negative results were assessed., Results: Among 196 individuals, pre-test probability was low in 74 (38%) and high in 122 (62%). A lower percentage of individuals with a low versus high pre-test probability for moderate to severe OSA had HSAT results that confirmed OSA (61 versus 84%, p = 0.0002) resulting in an odds ratio (OR) of 0.29 for confirmatory HSAT in the low pre-test probability group (95% CI [0.146, 0.563]). Multivariate logistic regression demonstrated that age ≤ 50 (OR 3.10 [1.24-7.73]), female gender (OR 3.58[1.50-8.66]), non-enlarged neck circumference (OR 11.50 [2.50-52.93]), and the absence of loud snoring (OR 3.47 [1.30-9.25]) best predicted non-diagnostic HSAT. OSA was diagnosed by PSG in 54% of individuals with negative HSAT which was similar in both pre-test probability groups., Conclusion: HSATs should be reserved for individuals with high pre-test probability for moderate to severe disease as opposed to any individual with suspected OSA.

Abstract: Consumer sleep technologies (CSTs) are widespread applications and devices that purport to measure and even improve sleep. Sleep clinicians may frequently encounter CST in practice and, despite lack of validation against gold standard polysomnography, familiarity with these devices has become a patient expectation. This American Academy of Sleep Medicine position statement details the disadvantages and potential benefits of CSTs and provides guidance when approaching patient-generated health data from CSTs in a clinical setting. Given the lack of validation and United States Food and Drug Administration (FDA) clearance, CSTs cannot be utilized for the diagnosis and/or treatment of sleep disorders at this time. However, CSTs may be utilized to enhance the patient-clinician interaction when presented in the context of an appropriate clinical evaluation. The ubiquitous nature of CSTs may further sleep research and practice. However, future validation, access to raw data and algorithms, and FDA oversight are needed., (© 2018 American Academy of Sleep Medicine.)

Objective: To demonstrate that commercial activity monitoring devices (CAMDs) are practical for monitoring resident sleep while on call. Studies that have directly monitored resident sleep are limited, likely owing to both cost and difficulty in study interpretation. The advent of wearable CAMDs that estimate sleep presents the opportunity to more readily evaluate resident sleep in physically active settings and "home call," a coverage arrangement familiar to urology programs., Methods: Twelve urology residents were outfitted with Fitbit Flex devices during "home call" for a total of 57 (out of 64, or 89%) call or post-call night pairs. Residents were surveyed with the Stanford Sleepiness Scale (SSS), a single-question alertness survey. Time in bed (TIB) was "time to bed" to "rise for day." Fitbit accelerometers register activity as follows: (1) not moving; (2) minimal movement or restless; or (3) above threshold for accelerometer to register steps. Total sleep time (TST) was the number of minutes in level 1 activity during TIB. Sleep efficiency (SE) was defined as TST divided by TIB., Results: While on call, 10 responding (of 12 available, 83%) residents on average reported TIB as 347 minutes, TST as 165 minutes, and had an SE of 47%. Interestingly, SSS responses did not correlate with sleep parameters. Post-call sleep demonstrated increases in TIB, SE, and TST (+23%, +15%, and +44%, respectively) while sleepiness was reduced by 22%., Conclusion: We demonstrate that urologic residents can consistently wear CAMDs while on home call. SSS did not correlate with Fitbit-estimated sleep duration. Further study with such devices may enhance sleep deprivation recognition to improve resident sleep., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Study Objective: Esophageal manometry (Pes) is the gold standard to detect repetitive episodes of increased respiratory effort followed by arousal (RERAs). Because RERAs are not included in the apnea-hypopnea index (AHI), we often refer patients with symptoms of sleep disordered breathing (SDB) and AHI < 5 for a second polysomnogram (PSG) with Pes. Often, the second PSG will demonstrate AHI > 5, confirming a diagnosis of OSA. We speculate that in most cases of suspected SDB, Pes does not add further diagnostic data and that night-to-night variability in OSA severity results in a first false-negative study., Methods: We conducted a retrospective review of PSGs between 2008 and 2012 in adults with initial PSG negative for OSA followed by a second study (with or without Pes) within 6 mo., Results: Of 125 studies that met inclusion criteria, a second study was completed with Pes in 105 subjects. SDB was diagnosed in 73 subjects (68.5%) completing a second PSG with Pes: 49 (46.7%) received a diagnosis based on AHI, and 24 (22.8%) received a diagnosis based on Pes (p = 0.003). There were no statistically significant differences in the mean AHI change between the two PSGs in subjects who completed the second study with or without Pes., Conclusions: In patients with symptoms of SDB and initial PSG with AHI < 5, the majority met criteria for OSA on second PSG by AHI without additional information added by Pes. Because Pes is not widely available and is somewhat invasive, a repeat study without Pes may be sufficient to diagnose SDB., (© 2015 American Academy of Sleep Medicine.)

Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during polysomnography (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level.