Pediatric Adenotonsillectomy Trial for Snoring (PATS): protocol for a randomised controlled trial to evaluate the effect of adenotonsillectomy in treating mild obstructive sleep-disordered breathing.

INTRODUCTION: Mild obstructive sleep-disordered breathing (oSDB), characterised by habitual snoring without frequent apnoeas and hypopnoeas on polysomnography, is prevalent in children and commonly treated with adenotonsillectomy (AT). However, the absence of high-level evidence addressing the role of AT in improving health and behavioural outcomes has contributed to significant geographical variations in care and potential for surgery to be both overused and underused. METHODS AND ANALYSIS: The Pediatric Adenotonsillectomy Trial for Snoring (PATS) is a single-blinded, multicentre randomised controlled trial designed to evaluate the effect of AT in treating mild oSDB. Four hundred sixty eligible children, aged 3.0-12.9 years old, will be randomised to either early adenotonsillectomy or to watchful waiting with supportive care (WWSC) with a 1:1 ratio. The study's coprimary endpoints are (1) change from baseline in executive behaviour relating to self-regulation and organisation skills as measured by the Behavioural Rating Inventory of Executive Function (BRIEF) Global Composite Score (GEC); and (2) change from baseline in vigilance as measured on the Go-No-Go (GNG) signal detection parameter (d-prime). A mixed effects model will be used to compare changes in the BRIEF GEC score and GNG score at 6 and 12 months from baseline between the AT arm and the WWSC arm. ETHICS AND DISSEMINATION: The study protocol was approved by the institutional review board (IRB) at Children's Hospital of Philadelphia (CHOP) on 3 October 2014 (14-0 11 214). The approval of CHOP as the central IRB of record was granted on 29 February 2016. The results will be published in peer-reviewed journals and presented at academic conferences. The data collected from the PATS study will be deposited in a repository (National Sleep Research Resource, sleepdata.org) after completion of the study to maximise use by the scientific community. TRIAL REGISTRATION NUMBER: NCT02562040; Pre-results.

Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource.

STUDY OBJECTIVES: We present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach). METHODS: EEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots. RESULTS: Assessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach. CONCLUSION: Despite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.

Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource.

ABSTRACT: Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.

Motivational Enhancement for Increasing Adherence to CPAP: A Randomized Controlled Trial.

BACKGROUND: Motivational enhancement (ME) shows promise as a means of increasing adherence to CPAP for OSA. METHODS: We performed an open-label, parallel-arm, randomized controlled trial of CPAP only or CPAP + ME, recruiting individuals 45 to 75 years with moderate or severe OSA without marked sleepiness and with either established cardiovascular disease (CVD) or at risk for CVD. All participants received standardized CPAP support from a sleep technologist; those randomly assigned to CPAP + ME also received standardized ME delivered by a psychologist during two appointments and six phone calls over 32 weeks. Mixed-effect models with subject-specific intercepts and slopes were fitted to compare objective CPAP adherence between arms, adjusting for follow-up duration, randomization factors, and device manufacturer. All analyses were intention-to-treat. RESULTS: Overall, 83 participants (n = 42 CPAP only; n = 41 CPAP + ME) contributed 14,273 nights of data for 6 months. Participants were predominantly male (67%) and had a mean ± SD age of 63.9 ± 7.4 years, a BMI of 31.1 ± 5.2 kg/m(2), and an apnea-hypopnea index of 26.2 ± 12.9 events/h. In our fully adjusted model, average nightly adherence for 6 months was 99.0 min/night higher with CPAP + ME compared with CPAP only (P = .003; primary analysis). A subset of 52 participants remained in the study for 12 months; modeling these data yielded a consistent difference in adherence between arms of 97 min/night (P = .006) favoring CPAP + ME. CONCLUSIONS: ME delivered during brief appointments and phone calls resulted in a clinically significant increase in CPAP adherence. This strategy may represent a feasible approach for optimizing management of OSA. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01261390; URL: www.clinicaltrials.gov.

Prevalence and associations of respiratory-related leg movements: the MrOS sleep study.

OBJECTIVES: Obstructive respiratory events often terminate with an associated respiratory-related leg movement (RRLM). Such leg movements are not scored as periodic leg movements (periodic limb movements during sleep, PLMS), although the criteria for distinguishing RRLM from PLMS differ between the American Academy of Sleep Medicine (AASM) and the World Association of Sleep Medicine (WASM)/ International Restless Legs Syndrome Study Group (IRLSSG) scoring manuals. Such LMs may be clinically significant in patients with obstructive sleep apnea (OSA). The prevalence and correlation of RRLM in men with OSA were examined. METHODS: A case-control sample of 575 men was selected from all men with an apnea-hypopnea index (AHI, ≥3% desaturation criteria) ≥ 10 and good data from piezoelectric leg movement sensors at the first in-home sleep study in the MrOS cohort (mean age = 76.8 years). Sleep studies were rescored for RRLMs using five different RRLM definitions varying in both latency of leg movement onset from respiratory event termination and duration of the leg movement. The quartile of RRLM% (the number of RRLM/the number of hypopneas + apneas) was derived. RESULTS: The nonparametric densities of RRLM% were most influenced by alterations in the latency rather than the duration of the LM. The most liberal RRLM definition (latency 0-5 s, duration 0.5-10 s) led to a median RRLM% of 23.4 (interquartile range 12.41, 37.12) in this sample. The average AHI and arousal index increased as the quartile of RRLM% increased, as well as the prevalence of chronic obstructive pulmonary disease (COPD). The prevalence of those with a history of hypertension decreased as RRLM% increased. The non-Caucasian race was associated with lower RRLM%. CONCLUSION: Within an elderly sample with moderate to severe OSA, piezoelectric-defined RRLM% is associated with a number of sleep-related and demographic factors. Further study of the optimal definition, predictors, and consequences of RRLM is warranted.