The correlation of the severity of polysomnography with clinical assessment in cases of adult OSA in a tertiary hospital

INTRODUCTION

Obstructive sleep apnea is the most common sleep disordered breathing. The most recent analysis prevalence of OSA in general population is 5% .¹ The prevalence of OSA varies significantly based on the population studied and how OSA is analyzed (e.g., testing methodology, scoring criteria used, apnea-hypopnea index (AHI) threshold .² The OSA has been estimated to be 14% of men and 5% of women.³ Various mechanisms may exist including body fat distribution, craniofacial differences and female hormones. Obesity is important risk factor for OSA. The exact relationship between obesity and OSA are not well understood. It is multifactorial including BMI, neck soft tissue mass, parapharyngeal and lingual adipose deposition.⁴ Any weight gain is not necessarily cause OSA , an increase the soft tissue mass around the airway, other factors such as pharyngeal muscle tone and the biophysical compliance between airway patency and critical closing pressure are more important for OSA.⁵

The assessment of history and clinical findings are not enough to diagnose the OSA. The subjective and objective analysis is important to diagnose the OSA. Epworth sleepiness scale (ESS) is most commonly used for subjective analysis. The ESS is a self reported questionnaire involving eight questions to assess the propensity for day time sleepiness.⁶ Although this cannot reliable distinguish between simple snoring and OSA. A combination of clinical history, Epworth sleepiness scale and body mass index can be used to screen out non apneic snorers.⁷ The objective analysis is laboratory polysomnography, which is gold standard test for OSA.⁸It consists of simultaneous recording physiological parameter related to sleep and wakefulness. The aim of this study is to correlate the clinical characteristics and polysomnographic outcome of OSA patients.

METHODS

This study was performed in dept. of otorhinolaryngology and head & neck surgery, Govt. Stanley medical college, Chennai between January 2017 to December 2017.

Inclusion criteria

The persons whose age between 15 to 65 yrs with snoring and daytime somonolance was included in this study.

Exclusion criteria

The person who have cardiovascular disease, stroke, neuromuscular disorder were excluded.

The access to patient records was approved by the local ethical committee, and patient confidential was maintained. The anthropometric data including age, gender, body mass index and detailed clinical history were recorded. Excessive day time sleepiness was assessed by an ESS score of >10. Total of 60 cases that underwent level I laboratory PSG test were AHI >5 events per hour consider OSA.

All statistical analysis was performed by using IBM. SPSS statistics software 23.0 version. To describe about the data descriptive statistics frequency analysis, percentage analysis were used for categorical variables and the mean and S.D were used for continuous variable. To find the significant difference in the multivariate analysis the one way ANOVA with Turkey’s Post-Hoc test was used. To assess the relationship between the variables Pearson’s correlation was used. In both the above statistical tools the probability value.05 is considered as significant level.

REI is the number of respiratory events per hour. OAI is the number of obstructive apneas per hour. CAI is the number of central apneas per hour. Lowest Desat is the lowest blood oxygen level that lasted at least 2 seconds.

RESULTS

A total of 60 patients were recruited to this study with an age of (36.62±11.908); 58 patients were male and 2 patients were female. AHI parameter according to BMI categories, among the patients recruited to this study BMI was normal in 10%, overweight in 67% and obese in 83.3%. According to AHI mild were identified in 30%, moderate in 25%, severe in 45%. Pearson’s correlation analysis was used to compare BMI and AHI. The analysis showed BMI was positively correlated with AHI (Table 3). The obese individual tended to have severe AHI than normal and overweight individuals (p value 0.054). The AHI parameter according to ESS, the ESS score were normal in 18.3%, mild in 18.3%, moderate in 28.3% and severe in 35%. Pearson study showed the ESS and AHI were positively correlated (Table 4).

**Correlation is significant at the 0.01 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

The average oxygen saturation in our study was 72.35±11.47. We used Pearson’s co-efficient to establish the correlation between AHI and oxygen saturation. The analysis showed AHI had negatively correlated with oxygen saturation (p-0.459) (Table 5). The average heart rate in our study group was 69.90±15.260. When comparing the severity of AHI with heart rate there was a weak positive correlation (p-0.119) (Table 6), suggest that severity of sleep apnea exhibit some effects on heart rate.

DISCUSSION

Patient with OSA have experience of excessive day time sleepiness, impaired concentration, recurring nocturnal awakening and non-restorative sleep with an increased risk of cardiovascular disease, stroke, metabolic syndromes and road traffic accident. This will lead substantial socio-economic implication.⁹In this study, we revealed that most affected population were young adult working male rather than female. This is accordance with previous epidemiological studies by Lurie.¹⁰ In our study who are all overweight and obese person more vulnerable to get OSA. It showed that, when the person increases with the BMI value will reflect the severity of disease in PSG study report. Cheng et al showed that the BMI might be the of indicator the severity of OSA and would be screen for OSA in the general population.¹¹ In this study the participants were those suspected of OSA and included mainly middle aged male compare to elderly people. But it doesn’t mean that the elderly people not affect OSA, it’s due to lack of awareness. Previous study suggested that the prevalence of OSA is increase with age.¹²

The ESS is widely used for clinical evaluation of sleep disordered breathing, because of its simplicity and convenience Damiani et al.¹³ We found that the severity of ESS score was directly affecting the severity of AHI in PSG study. But some other study showed ESS had a large number of false negative results limiting its utility for assessing the severity of disease by PSG study, because the individual who have day time sleepiness may maximize or minimize the symptom.

In this study, most of the participants who had AHI >5 in PSG study consider as OSA without desatuaration of oxygen <5%. Peppard et al suggested OSA may impact large proportion of the population than indicated, because the subject is AHI>5 without oxygen desatuaration.² The heart rate was variable in OSA patient in PSG study. According to Cifcin et al, people with OSA are repeated episode of falling oxygen level lead to variety of physiological changes that affect the heart rate and blood vessels. During this episode heart is stressed which increase the heart rate and blood pressure.¹⁴

CONCLUSION

In this work, OSA is most common disease that affecting the young working population because changes in life style of the society. The clinical evaluation of OSA is best to assessed by BMI and ESS score. It gives the value to assess the severity OSA by PSG study. But we need future study about the classification of symptom, anthropometric measurement scale and sleepiness scale questionnaire according to Indian population.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: The study was approved by the Institutional Ethics Committee

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