Obstrüktif uyku apnesinde oksijen satürasyonunun %90-95 altında kaldığı durumlarda uyku süresi ile klinik ve laboratuvar bulguları arasındaki ilişki

Yıl 2016, Cilt: 6 Sayı: 1, 16 - 22, 30.04.2016

Suat Avcı , Aynur Yılmaz Avcı Berna Devrim Yağbasan Hüseyin Günizi

Öz

Objective: The aim of the present study was to determine correlations between CT90 and CT95 values and physical examination parameters, chronic metabolic diseases, smoking, mean platelet volume, cerebral magnetic resonance imaging (MRI), presence and number of hyperintense foci in obstructive sleep apnea (OSA).

Methods: A total of 1154 patients who underwent polysomnography in our sleep laboratory between 2011 and 2014 were screened retrospectively. Among them, 72 cases who underwent ear, nose and throat examinations, cerebral MR, CBC and biochemical tests were included in the study. All patients underwent a detailed anamnesis together with (1) measurements of BMI (body mass index) (2) circumferences of neck and abdomen, (3) examination of oropharynx, (4) Müller maneuver with the aid of fiberoptic endoscope, (5) estimation of Epworth sleep scale scores, (6) and polysomnographic (PSG) tests.

Results: According to the severity of OSA, the patients had simple snoring (22.2%), mild (19.4%) and severe OSA (38.9%). In multivariate regression analysis, body mass index (BMI) (p=0.026) and apnea/hypopnea index (AHI) (p=0.013) were seen as independent variables affecting CT90 (R2=49%). Multivariate linear regression analysis demonstrated that independent variables of smoking (p=0.001), AHI (p= 0.003) and number of hyperintense foci (p=0.013) affected CT95 (R2=%47.9), while relationships
between diabetes, BMI and CT95 were not statistically significant.

Conclusion: Since CT95 values are affected by smoking without any statistically significant correlation with retropalatal and retroglossal Müller stages, we think that consideration of CT90 value will be more appropriate in the evaluation of the severity of chronic intermittent hypoxia in patients with obstructive sleep apnea. However, the correlation between CT90 value and AHI is closer to the value indicated in the literature, but not stronger.

Anahtar Kelimeler

Obstructive sleep apnea, duration of sleep, clinical, laboratory

Kaynakça

• 1. Bostanci A, Turhan M, Bozkurt S. Factors influencing sleep time with oxygen saturation below 90% in sleep-disordered breathing. Laryngoscope 2015;125:1008–12.
• 2. Iber C, Ancoli-Israel S, Chesson A, Quan S. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Westchester, IL: American Academy of Sleep Medicine; 2007.
• 3. Kulkas A, Tiihonen P, Julkunen P, Mervaala E, Töyras J. Novel parameters indicate significant differences in severity of obstructive sleep apnea with patients having similar apnea-hypopnea index. Med Biol Eng Comput 2013;51:697–708.
• 4. Mediano O, Barcelo A, de la Pena M, Gozal D, Agusti A, Barbe F. Daytime sleepiness and polysomnographic variables in sleep apnoea patients. Eur Respir J 2007;30:110–3.
• 5. Asano K, Takata Y, Usui Y, et al. New index for analysis of polysomnography, "integrated area of desaturation", is associated with high cardiovascular risk in patients with mild to moderate obstructive sleep apnea. Respiration 2009;78-278–84.
• 6. Neubauer J. Invited review: physiological and pathophysiological responses to intermittent hypoxia. J Appl Physiol 2001;90: 1593–9.
• 7. Li Q, Jin XJ. Correlations between the duration and frequency of sleep apnea episode and hypoxemia in patients with obstructive sleep apnea syndrome. [Article in Chinese] Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2009;44:825–30.
• 8. Zhang XB, Zen HQ, Lin QC, Chen GP, Chen LD, Chen H. TST, as a polysomnographic variable, is superior to the apnea hypopnea index for evaluating intermittent hypoxia in severe obstructive sleep apnea. Eur Arch Otorhinolaryngol 2014;271: 2745–50.
• 9. Zhang J, Li Y, Cao X, et al. The combination of anatomy and physiology in predicting the outcomes of velopharyngeal surgery. Laryngoscope. 2014;124:1718–23.
• 10. Vgontzas AN, Papanicolaou DA, Bixler EO, et al. Sleep apnea and daytime sleepiness and fatigue: relation to visceral obesity,insulin resistance and hypercytokinemia. J Clin Endocrinol Metab 2000; 85:1151–8.
• 11. Alam I, Lewis K, Stephens JW, Baxter JN. Obesity, metabolic syndrome and sleep apnoea: all pro-inflammatory states. Obes Rev 2007;8:119–27.
• 12. Punjabi NM, Sorkin JD, Katzel LI, Goldberg AP, Schwartz AR, Smith PL. Sleep-disordered breathing and insulin resistance in middle-aged and overweight men. Am J Respir Crit Care Med 2002;165:677–82.
• 13. Theorell-Haglöw J, Berne C, Janson C, Lindberg E. Obstructive sleep apnoea is associated with decreased insulin sensitivity in females. Eur Respir J 2008;31:1054–60.
• 14. Punjabi NM, Beamer BA. Alterations in glucose disposal in sleep-disordered breathing. Am J Respir Crit Care Med 2009; 179:235–40.
• 15. Punjabi NM, Shahar E, Redline S, Gottlieb DJ, Givelber R, Resnick HE. Sleep-disordered breathing, glucose intolerance and insulin resistance: the Sleep Heart Health Study. Am J Epidemiol 2004;160:521–30.
• 16. Redline S, Yenokyan G, Gottlieb DJ, et al. Obstructive sleep apnea-hypopnea and incident stroke: the sleep heart health study. Am J Respir Crit Care Med 2010;182:269–77.
• 17. Yaffe K, Laffan AM, Harrison SL,et al. Sleep-disordered breathing, hypoxia and risk of mild cognitive impairment and dementia in older women. JAMA 2011;306:613–9.
• 18. Davies CW, Crosby JH, Mullins RL. et al. Case control study of cerebrovascular damage defined by magnetic resonance imaging in patients with OSA and normal matched control subjects. Sleep 2001;24:715–20.
• 19. Bracco L, Piccini C, Moretti M, et al. Alzheimer's disease: role of size and location of white matter changes in determining cognitive deficits. Dement Geriatr Cogn Disord 2005;20:358–66.
• 20. Xiong Y, Mok V, Wong A, et al. The age-related white matter changes scale correlates with cognitive impairment. Eur J Neurol 2010;17:1451–6.
• 21. Fazekas F, Klemert R, Offenbacher H, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43:1683–9.
• 22. Schmidt R, Grazer A, Enzinger C, et al. MRI-detected white matter lesions: do they really matter? J Neural Transm 2011;118:673– 81.
• 23. Vermeer SE, Den Heijer T, Koudstaal PJ, Oudkerk M, Hofman A, Breteler MM. Incidence and risk factors of silent brain infarcts in the population-based Rotterdam Scan Study. Stroke 2003;34: 392–6.
• 24. Gottesman RF, Coresh J, Catellier DJ, et al. Blood pressure and white-matter disease progression in a biethnic cohort. Atherosclerosis Risk in Communities (ARIC) study. Stroke 2010;41:3–8.
• 25. Kim H, Yun CH, Thomas RJ, et al. Obstructive sleep apnea as a risk factor for cerebral white matter change in a middle-aged and older general population. Sleep 2013;36:709–15B.
• 26. Karakafl MS, Altekin RE, Bakt›r AO, Küçük M, Cilli A, Yalç›nkaya S. Association between mean platelet volume and severity of disease in patients with obstructive sleep apnea syndrome without risk factors for cardiovascular disease. Turk Kardiyol Dern Ars 2013; 41:14–20.
• 27. Geiser T, Buck F, Meyer BJ, Bassetti C, Haeberli A, Gugger M. In vivo platelet activation is increased during sleep in patients with obstructive sleep apnea syndrome. Respiration 2002;69: 229–34.
• 28. Hui DS, Ko FW, Fok JP, et al. The effects of nasal continuous positive airway pressure on platelet activation in obstructive sleep apnea syndrome. Chest 2004;125:1768–75.
• 29. Minoguchi K, Yokoe T, Tazaki T, et al. Silent brain infarction and platelet activation in obstructive sleep apnea. Am J Respir Crit Care Med 2007;175:612–7.
• 30. Varol E, Ozturk O, Gonca T, et al. Mean platelet volume is increased in patients with severe obstructive sleep apnea. Scand J Clin Lab Invest 2010;70:497–502.
• 31. Vizioli L, Muscari S, Muscari A. The relationship of mean platelet volume with the risk and prognosis of cardiovascular diseases. Int J Clin Pract 2009;63:1509–15.