RATIONALE:Advanced polysomnographic (PSG) metrics reflecting the physiological causes and consequences of sleep apnea may enable precision medicine in research settings, but their feasibility in routine clinical practice has yet to be demonstrated. OBJECTIVE:Assess (1) the generalizability of PSG metrics from research to clinical cohort, and (2) their associations with a broad range of comorbid diseases, many of which have not been previously examined. METHODS:PSG metrics including endotypes (eg, loop gain) and physiological burdens (eg, hypoxic burden) were estimated from diagnostic polysomnographs of 6,427 participants at Mass General Brigham (MGB; Boston, MA). Comorbid conditions analyzed from MGB's medical record system included 9 representative cardio-metabolic and respiratory diseases, as well as 408 prevalent diseases. Associations were assessed using modified Poisson and LASSO regression. RESULTS:The sample included 62% females, age: 52.9 ± 16.8 years, and apnea-hypopnea index (AHI) 21.4 ± 15.9 events/hr. Associations between endotypes and demographics/obesity-related factors were consistent with prior observational studies (median difference in β = 0.03SD). After adjusting for AHI, older age was associated with lower heart rate (-0.40SD) and arousal burdens (-0.23SD), while higher BMI was associated with increased hypoxic burden (0.25SD). Having demonstrated that there is reasonable concordance with published data, our subsequent analysis identified distinct and clinically meaningful associations between advanced PSG metrics and comorbid conditions. Specifically, elevated loop gain, ventilatory burden, and hypoxic burden were associated with hypertension, diabetes, and renal failure; increased ventilatory instability was associated with cardiovascular disease; and reduced collapsibility and ventilatory instability with chronic airway obstruction. Even after LASSO-based selection, no single PSG metric consistently predicted risk across all comorbidities; ventilatory instability showed the most associations among endotypic traits, and heart rate burden among physiological burdens, underscoring the heterogeneity of OSA pathophysiology. CONCLUSIONS:Phenome-wide analyses of a large clinical cohort demonstrate the real-world feasibility and clinical relevance of extracting advanced PSG metrics, supporting their potential to identify personalized, mechanism-specific intervention targets for sleep apnea.

Poor sleep and sedentary behavior patterns increase the risk of chronic diseases and negatively impact an individual's health and quality of life. Large-scale surveillance studies can unobtrusively measure free-living physical activities, sedentary behaviors, and sleep using wearable sensors; however, many human activity recognition algorithms cannot reliably detect activities in true free-living settings because they are trained on data collected in a controlled, lab setting. We describe the data collection protocol and present the first release of a multimodal, multi-sensor-site dataset (PAAWS R1). The PAAWS R1 release includes ~4 hours of semi-naturalistic activities from 252 individuals and ~7 days of 24-hour, free-living activities from 20 adults. We have annotated waking day activities using video to provide second-by-second, ground-truth labels capturing short, quickly changing bouts of activity with realistic activity transitions. Additionally, we have labeled up to two nights of sleep stages from PSG data collected during some nights of the free-living protocol. The PAAWS dataset enables researchers to directly compare activity recognition algorithms on the same participants' data across multiple collection protocols and days of free-living behaviors, encouraging convergence towards robust algorithms that could aid health research and drive novel mobile computing interventions and applications.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Objective: The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes. Approach: We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results: The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26,808 subjects and 31,166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research. The NIH Data Commons (or Commons) is an ambitious vision for a shared virtual space to allow digital objects to be stored and computed upon by the scientific community. The Commons would allow investigators to find, manage, share, use and reuse data, software, metadata and workflows. It imagines an ecosystem that makes digital objects Findable, Accessible, Interoperable and Reusable (FAIR). Four components are considered integral parts of the Commons: a computing resource for accessing and processing of digital objects; a "digital object compliance model" that describes the properties of digital objects that enable them to be FAIR; datasets that adhere to the digital object compliance model; and software and services to facilitate access to and use of data. This paper describes the contributions of NSRR along several aspects of the Commons vision: metadata for sleep research digital objects; a collection of annotated sleep data sets; and interfaces and tools for accessing and analyzing such data. More importantly, the NSRR provides the design of a functional architecture for implementing a Sleep Data Commons. The NSRR also reveals complexities and challenges involved in making clinical sleep data conform to the FAIR principles. Future directions: Shared resources offered by emerging resources such as cloud instances provide promising platforms for the Data Commons. However, simply expanding storage or adding compute power may not allow us to cope with the rapidly expanding volume and increasing complexity of biomedical data. Concurrent efforts must be spent to address digital object organization challenges. To make our approach future-proof, we need to continue advancing research in data representation and interfaces for human-data interaction. A possible next phase of NSRR is the creation of a universal self-descriptive sequential data format. The idea is to break large, unstructured, sequential data files into minimal, semantically meaningful, fragments. Such fragments can be indexed, assembled, retrieved, rendered, or repackaged on-the-fly, for multitudes of application scenarios. Data points in such a fragment will be locally embedded with relevant metadata labels, governed by terminology and ontology. Potential benefits of such an approach may include precise levels of data access, increased analysis readiness with on-the-fly data conversion, multi-level data discovery and support for effective web-based visualization of contents in large sequential files.

Sleep-disordered breathing (SDB) refers to a group of disorders characterized by abnormal respiratory patterns or abnormal gas exchange during sleep. The most common type of SDB, especially among young obese women, is obstructive sleep apnea. SDB has clearly been linked to poor sleep and impaired daytime function, but there are also data linking SDB to other health outcomes, principally cardiovascular and metabolic disease. SDB symptoms are common in pregnancy, and pregnancy itself has been associated with an increase in the prevalence of SDB symptoms. Although a link between SDB and adverse pregnancy outcomes appears to be biologically plausible, data exploring this relationship are only now emerging, and large prospective studies in which the authors use objective measures of sleep are lacking. Until we know more about the epidemiology and the impact of SDB in pregnancy, screening efforts for SDB in pregnancy should be focused on identifying very symptomatic patients because treatment of these individuals often leads to improved sleep quality and daytime functioning.

STUDY OBJECTIVES:Type 3 home sleep apnea tests may underestimate the apnea-hypopnea index (AHI) due to overestimation of total sleep time (TST). We aimed to evaluate the effect of manual editing of the total recording time (TRT) on the TST and AHI. METHODS:Thirty 15-channel in-home polysomnography studies (AHI 0 to 30 events/h) scored using American Academy of Sleep Medicine criteria were rescored by two blinded polysomnologists after data from electroencephalogram, electrooculogram, and electromyogram were masked. In method 1, periods of probable wakefulness and artifact were manually edited and removed from analysis. Method 2 identified TST as the TRT without manual editing. Paired t-tests were used to compare the TST and AHI between these methods. Sensitivity and specificity of each method were calculated for gold standard AHI cutoffs of ≥ 5 and ≥ 15 events/h. RESULTS:TST (mean [standard deviation, SD]) by polysomnography, method 1, and method 2 was 366.0 (70.1), 447.1 (59.0), and 542 (61.9) min, respectively. The corresponding AHI was 12.5 (8.2), 10.8 (7.0), and 9.1 (6.1) events/h, respectively. Compared to polysomnography, both alternative methods overestimated the TST (method 1: mean difference [SD] 81.1 [56.1] min, method 2: 176.0 [89.7] min; both p < 0.001) and underestimated the AHI (method 1: mean difference [SD] -1.6 [3.3], method 2: -3.3 [3.9]; both p < 0.001). The sensitivity was 100% and 70.0% for method 1, and 91.3% and 40.0% for method 2 for identifying sleep-disordered breathing using AHI cutoffs of ≥ 5 and ≥ 15 events/h, respectively. CONCLUSIONS:Manual editing of TRT reduces the overestimation of TST and improves the sensitivity for identifying studies with sleep-disordered breathing. COMMENTARY:A commentary on this article appears in this issue on page 9.

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.

OBJECTIVE:The objective of the Sleep Disordered Breathing substudy of the Nulliparous Pregnancy Outcomes Study Monitoring Mothers-to-be (nuMoM2b) is to determine whether sleep disordered breathing during pregnancy is a risk factor for adverse pregnancy outcomes. STUDY DESIGN:NuMoM2b is a prospective cohort study of 10,037 nulliparous women with singleton gestations that was conducted across 8 sites with a central Data Coordinating and Analysis Center. The Sleep Disordered Breathing substudy recruited 3702 women from the cohort to undergo objective, overnight in-home assessments of sleep disordered breathing. A standardized level 3 home sleep test was performed between 6(0)-15(0) weeks' gestation (visit 1) and again between 22(0)-31(0) weeks' gestation (visit 3). Scoring of tests was conducted by a central Sleep Reading Center. Participants and their health care providers were notified if test results met "urgent referral" criteria that were based on threshold levels of apnea hypopnea indices, oxygen saturation levels, or electrocardiogram abnormalities but were not notified of test results otherwise. The primary pregnancy outcomes to be analyzed in relation to maternal sleep disordered breathing are preeclampsia, gestational hypertension, gestational diabetes mellitus, fetal growth restriction, and preterm birth. RESULTS:Objective data were obtained at visit 1 on 3261 women, which was 88.1% of the studies that were attempted and at visit 3 on 2511 women, which was 87.6% of the studies that were attempted. Basic characteristics of the substudy cohort are reported in this methods article. CONCLUSION:The substudy was designed to address important questions regarding the relationship of sleep-disordered breathing on the risk of preeclampsia and other outcomes of relevance to maternal and child health.

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. (C) 2015 Elsevier B.V. All rights reserved.

In sleep medicine, clinical studies often use their own data dictionaries for capturing clinical sleep events using proprietary signal analysis software [1][2]. Visualization of polysomnograms and their associated events from multiple distinct studies, such as for the National Sleep Research Resource (NSRR)[3], is an unresolved issue. Currently, there is no known visualization software for the European Data Format (EDF) that can be dynamically configured to support rendering of sleep events for multiple vendor formats. To address this challenge, domain ontology has been developed as a part of NSRR to model all sleep medicine terms and concepts to provide a common schema for addressing the structural and semantic heterogeneity of multiple vendor formats [4]. A Reconfigurable Rendering Engine using Abstract Factory pattern [5] and domain ontology provides a standard interface for accessing ontology-enabled clinical events for the visualization of electrophysiological signals. About 11,078 polysomnograms (8,444 SHHS, 860 CHAT, 591 HeartBEAT, 730 CFS, 453 SOF) [12] in EDF have been processed resulting in 1.1TB of web-accessible and reusable PSGs with NSRR standardized event annotations.