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Prepolysomnography evaluation can predict obstructive sleep apnea and is correlated to its severity

Abstract

Background

Obstructive sleep apnea (OSA) is increasingly identified as a disease with major health consequences. The limited availability of the gold standard diagnostic test, polysomnography (PSG), mandates careful clinical evaluation of suspected patients. This can allow better patient selection for referral for confirmatory diagnostic test.

Objective

The study aimed at identifying the importance of pre-PSG evaluation in prediction of obstructive sleep apnea and its relation to disease severity.

Patients and methods

A total of 170 patients were included. Detailed demographic characteristics, anthropometric measures, and comorbid conditions were recorded. Correlation to PSG results was done, and multivariate analysis was used to identify predictors of disease.

Results

OSA was diagnosed in 58.8% of our studied patients. The patients with OSA and notably the severe subgroup were of older age, predominantly male, and current or ex-smoker. Mean BMI was highest in the patients with severe OSA (41.99 ±8.92) and same for mean neck and waist circumference, both were significantly higher in patients with severe OSA. In multivariate logistic regression analysis, significant predictive factors for OSA were older age, male sex, being nonemployed, having hypertension, and larger tonsillar size.

Conclusion

Patient demographics, anthropometric characteristics, and presence of comorbid conditions such as hypertension are strong predictors of having OSA and justify referral for diagnostic sleep study.

References

  1. Lam JC, Sharma SK, Lam B. Obstructive sleep apnoea: definitions, epidemiology & natural history. Indian J Med Res 2010; 131:165–170.

    PubMed  Google Scholar 

  2. Ulualp SO. Snoring and obstructive sleep apnea. Med Clin North Am 2010; 94:1047–1055.

    Article  Google Scholar 

  3. Abrishami A, Khajehdehi A, Chung F. A systematic review of screening questionnaires for obstructive sleep apnea. Can J Anaesth 2010; 57:423–438.

    Article  Google Scholar 

  4. World Health Organization. Physical status: the use and interpretation of anthropometry. Technical Report Series No. 854. World Health Organization, Geneva, 1995. Available at: www.who.int/en/. [Accessed 20 March 2015].

  5. WHO Expert Consultation. Appropriate body mass index for Asian populations and its implications for policy intervention strategies. Lancet 2004; 363:157–163.

    Article  Google Scholar 

  6. Davies R, Ali N, Stradling J. Neck circumference and other clinical features in the diagnosis of the obstructive sleep apnoea syndrome. Thorax 1992; 47:101–105.

    Article  CAS  Google Scholar 

  7. Ostchega Y, Dillon C, Prineas RJ, McDowell M, Carroll M. Tables for the selection of correct blood pressure cuff size based on self-reported height and weight and estimating equations for mid-arm circumference: data from the U.S. National Health and Nutrition Examination Survey. J Hum Hypertens 2006; 20:15–22.

    Article  CAS  Google Scholar 

  8. Friedman M, Ibrahim H, Bass L. Clinical staging for sleep-disordered breathing. Otolaryngol Head Neck Surg 2002; 127:13–21.

    Article  Google Scholar 

  9. Friedman M, Tanyeri H, La Rosa M, Landsberg R, Vaidyanathan K, Pieri S, Caldarelli D. Clinical predictors of obstructive sleep apnea. Laryngoscope 1999; 109:1901–1907.

    Article  CAS  Google Scholar 

  10. Friedman M, Soans R, Gurpinar B, Lin HC, Joseph NJ. Inter examiner agreement of Friedman tongue positions for staging of obstructive sleep apnea/hypopnea syndrome. Otolaryngol Head Neck Surg 2008; 139:372–377.

    Article  Google Scholar 

  11. Berry RB, Brooks R, Gamaldo CE, Harding SM, Loyd RM, Marcus CL, Bradley VV. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, version 2.3. Darien, IL: American Academy of Sleep Medicine; 2016.

    Google Scholar 

  12. American Academy of Sleep Medicine. Sateia M, ed. International classification of sleep disorders. Chapter 2 3rd ed. Darien, IL: American Academy of Sleep Medicine; 2014; 114–122.

    Google Scholar 

  13. Suliman LA, Shalabi NM, Elmorsy AS, Moawed MK. Value of STOP-BANG and Berlin questionnaires in the diagnosis and severity prediction of obstructive sleep apnea hypopnea syndrome. Egypt J Bronchol 2017; 11:367–371.

    Article  Google Scholar 

  14. El-Sayed IH. Comparison of four sleep questionnaires for screening obstructive sleep apnea. Egypt J Chest Dis Tuber 2012; 61:433–441.

    Article  Google Scholar 

  15. Martins AB, Tufik S, Moura SM. Physiopathology of obstructive sleep apnea-hypopnea syndrome. J Bras Pneumol 2007; 33:93–100.

    Article  Google Scholar 

  16. Young T, Shahar E, Nieto FJ, Redline S, Newman AB, Gottlieb DJ, et al. Sleep Heart Health Study Research Group. Predictors of sleep-disordered breathing in community-dwelling adults: the Sleep Heart Health Study. Arch Intern Med 2002; 162:893–900.

    Article  Google Scholar 

  17. Mak KK, Ho SY, Thomas GN, Lo WS, Cheuk DK, Lai YK, et al. Smoking and sleep disorders in Chinese adolescents. Sleep Med 2010; 11:268–273.

    Article  Google Scholar 

  18. Kapsimalis F, Kryger MH. Gender and obstructive sleep apnea syndrome: part 2. Mechanisms. Sleep 2002; 25:499–506.

    PubMed  Google Scholar 

  19. Adewole OO, Hakeem A, Fola A, Anteyi E, Ajuwon Z, Erhabor G. Obsructive sleep apnea among adults in Nigeria. J Natl Med Assoc 2009; 101:720–725.

    Article  Google Scholar 

  20. Hussain SF, Cloonan YK, Islam M, Rahbar MH. Prevalence and associated risk factors of sleep-disordered breathing symptoms in young and middle-aged Pakistani employed adults. Sleep Breath 2010; 14:137–144.

    Article  Google Scholar 

  21. Olusola AS, Adegboyega O. Risk factors of obstructive sleep apnea among nigerian outpatients. Braz J Otorhinolaryngol 2012; 78:27–33.

    Google Scholar 

  22. Caples SM, Gami AS, Somers VK. Obstructive sleep apnea. Ann Intern Med 2005; 142:187–197.

    Article  Google Scholar 

  23. Salome CM, King GG, Berend N. Physiology of obesity and effects on lung function. J Appl Physiol 2010; 108:206–211.

    Article  Google Scholar 

  24. Reddy EV, Kadhiravan T, Mishra HK, Sreenivas V, Handa KK, Sinha S, et al. Prevalence and risk factors of obstructive sleep apnea among middle-aged urban Indians: a community-based study. Sleep Med 2009; 10:913–918.

    Article  Google Scholar 

  25. Hiestand DM, Britz P, Goldman M, Phillips B. Prevalence of symptoms and risk of sleep apnea in the US population: results from the national sleep foundation sleep in America 2005 poll. Chest 2006; 130:780–786.

    Article  Google Scholar 

  26. Shaun WHL, aKhuen YN, bWeng KC. The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: a systematic review and meta-analysis. Sleep Med Rev 2017; 31:91–101.

    Article  Google Scholar 

  27. Wolk R, Shamsuzzaman AS, Somers VK. Obesity, sleep apnea, and hypertension. Hypertension 2003; 42:1067–1074.

    Article  CAS  Google Scholar 

  28. Wolf J, Lewicka J, Narkiewicz K. Obstructive sleep apnea: an update on mechanisms and cardiovascular consequences. Nutr Metab Cardiovasc Dis 2007; 17:233–240.

    Article  Google Scholar 

  29. Wali SO, Abalkhail B, Krayem A. Prevalence and risk factors of obstructive sleep apnea syndrome in a Saudi Arabian population. Ann Thorac Med 2017; 12:88–94.

    Article  Google Scholar 

  30. Bahammam AS, Al-Rajeh MS, Al-Ibrahim FS, Arafah MA, Sharif MM. Prevalence of symptoms and risk of sleep apnea in middle-aged Saudi women in primary care. Saudi Med J 2009; 30:1572–1576.

    PubMed  Google Scholar 

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Correspondence to Hend M. Esmaeel MD.

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Esmaeel, H.M., Mohammadien, H.A., Saleh, AE.M. et al. Prepolysomnography evaluation can predict obstructive sleep apnea and is correlated to its severity. Egypt J Bronchol 13, 556–562 (2019). https://doi.org/10.4103/ejb.ejb_19_19

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