The Determining Risk of Vascular Events by Apnea Monitoring (DREAM) study: design, rationale, and methods
The goal of the Determining Risk of Vascular Events by Apnea Monitoring (DREAM) study is to develop a prognostic model for cardiovascular outcomes, based on physiologic variables—related to breathing, sleep architecture, and oxygenation—measured during polysomnography in US veterans.
The DREAM study is a multi-site, retrospective observational cohort study conducted at three Veterans Affairs (VA) centers (West Haven, CT; Indianapolis, IN; Cleveland, OH). Veterans undergoing polysomnography between January 1, 2000 and December 31, 2004 were included based on referral for evaluation of sleep-disordered breathing, documented history and physical prior to sleep testing, and ≥2-h sleep monitoring. Demographic, anthropomorphic, medical, medication, and social history factors were recorded. Measures to determine sleep apnea, sleep architecture, and oxygenation were recorded from polysomnography. VA Patient Treatment File, VA–Medicare Data, Vista Computerized Patient Record System, and VA Vital Status File were reviewed on dates subsequent to polysomnography, ranging from 0.06 to 8.8 years (5.5 ± 1.3 years; mean ± SD).
The study population includes 1840 predominantly male, middle-aged veterans. As designed, the main primary outcome is the composite endpoint of acute coronary syndrome, stroke, transient ischemic attack, or death. Secondary outcomes include incidents of neoplasm, congestive heart failure, cardiac arrhythmia, diabetes, depression, and post-traumatic stress disorder. Laboratory outcomes include measures of glycemic control, cholesterol, and kidney function. (Actual results are pending.)
This manuscript provides the rationale for the inclusion of veterans in a study to determine the association between physiologic sleep measures and cardiovascular outcomes and specifically the development of a corresponding outcome-based prognostic model.
KeywordsSleep apnea OSA Epidemiology Veterans Cardiovascular
- 1.Colten H, Altevogt B (2006) Sleep disorders and sleep deprivation: an unmet public health problem. National Academies Press (US) National Academy of Sciences, Washington DCGoogle Scholar
- 9.Punjabi NM, Polotsky VY (2005) Disorders of glucose metabolism in sleep apnea. J Appl Physiol 1985:991998–992007Google Scholar
- 11.Samson P, Casey KR, Knepler J, Panos RJ (2012) Clinical characteristics, comorbidities, and response to treatment of veterans with obstructive sleep apnea, cincinnati veterans affairs medical center, 2005-2007. Prev Chron Dis 9, E46Google Scholar
- 12.Hirshkowitz M KS, Littner M, Kuna S, Weaver E, Kryger MH, Yaggi, Bishop M, Almenoff P. The VA Sleep Field Advisory Group (2014) Sleep-related breathing disorders: Sourcebook, 3rd Edition (Version 2.1)Google Scholar
- 14.Gottlieb DJ, Yenokyan G, Newman AB, O’Connor GT, Punjabi NM, Quan SF, Redline S, Resnick HE, Tong EK, Diener-West M, Shahar E (2010) Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure: the sleep heart health study. Circulation 122:352–360CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Koo BB, Blackwell T, Ancoli-Israel S, Stone KL, Stefanick ML, Redline S (2011) Association of incident cardiovascular disease with periodic limb movements during sleep in older men: outcomes of sleep disorders in older men (MrOS) study. Circulation 124:1223–1231CrossRefPubMedPubMedCentralGoogle Scholar
- 18.Rechtschaffen A, Kales A (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. National Institutes of Health, Washington DCGoogle Scholar
- 19.Iber C, Ancoli-Israel S, Chesson A (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specifications, 1st edn. American Academy of Sleep Medicine, WestchesterGoogle Scholar
- 20.Amerian Academy of Sleep Medicine (1992) EEG arousals: scoring rules and examples: a preliminary report from the sleep disorders atlas task force of the American sleep disorders association. Sleep 15:173–184Google Scholar
- 22.Cannon CP, Brindis RG, Chaitman BR, Cohen DJ, Cross JT, Drozda JP Jr, Fesmire FM, Fintel DJ, Fonarow GC, Fox KA, Gray DT, Harrington RA, Hicks KA, Hollander JE, Krumholz H, Labarthe DR, Long JB, Mascette AM, Meyer C, Peterson ED, Radford MJ, Roe MT, Richmann JB, Selker HP, Shahian DM, Shaw SE, Sprenger S, Swor R, Underberg JA, Van de Werf F, Weiner BH, Weintraub WS (2013) ACCF/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes and coronary artery disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Acute Coronary Syndromes and Coronary Artery Disease Clinical Data Standards). Crit Pathw Cardiol 12:65–105CrossRefPubMedGoogle Scholar
- 25.S.S. Administration, Actuarial Lift Table (2010) in: O.S.S. Website (Ed.), www.ssa.gov/oact/STATS/table4c6.html. Accessed July 10, 2015