Obstructive Sleep Apnea and Risk of Cardiovascular Events and All-Cause Mortality: A Decade-Long Historical Cohort Study
Background:
Obstructive sleep apnea (OSA) has been reported to be a risk factor for cardiovascular (CV) disease. Although the apnea-hypopnea index (AHI) is the most commonly used measure of OSA, other less well studied OSA-related variables may be more pathophysiologically relevant and offer better prediction. The objective of this study was to evaluate the relationship between OSA-related variables and risk of CV events.
Methods and Findings:
A historical cohort study was conducted using clinical database and health administrative data. Adults referred for suspected OSA who underwent diagnostic polysomnography at the sleep laboratory at St Michael's Hospital (Toronto, Canada) between 1994 and 2010 were followed through provincial health administrative data (Ontario, Canada) until May 2011 to examine the occurrence of a composite outcome (myocardial infarction, stroke, congestive heart failure, revascularization procedures, or death from any cause). Cox regression models were used to investigate the association between baseline OSA-related variables and composite outcome controlling for traditional risk factors. The results were expressed as hazard ratios (HRs) and 95% CIs; for continuous variables, HRs compare the 75th and 25th percentiles. Over a median follow-up of 68 months, 1,172 (11.5%) of 10,149 participants experienced our composite outcome. In a fully adjusted model, other than AHI OSA-related variables were significant independent predictors: time spent with oxygen saturation <90% (9 minutes versus 0; HR = 1.50, 95% CI 1.25–1.79), sleep time (4.9 versus 6.4 hours; HR = 1.20, 95% CI 1.12–1.27), awakenings (35 versus 18; HR = 1.06, 95% CI 1.02–1.10), periodic leg movements (13 versus 0/hour; HR = 1.05, 95% CI 1.03–1.07), heart rate (70 versus 56 beats per minute [bpm]; HR = 1.28, 95% CI 1.19–1.37), and daytime sleepiness (HR = 1.13, 95% CI 1.01–1.28).The main study limitation was lack of information about continuous positive airway pressure (CPAP) adherence.
Conclusion:
OSA-related factors other than AHI were shown as important predictors of composite CV outcome and should be considered in future studies and clinical practice.
Please see later in the article for the Editors' Summary
Vyšlo v časopise:
Obstructive Sleep Apnea and Risk of Cardiovascular Events and All-Cause Mortality: A Decade-Long Historical Cohort Study. PLoS Med 11(2): e32767. doi:10.1371/journal.pmed.1001599
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1001599
Souhrn
Background:
Obstructive sleep apnea (OSA) has been reported to be a risk factor for cardiovascular (CV) disease. Although the apnea-hypopnea index (AHI) is the most commonly used measure of OSA, other less well studied OSA-related variables may be more pathophysiologically relevant and offer better prediction. The objective of this study was to evaluate the relationship between OSA-related variables and risk of CV events.
Methods and Findings:
A historical cohort study was conducted using clinical database and health administrative data. Adults referred for suspected OSA who underwent diagnostic polysomnography at the sleep laboratory at St Michael's Hospital (Toronto, Canada) between 1994 and 2010 were followed through provincial health administrative data (Ontario, Canada) until May 2011 to examine the occurrence of a composite outcome (myocardial infarction, stroke, congestive heart failure, revascularization procedures, or death from any cause). Cox regression models were used to investigate the association between baseline OSA-related variables and composite outcome controlling for traditional risk factors. The results were expressed as hazard ratios (HRs) and 95% CIs; for continuous variables, HRs compare the 75th and 25th percentiles. Over a median follow-up of 68 months, 1,172 (11.5%) of 10,149 participants experienced our composite outcome. In a fully adjusted model, other than AHI OSA-related variables were significant independent predictors: time spent with oxygen saturation <90% (9 minutes versus 0; HR = 1.50, 95% CI 1.25–1.79), sleep time (4.9 versus 6.4 hours; HR = 1.20, 95% CI 1.12–1.27), awakenings (35 versus 18; HR = 1.06, 95% CI 1.02–1.10), periodic leg movements (13 versus 0/hour; HR = 1.05, 95% CI 1.03–1.07), heart rate (70 versus 56 beats per minute [bpm]; HR = 1.28, 95% CI 1.19–1.37), and daytime sleepiness (HR = 1.13, 95% CI 1.01–1.28).The main study limitation was lack of information about continuous positive airway pressure (CPAP) adherence.
Conclusion:
OSA-related factors other than AHI were shown as important predictors of composite CV outcome and should be considered in future studies and clinical practice.
Please see later in the article for the Editors' Summary
Zdroje
1. PeppardPE, YoungT, BarnetJH, PaltaM, HagenEW, et al. (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol In press.
2. EpsteinLJ, KristoD, StrolloPJJr, FriedmanN, MalhotraA, et al. (2009) Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 5: 263–276.
3. LeungRS, ComondoreVR, RyanCM, StevensD (2012) Mechanisms of sleep-disordered breathing: causes and consequences. Pflugers Arch 463: 213–230.
4. BradleyTD, FlorasJS (2009) Obstructive sleep apnoea and its cardiovascular consequences. Lancet 373: 82–93.
5. KohlerM, StradlingJR (2010) Mechanisms of vascular damage in obstructive sleep apnea. Nat Rev Cardiol 7: 677–685.
6. ChamiHA, FontesJD, VasanRS, KeaneyJFJr, O'ConnorGT, et al. (2013) Vascular inflammation and sleep disordered breathing in a community-based cohort. Sleep 36: 763–768C.
7. KendzerskaT, MollayevaT, GershonAS, LeungRS, HawkerG, et al. (2014) Untreated obstructive sleep apnea and the risk for serious long-term adverse outcomes: a systematic review. Sleep Med Rev 18: 49–59.
8. Polysomnography in patients with obstructive sleep apnea: an evidence-based analysis. Ont Health Technol Assess Ser 6: 1–38.
9. EdwardsBA, WellmanA, OwensRL (2013) PSGs: more than just the AHI. J Clin Sleep Med 9: 527–528.
10. EckertDJ, JordanAS, MalhotraA, WhiteDP, WellmanA (2013) Defining phenotypic causes of obstructive sleep apnea: Identification of novel therapeutic targets. Am J Respir Crit Care Med 188: 996–1004.
11. PunjabiNM, CaffoBS, GoodwinJL, GottliebDJ, NewmanAB, et al. (2009) Sleep-disordered breathing and mortality: a prospective cohort study. PLoS Med 6: e1000132.
12. YoungT, FinnL, PeppardPE, Szklo-CoxeM, AustinD, et al. (2008) Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 31: 1071–1078.
13. GottliebDJ, YenokyanG, NewmanAB, O'ConnorGT, PunjabiNM, et al. (2010) Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure: the sleep heart health study. Circulation 122: 352–360.
14. RedlineS, YenokyanG, GottliebDJ, ShaharE, O'ConnorGT, et al. (2010) Obstructive sleep apnea-hypopnea and incident stroke: the sleep heart health study. Am J Respir Crit Care Med 182: 269–277.
15. (2005) Improving health care data in Ontario. ICES investigative report Toronto: Institute for Clinical Evaluative Sciences.
16. Policies and procedures manual for the assistive devices program. Ministry of Health and Long-Term Care 1–121.
17. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force. Sleep 22: 667–689.
18. FleethamJ, AyasN, BradleyD, FergusonK, FitzpatrickM, et al. (2006) Canadian Thoracic Society guidelines: diagnosis and treatment of sleep disordered breathing in adults. Can Respir J 13: 387–392.
19. Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer. xxii, 568 .
20. GrambschP, TherneauT (1994) Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81: 515–526.
21. AzurMJ, StuartEA, FrangakisC, LeafPJ (2011) Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res 20: 40–49.
22. Van BuurenS, Groothuis-OudshoornK (2011) MICE: multivariate imputation by chained equations in R. Journal of Statistical Software 45: 1–67.
23. Rubin DB (1987) Multiple imputation for nonresponse in surveys. New York: Wiley. xxix, 258 p.
24. AtkinsonAC (1980) A note on the generalized information criterion for choice of a model. Biometrika 67: 413–418.
25. ChiaYC (2011) Review of tools of cardiovascular disease risk stratification: interpretation, customisation and application in clinical practice. Singapore Med J 52: 116–123.
26. Harrell FE (2013) RMS: regression modeling strategies, R package verson 4.0-0. http://CRANR-projectorg[mgn]package = rms.
27. MarinJM, CarrizoSJ, VicenteE, AgustiAG (2005) Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 365: 1046–1053.
28. BuchnerNJ, SannerBM, BorgelJ, RumpLC (2007) Continuous positive airway pressure treatment of mild to moderate obstructive sleep apnea reduces cardiovascular risk. Am J Respir Crit Care Med 176: 1274–1280.
29. PunjabiNM, NewmanAB, YoungTB, ResnickHE, SandersMH (2008) Sleep-disordered breathing and cardiovascular disease: an outcome-based definition of hypopneas. Am J Respir Crit Care Med 177: 1150–1155.
30. JunJ, ReinkeC, BedjaD, BerkowitzD, Bevans-FontiS, et al. (2010) Effect of intermittent hypoxia on atherosclerosis in apolipoprotein E-deficient mice. Atherosclerosis 209: 381–386.
31. FosterGE, HanlyPJ, AhmedSB, BeaudinAE, PialouxV, et al. (2010) Intermittent hypoxia increases arterial blood pressure in humans through a Renin-Angiotensin system-dependent mechanism. Hypertension 56: 369–377.
32. AtkesonA, JelicS (2008) Mechanisms of endothelial dysfunction in obstructive sleep apnea. Vasc Health Risk Manag 4: 1327–1335.
33. KooBB, BlackwellT, Ancoli-IsraelS, StoneKL, StefanickML, et al. (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: 1223–1231.
34. WaltersAS, RyeDB (2010) Evidence continues to mount on the relationship of restless legs syndrome/periodic limb movements in sleep to hypertension, cardiovascular disease, and stroke. Sleep 33: 287.
35. VgontzasAN, LiaoD, PejovicS, CalhounS, KaratarakiM, et al. (2010) Insomnia with short sleep duration and mortality: the Penn State cohort. Sleep 33: 1159–1164.
36. KrakowB, RomeroE, UlibarriVA, KiktaS (2012) Prospective assessment of nocturnal awakenings in a case series of treatment-seeking chronic insomnia patients: a pilot study of subjective and objective causes. Sleep 35: 1685–1692.
37. Kendzerska T, Shapiro C. (2013) Associations and consequences of hypersomnias: morbidity and mortality. Kushida CA, editor. The encyclopedia of sleep. Waltham (Massachusetts): Academic Press 2: : 460–468.
38. GottliebDJ, CraigSE, Lorenzi-FilhoG, HeeleyE, RedlineS, et al. (2013) Sleep apnea cardiovascular clinical trials – current status and steps forward: The International Collaboration of Sleep Apnea Cardiovascular Trialists. Sleep 36: 975–980.
39. BarbeF, Duran-CantollaJ, Sanchez-de-la-TorreM, Martinez-AlonsoM, CarmonaC, et al. (2012) Effect of continuous positive airway pressure on the incidence of hypertension and cardiovascular events in nonsleepy patients with obstructive sleep apnea: a randomized controlled trial. JAMA 307: 2161–2168.
40. YoungT, FinnL, AustinD, PetersonA (2003) Menopausal status and sleep-disordered breathing in the Wisconsin Sleep Cohort Study. Am J Respir Crit Care Med 167: 1181–1185.
41. PialouxV, BrownAD, LeighR, FriedenreichCM, PoulinMJ (2009) Effect of cardiorespiratory fitness on vascular regulation and oxidative stress in postmenopausal women. Hypertension 54: 1014–1020.
42. EasthamJA, KattanMW, ScardinoPT (2002) Nomograms as predictive models. Semin Urol Oncol 20: 108–115.
43. IasonosA, SchragD, RajGV, PanageasKS (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26: 1364–1370.
44. LinDY, PsatyBM, KronmalRA (1998) Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54: 948–963.
45. HandDJ (2006) Classifier technology and the illusion of progress. Stat Sci 21: 1–14.
46. TzoulakiI, LiberopoulosG, IoannidisJP (2009) Assessment of claims of improved prediction beyond the Framingham risk score. JAMA 302: 2345–2352.
47. PencinaMJ, D'AgostinoRB, VasanRS (2010) Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med 48: 1703–1711.
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