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Introduction Although sleep is critical to maintaining health and quality of life, inadequate sleep duration and/or quality is common. It can be difficult to distinguish sleep problems that may be addressed through adjustments to lifestyle versus issues that may represent a more serious condition requiring medical intervention. SmartSleep Analyzer is a cloud-based questionnaire and scoring algorithm designed to categorize respondents according to likely sleep problems as follows: obstructive sleep apnea (OSA), snoring, trouble falling asleep or staying asleep, delayed sleep phase disorder (DSPD), shift work disorder (SWD), chronic sleep restriction (CSR), or no sleep problem. Primary, secondary, and tertiary categorizations are provided, where applicable. The objective of this study was to validate the questionnaire scoring algorithm categorization/s against a sleep physician assessment. Methods From 2,316 available records, 90 complete questionnaires were randomly selected for this analysis. The questionnaire scoring algorithm categorization was compared against the consensus assessment of three independent sleep physicians who each reviewed the answers to all questions before arriving at a diagnosis. Results The questionnaire respondents (70% female) were aged 42.2±14.5 years, had a mean BMI of 32.0±7.7 kg/m2, and self-reported sleep duration of 6.5±1.4 hours/night. The primary, secondary, or tertiary categorization of the questionnaire scoring algorithm matched the primary consensus categorization of the physicians 90.6% of the time (95% confidence interval (CI): 82.6 to 95.7). When OSA and snoring were grouped, agreement increased to 98.9% (95% CI: 94.0 to 100). In all analyses undertaken, the accuracy of questionnaire scoring algorithm against the physicians exceeded the accuracy of the physicians when compared to each other. Conclusion These results demonstrate that our questionnaire and scoring algorithm performs well in identifying sleep problems that may impact adult respondents, using physician-review as the comparison standard. Support Philips

Introduction There is no universal process for selecting mask style, size, and fit, and there is considerable variance in clinician and patient mask preference and patient anatomy. Poor mask fit may negatively affect adherence. A three-dimensional (3D) facial scanner and proprietary analytical software were developed to bring efficiencies to mask selection. This study explored the impact of that system on initial mask success compared to standard practice. Methods This was an open-label, randomized-controlled study. Participants provided written informed consent. 3D Scanner Arm (3DA): Participants answered questions about sleeping habits then had 3D facial images taken. Proprietary software recommended a hierarchy of up to four Philips Respironics masks and sizes. Traditional Fitting Arm (TFA): A designated clinician selected and fit masks using their standard methods. Mask selection was assessed by applying therapy and soliciting patient and clinician feedback. Mask refits and adherence were tracked through 90 days. Five sleep centers recruited 115 participants into the 3DA (61 males, 51.1±13.4 years, BMI 35.2±7.0, diagnostic AHI 26.2±21.9) and 123 into the TFA (79 males, 51.1±11.9 years, BMI 35±7.9, diagnostic AHI 26.9±22.6). Results A significantly higher percentage of 3DA patients required only one mask fitting (with no refits) compared to TFA during the initial setup (89.6% vs. 54.5%, p Conclusion The 3D scanner system was successful in mask selection with lower mask leak and greater patient satisfaction and confidence. This tool may bring about operational efficiencies to the mask selection process. Support This study was sponsored by Philips Respironics

STUDY OBJECTIVES To compare a positive airway pressure (PAP) device's detection of respiratory events and airway status during device-detected apneas with events scored on simultaneous polysomnography (PSG). DESIGN Prospective PSGs of patients with sleep apnea using a new-generation PAP device. SETTINGS Four clinical and academic sleep centers. PATIENTS Forty-five patients with obstructive sleep apnea (OSA) and complex sleep apnea (Comp SA) performed a PSG on PAP levels adjusted to induce respiratory events. INTERVENTIONS None. MEASUREMENTS AND RESULTS PAP device data identifying the type of respiratory event and whether the airway during a device-detected apnea was open or obstructed were compared to time-synced, manually scored respiratory events on simultaneous PSG recording. Intraclass correlation coefficients between device-detected and PSG scored events were 0.854 for apnea-hypopnea index (AHI), 0.783 for apnea index, 0.252 for hypopnea index, and 0.098 for respiratory event-related arousals index. At a device AHI (AHIFlow) of 10 events/h, area under the receiver operating characteristic curve was 0.98, with sensitivity 0.92 and specificity 0.84. AHIFlow tended to overestimate AHI on PSG at values less than 10 events/h. The device detected that the airway was obstructed in 87.4% of manually scored obstructive apneas. Of the device-detected apneas with clear airway, a minority (15.8%) were manually scored as obstructive apneas. CONCLUSIONS A device-detected apnea-hypopnea index (AHIFlow) < 10 events/h on a positive airway pressure device is strong evidence of good treatment efficacy. Device-detected airway status agrees closely with the presumed airway status during polysomnography scored events, but should not be equated with a specific type of respiratory event.