Since 1994, NIH has supported the standardized collection of polysomnography (PSG) studies as part of landmark multi-center studies which have used well-defined methods for data collection and quality assurance. Across studies, information on disease risk factors and outcomes, cardiovascular and neurocognitive function, and biochemical marker data, are available for children and adults representing diverse backgrounds. These data could serve as an invaluable national resource, providing opportunities to engage the scientific community, including trainees, in efforts to discover predictive bio-physiological signals for disease incidence and progression and to address critical questions regarding disease susceptibility and subgroup differences not possible using data from single cohorts. Improved access to those data would add value to already funded projects, ensuring maximal and enduring impact. Optimal use of such data requires the systematic organization and structuring of these data and access to procedures and computational tools for easy but secure access and curation of well-defined data subsets. The NHLBI National Sleep Research Resource (NSRR) aims to meet these compelling needs, thus leveraging the NIH's investments in the collection of sleep data in well characterized cohort studies and clinical trial to create a unique national resource of reliably-scored, well-annotated research PSGs from many major NHLBI cohorts or clinical trials (~ 50,000 sleep studies). The NSRR also will include: a) an electronic database of polysomnography data (raw signals, scored annotations, and summary sleep metrics);b) quantitative metrics of heart rate and EEG signals;b) linked data on clinical, physiological and biochemical parameters;c) a tool set to allow the user to search and access de-identified research data using a secure, cloud platform;d) an open-source suite of tools for offline analyses for signal processing, file structure editing, data harmonization and statistic generation;and e) user support for resource usage. NSRR will be a scalable and expandable resource constructed by capitalizing on major advances in informatics, developed with leading-edge agile software engineering methodology, user-centered interface design and an ontology-driven architecture.

Public Health Relevance

The creation of a central library of well-defined sleep studies linked to clinical and physiological data will transform data sharing approaches across the scientific community, enhancing the ability of researchers to address critical questions regarding the role of sleep disorders in the etiology of chronic health conditions, such as cardiovascular disease and identify subgroups of the population at greatest risk for sleep disorders and their related co-morbidities.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Resource-Related Research Projects (R24)
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Special Emphasis Panel (ZHL1-CSR-P (F3))
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Twery, Michael
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Brigham and Women's Hospital
United States
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Kaplan, Katherine A; Hardas, Prajesh P; Redline, Susan et al. (2017) Correlates of sleep quality in midlife and beyond: a machine learning analysis. Sleep Med 34:162-167
Purcell, S M; Manoach, D S; Demanuele, C et al. (2017) Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat Commun 8:15930
Pamidi, Sushmita; Redline, Susan; Rapoport, David et al. (2017) An Official American Thoracic Society Workshop Report: Noninvasive Identification of Inspiratory Flow Limitation in Sleep Studies. Ann Am Thorac Soc 14:1076-1085
Costa, Madalena D; Davis, Roger B; Goldberger, Ary L (2017) Heart Rate Fragmentation: A Symbolic Dynamical Approach. Front Physiol 8:827
Goldberger, Ary L; Henriques, Teresa; Mariani, Sara (2016) Sublimation-like Behavior of Cardiac Dynamics in Heart Failure: A Malignant Phase Transition? Complexity 21:24-32
Budhiraja, Rohit; Thomas, Robert; Kim, Matthew et al. (2016) The Role of Big Data in the Management of Sleep-Disordered Breathing. Sleep Med Clin 11:241-55
Dean 2nd, Dennis A; Goldberger, Ary L; Mueller, Remo et al. (2016) Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource. Sleep 39:1151-64
Mariani, Sara; Borges, Ana F T; Henriques, Teresa et al. (2016) Analysis of the sleep EEG in the complexity domain. Conf Proc IEEE Eng Med Biol Soc 2016:6429-6432
Weingarten, Jeremy A; Dubrovsky, Boris; Basner, Robert C et al. (2016) Polysomnographic Measurement of Sleep Duration and Bodily Pain Perception in the Sleep Heart Health Study. Sleep 39:1583-9
Henriques, Teresa S; Mariani, Sara; Burykin, Anton et al. (2016) Multiscale Poincaré plots for visualizing the structure of heartbeat time series. BMC Med Inform Decis Mak 16:17

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