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      The National Sleep Research Resource: towards a sleep data commons

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          Abstract

          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.

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          Most cited references30

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          A randomized trial of adenotonsillectomy for childhood sleep apnea.

          Adenotonsillectomy is commonly performed in children with the obstructive sleep apnea syndrome, yet its usefulness in reducing symptoms and improving cognition, behavior, quality of life, and polysomnographic findings has not been rigorously evaluated. We hypothesized that, in children with the obstructive sleep apnea syndrome without prolonged oxyhemoglobin desaturation, early adenotonsillectomy, as compared with watchful waiting with supportive care, would result in improved outcomes. We randomly assigned 464 children, 5 to 9 years of age, with the obstructive sleep apnea syndrome to early adenotonsillectomy or a strategy of watchful waiting. Polysomnographic, cognitive, behavioral, and health outcomes were assessed at baseline and at 7 months. The average baseline value for the primary outcome, the attention and executive-function score on the Developmental Neuropsychological Assessment (with scores ranging from 50 to 150 and higher scores indicating better functioning), was close to the population mean of 100, and the change from baseline to follow-up did not differ significantly according to study group (mean [±SD] improvement, 7.1±13.9 in the early-adenotonsillectomy group and 5.1±13.4 in the watchful-waiting group; P=0.16). In contrast, there were significantly greater improvements in behavioral, quality-of-life, and polysomnographic findings and significantly greater reduction in symptoms in the early-adenotonsillectomy group than in the watchful-waiting group. Normalization of polysomnographic findings was observed in a larger proportion of children in the early-adenotonsillectomy group than in the watchful-waiting group (79% vs. 46%). As compared with a strategy of watchful waiting, surgical treatment for the obstructive sleep apnea syndrome in school-age children did not significantly improve attention or executive function as measured by neuropsychological testing but did reduce symptoms and improve secondary outcomes of behavior, quality of life, and polysomnographic findings, thus providing evidence of beneficial effects of early adenotonsillectomy. (Funded by the National Institutes of Health; CHAT ClinicalTrials.gov number, NCT00560859.).
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            The Sleep Heart Health Study: design, rationale, and methods.

            The Sleep Heart Health Study (SHHS) is a prospective cohort study designed to investigate obstructive sleep apnea (OSA) and other sleep-disordered breathing (SDB) as risk factors for the development of cardiovascular disease. The study is designed to enroll 6,600 adult participants aged 40 years and older who will undergo a home polysomnogram to assess the presence of OSA and other SDB. Participants in SHHS have been recruited from cohort studies in progress. Therefore, SHHS adds the assessment of OSA to the protocols of these studies and will use already collected data on the principal risk factors for cardiovascular disease as well as follow-up and outcome information pertaining to cardiovascular disease. Parent cohort studies and recruitment targets for these cohorts are the following: Atherosclerosis Risk in Communities Study (1,750 participants), Cardiovascular Health Study (1,350 participants), Framingham Heart Study (1,000 participants), Strong Heart Study (600 participants), New York Hypertension Cohorts (1,000 participants), and Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study (900 participants). As part of the parent study follow-up procedures, participants will be surveyed at periodic intervals for the incidence and recurrence of cardiovascular disease events. The study provides sufficient statistical power for assessing OSA and other SDB as risk factors for major cardiovascular events, including myocardial infarction and stroke.
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              Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men.

              Very little information is available to direct the prevention or management of osteoporosis in men. The Osteoporotic Fractures in Men (MrOS) Study is a prospective cohort study designed to examine the extent to which fracture risk is related to bone mass, bone geometry, lifestyle, anthropometric and neuromuscular measures, and fall propensity, as well as to determine how fractures affect quality of life in men. The study is also designed to understand how osteoporosis is related to prostate disease. At baseline, participants completed questionnaires regarding medical history, medications, physical activity, diet, alcohol intake, and cigarette smoking. Objective measures of anthropometric, neuromuscular, vision, strength, and cognitive variables were obtained. Skeletal assessments included DEXA, calcaneal ultrasound, and vertebral radiographs. Vertebral and proximal femoral QCT was performed on a subset (65%). Serum, urine, and DNA specimens were collected. After the baseline assessments, a questionnaire is mailed to participants every 4 months to ascertain incident falls, fractures, prostate cancer, and deaths. After an average of 4.5 years, participants are scheduled to return for a second comprehensive visit. Men were eligible if > or =65 years. 5995 men enrolled with a mean (+/-SD) age of 73.7 (+/-5.9) years, 11% of which were minorities. Most rated their health as good/excellent. Few were current smokers, although 59% had smoked previously, and 35% reported no alcohol intake, while 47% consumed at least 2 drinks per week. The mean (range) body mass index was 26.9 kg/m2 (17-56). A non-traumatic fracture after age 50 was reported by 17% of the cohort. The MrOS cohort should provide valuable information concerning the determinants of fracture in men and should help set the stage for the development of effective methods to identify those at risk.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                October 2018
                31 May 2018
                31 May 2018
                : 25
                : 10
                : 1351-1358
                Affiliations
                [1 ]Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA
                [2 ]Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA
                [3 ]Brigham and Women’s Hospital, Boston, Massachusetts, USA
                [4 ]Harvard Medical School, Harvard University, Boston, Massachusetts, USA
                Author notes
                Corresponding Author: Guo-Qiang Zhang, PhD, Multidisciplinary Science Building 230, 725 Rose Street, Lexington, KY 40536, USA ( gq.zhang@ 123456uky.edu )
                Article
                ocy064
                10.1093/jamia/ocy064
                6188513
                29860441
                76eefdae-63d0-4418-a8bf-81803709e93f
                © The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contactjournals.permissions@oup.com

                History
                : 30 May 2017
                : 5 April 2018
                : 26 April 2018
                Page count
                Pages: 8
                Funding
                Funded by: National Heart, Lung, and Blood Institute 10.13039/100000050
                Award ID: NHLBI R24 HL114473
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: 1626364
                Funded by: Center for Clinical and Translational Science 10.13039/100008523
                Award ID: UL1TR001998
                Categories
                Research and Applications

                Bioinformatics & Computational biology
                data commons,data integration,data sharing,common data element,data provenance,sleep research,polysomnography,data quality,european data format

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