Indian Journal of Health Sciences and Biomedical Research KLEU

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 13  |  Issue : 3  |  Page : 235--239

Evaluation of sleep-disordered breathing by level 1 polysomnography in a tertiary care hospital


Anusha Chittapur Madhusoodan, Gajanan S Gaude, Bhagyashri Santosh Patil, Jyothi Hattiholi 
 Department of Respiratory Medicine, J.N. Medical College, KLE Academy of Higher Education and Research, Belagavi, Karnataka, India

Correspondence Address:
Dr. Anusha Chittapur Madhusoodan
Department of Respiratory Medicine, J.N.Medical College, KLE Academy of Higher Education and Research, Belagavi - 590 010, Karnataka
India

Abstract

Background: Sleep Disordered Breathing has become one of the primary causes of mortality in especially obese individuals owing to the modern lifestyle Material and methods: 60 patients coming to outpatient clinic of a tertiary care hospital with Epworth sleepiness scale between 8 to 24 representing increasing levels Results: There were 60 (34 males and 26 females) subjects with mean age 56.45±12.51years and BMI 28.7±3.1. 16, 9, 22 subjects had mild, moderate Conclusions: OSA predominantly affects middle aged men who are overweight with many having high neck circumference. Severe OSA patients had longer.



How to cite this article:
Madhusoodan AC, Gaude GS, Patil BS, Hattiholi J. Evaluation of sleep-disordered breathing by level 1 polysomnography in a tertiary care hospital.Indian J Health Sci Biomed Res 2020;13:235-239


How to cite this URL:
Madhusoodan AC, Gaude GS, Patil BS, Hattiholi J. Evaluation of sleep-disordered breathing by level 1 polysomnography in a tertiary care hospital. Indian J Health Sci Biomed Res [serial online] 2020 [cited 2020 Dec 5 ];13:235-239
Available from: https://www.ijournalhs.org/text.asp?2020/13/3/235/297185


Full Text

 Introduction



Sleep disorder breathing (SDB) is described as a group of disorders with characters of abnormal respiratory patterns such as apnea or hypopneas or inadequate oxygen while asleep.[1] A study by Young et al.,[2] estimated the prevalence of SDB to be at least 6% for adults in the USA, though treatment is available; at least 75% of cases of severe SDB remain undiagnosed. The estimated prevalence of SDB has been observed to be 19.5% and that of obstructive sleep apnoea (OSA) with hypersomnolence was 7.5% by Udwadia et al.[3] With increasing urbanization and changes in lifestyle, sleep-disordered breathing may have a bigger social impact in a developing country like India, which is associated with increased mortality. Hence, our study was conducted to evaluate sleep-discorded breathing patterns, which was diagnosed using Level 1 polysomnography (PSG) study and its correlation of the severity of sleep apnea syndrome with body mass index (BMI).

Aims and objectives

The aim of this study is to evaluate various sleep-disordered breathing patterns by Level 1 PSG study.

To correlate the severity of sleep apnea syndrome with anthropometric and polysomnographic features.

 Subjects and Methods



The study was conducted at a tertiary care hospital and institutional ethical committee approval via Ref.no. MDC/DOME/57: dt : 22/11/2017 was obtained. Sixty patients coming to the outpatient clinic with Epworth sleepiness scale (ESS) between 8 and 24 representing increasing levels of excessive daytime sleepiness along with nocturnal awakening, choking episodes, daytime tiredness, weight gain, morning headaches, irritability, memory loss, personality change, Automobile or work-related accidents, Decreased libido, etc., were included after informed written consent. Patients who already on continuous positive airway pressure (CPAP) therapy, patients on long-term oxygen therapy, chronic debilitated, and uncooperative patients were excluded.

The study period was for 1 year from January 2018 to December 2018. Patients with symptoms of SDB were subjected to detailed history taking and clinical examination. Level I PSG study in a quiet, dark, temperature-controlled room with constant monitoring. The electrocardiogram, central-occipital electroencephalogram, submental electromyogram (EMG), nasal-oral airflow, arterial oxygen saturation, breathing pattern by thermistor, cannula, thoracic wall movements, abdominal movements, anterior tibialis EMG, snoring, body position were variables measured by sleep technician from 10 pm to 6 am. Sleep scoring was done by a sleep technician. PSG recording and sleep stage scoring were done according to the AASM guidelines. The parameters included were sleep onset (usually used for the first 3 consecutive epoch of stage 1 or the first epoch of any stage of sleep), total sleep time (TST: the amount of time spent sleeping in minutes), latency to sleep onset (time from lights out to the first of three continuous epochs of stage 1 or any other stage of sleep in minutes), sleep (the amount of time spent sleeping divided by the total time in bed).

OSA is defined as the presence of repetitive episodes of upper airway obstruction during sleep. Apnea is defined as an event lasting ≥10 s characterized by ≥90% reduction from preevent baseline in oro-nasal thermistor airflow. Apnea-hypopnea index (AHI) of equal to or >5 events/h is commonly used, with an obstruction or mixed events having 50% of the total.

OSA is usually classified according to AHI as:

Mild is 5–15 events/hModerate is 15–30 events/hSevere is >30 events/h.[4]

Statistical methods

Descriptive and inferential statistical analysis has been carried out in the present study. Results on continuous measurements are presented on mean ± standard deviation (min–max), and results on categorical measurements are presented in number (%). Significance is assessed at 5% level of significance.

Analysis of variance has been used to find the significance of study parameters between three or more groups of patients, Student t-test (two-tailed, independent) has been used to find the significance of the study parameters on continuous scale between two groups (intergroup analysis) on metric parameters. Pearson correlation between study variables is performed to find the degree of relationship, Pearson correlation coefficient ranging between − 1 and 1, −1 being the perfect negative correlation, 0 is the no correlation, and 1 means perfect positive correlation.

Significant figures:

+ Suggestive significance (P value: 0.05 < P < 0.10)Moderately significant (P value: 0.01 ≤ P ≤ 0.05)** Strongly significant (P value: P ≤ 0.01)

 Results



Our cohort had predominantly middle-aged male subjects with higher BMI and neck circumference. History of accidents was found in 10% of participants. The most common comorbidities were hypertension, diabetes mellitus, and ischemic heart disease. Patients mostly presented with snoring, daytime sleepiness, and weight gain.

In this study [Table 1], mean BMI 29 ± 2.5 was maximum in severe OSA patients. Mean Epworth score 17.1 ± 5.8 was maximum in moderate OSA patients. Maximum Respiratory desaturation index (RDI) 63.9 ± 26.6 was seen in severe OSA patients. In severe OSA patients, the mean Mallampatti score was 2.7 ± 1 [Table 2], whereas a maximum score of 3.1 ± 0.9 was seen in moderate OSA participants.{Table 1}{Table 2}

In this study [Table 3], the least amount of sleep time was seen in severe OSA patients, i.e., about 296 min in which TST was around 219 min. Mean sleep efficiency was 71 ± 22.2%, severe OSA patients had 69.9 ± 22.2%. The time in bed with O2 saturation <90% was least seen in moderate OSA subjects, i.e., around 19.6 ± 24.5%. REM AHI was maximum in moderate OSA subjects, i.e., 36 ± 16.2, NREM AHI maximum in severe OSA subjects, i.e., 59.4 ± 31.9 [Table 3].{Table 3}

 Discussion



Sleep-disordered breathing (SDB) is correctable but often an underdiagnosed condition. SDB has been associated with various cardiovascular diseases, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, stroke, and other morbidity. Obesity is an important precipitating factor. Overnight PSG has been the gold standard in the diagnosis of sleep-disordered breathing. Studies have demonstrated that CPAP is the most effective medical treatment, used for about 12–15 h/day. This along with lifestyle modifications, has been proven to show tremendous improvement in mortality and morbidity.[5],[6]

Obesity is one of the foremost risk factors in the development of SDB. The study cohort showed that >50% of the overweight individuals had OSA. In a hospital-based study,[7] higher BMI was found in OSA, but it did not correlate with the severity of OSA. While another study in Indian urban men, a linear correlation was found with increasing BMI and obesity.[3]

Neck circumference, a measure of central obesity, a mean of 37.63 ± 3.05, was noted in our study. It was similar, as reported by Udwadia et al.[3] and Pradeep kumar VG et al.[7] Higher neck circumference and BMI in OSA syndrome (OSAS) patients were associated with aerobic capacity, physical inactivity, and excess body fluid.[8],[9]

Mallampatti score has also been one of the predictors of OSA severity. In our cohort, 27.3% of subjects with a score of 4 had severe OSA. An Indian study by Shyamala and Khatri [10] showed that an increase in OSA severity was associated with increased Mallampatti score.

OSA patients are frequently observed with various comorbid conditions such as diabetes mellitus, hypertension, stroke, cerebrovascular accident (CVA), depression, and obesity. It is noticed that obesity and OSA, each are potently linked to hypertension. Our results showed 71.7% of OSA patients were hypertensive.[8] Smith et al.[11] had a similar prevalence of hypertension in OSA patients.

In our cohort, excessive daytime sleepiness was the one the frequent complaint that accounted for 73.3% of the study population, followed by snoring and choking episodes. The findings were similar to a cluster analysis study by Ye et al.[12] Johns proposed the ESS, an objective measure of daytime sleepiness as an instrument to differentiate a primary snorer from OSAS, dating back to 1993.[13] The AHI–EDS association has conflicting results in literature with some revealing strong co relation,[14],[15] while others show weak correlation.[16],[17] Our cohort had a positive correlation, indicating that AHI can be a diagnostic tool in predicting OSA severity, similar to other Indian studies.[3],[8]

Sleep characteristics such as sleep efficiency, REM and NREM AHI, nocturnal desaturation, TST, and time in bed were comparable with OSA severity. In our cohort, it was found that the overall sleep efficiency of the patients was about 71%. Lowest sleep efficiency (69.9%) was found in moderate OSA subjects. Patients with severe OSA had a mean AHI of 32. The study also showed that maximum nocturnal desaturation was found in the severe OSA group. TST in severe OSA patients was 356 min, whereas in normal subjects, it was 430 min. There were around 137 arousals in severe OSA patients. REM sleep duration was analogous, greater reduction of slow-wave sleep in patients with severe OSA. In this study, REM AHI was found to be highest in the moderate OSA group. Bianchi et al.[18] concluded that OSA accelerated the decay rate of REM and NREM sleep manifesting as shorter sleep bouts with an increased number of sleep transitions. Another study,[19] which compared sleepy with nonsleepy patients, showed that shorter sleep latency and slower deep wave sleep was seen in EDS subjects. From the above study, it can be concluded that sleepy patients have a worse sleep-related breathing parameters with a lighter and fragmented pattern than nonsleep patients. Since the number of subjects without EDS was less in our cohort, its association with slow-wave sleep shortening could not be assessed.

Sleep characteristics such as sleep efficiency, REM and NREM AHI, nocturnal desaturation, TST, and time in bed were comparable with OSA severity. In our cohort, it was found that the overall sleep efficiency of the patients was about 71%. Lowest sleep efficiency (69.9%) was found in moderate OSA subjects. Patients with severe OSA had a mean AHI of 32. Our study also showed that maximum nocturnal desaturation was found in the severe OSA group. TST in severe OSA patients was 356 min, whereas in normal subjects, it was 430 min. There were around 137 arousals in severe OSA patients. REM sleep duration was analogous, greater reduction of slow-wave sleep in patients with severe OSA. In our study, REM AHI was found to be highest in the moderate OSA group. Bianchi et al.[18] concluded that OSA accelerated the decay rate of REM and NREM sleep manifesting as shorter sleep bouts with an increased number of sleep transitions. Another study,[19] which compared sleepy with nonsleepy patients, showed that shorter sleep latency and slower deep wave sleep was seen in EDS subjects. From the above study, it can be concluded that sleepy patients have a worse sleep-related breathing parameters with lighter and fragmented patterns than nonsleep patients. Since the number of subjects without EDS was less in our cohort, its association with slow-wave sleep shortening could not be assessed.

 Conclusion



This study throws light over the prevalence of sleep-disordered breathing in the rural population of the Northern part of Karnataka. Our study revealed that OSA is more prevalent in middle-aged men with risk factors like alcohol consumption, diabetes mellitus, hypertension, coronary artery diseases, CVA, stroke, etc. They tend to be overweight with increased neck circumference and most commonly present with EDS, indicating the each of the above-mentioned factors can be a determinant of OSA severity and sometimes can be fatal. CPAP aids in lessening the morbidity, mortality, and thereby improve the quality of life. Awareness about this disease, identifying the red flag signs with prompt usage of positive airway pressure devices, may reduce the disease burden.

Financial support and sponsorship

Ethical approval was obtained from Arsi University Ethical Review Committee Ref.no. COHS/R/0036/2019/20.

Conflicts of interest

There are no conflicts of interest.

References

1Jung R, Kuhlo W. Neurophysiological studies of abnormal night sleep and the Pickwickian syndrome. Prog Brain Res 1965;18:140-59.
2Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ, et al. Sleep disordered breathing and mortality: Eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071-8.
3Udwadia F, Doshi AV, Lonkar SG, Singh CI. Prevalence of sleep-disordered breathing and sleep apnea in middle-aged urban Indian men. Am J Respir Crit Care Med 2004;169:168-73.
4Berry RB, Brooks R, Gamaldo CE. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Version 2.0.2. Darien, Illinois: American Academy of Sleep Medicine; 2013.
5Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc 2008;5:136-43.
6Giles TL, Lasserson TJ, Smith BJ, White J, Wright J, Cates CJ. Continuous positive airways pressure for obstructive sleep apnoea in adults. Cochrane Database Syst Rev 2006;(1):CD001106.
7Sreedharan SE, Agrawal P, Rajith RS, Nair S, Sarma SP, Radhakrishnan A. Clinical and polysomnographic predictors of severe obstructive sleep apnea in the South Indian population. Ann Indian Acad Neurol 2016;19:216-20.
8Hasan A, Uzma N, Swamy TL, Shoba A, Kumar BS. Correlation of clinical profiles with obstructive sleep apnea and metabolic syndrome. Sleep Breath 2012;16:111-6.
9Pradeep Kumar VG, Bhatia M, Tripathi M, Srivastava AK, Jain S. Obstructive sleep apnoea: A case-control study. Neurol India 2003;51:497-9.
10Shyamala KK, Khatri B. Polysomnographic spectrum in obstructive sleep apnea scholars. J Appl Med Sci 2016;4:2074-83.
11Smith PL, Gold AR, Meyers DA, Haponik EF, Bleecker ER. Weight loss in mildly to moderately obese patients with obstructive sleep apnea. Ann Intern Med 1985;103:850-5.
12Ye L, Pien GW, Ratcliffe SJ, Björnsdottir E, Arnardottir ES, Pack AI, et al. The different clinical faces of obstructive sleep apnoea: A cluster analysis. Eur Respir J 2014;44:1600-7.
13Johns MW. Daytime sleepiness, snoring, and obstructive sleep apnea. The Epworth sleepiness scale. Chest 1993;103:30-6.
14Roure N, Gomez S, Mediano O, Duran J, Peña Mde L, Capote F, et al. Daytime sleepiness and polysomnography in obstructive sleep apnea patients. Sleep Med 2008;9:727-31.
15Hesselbacher S, Subramanian S, Allen J, Surani S, Surani S. Body mass index, gender, and ethnic variations alter the clinical implications of the Epworth sleepiness scale in patients with suspected obstructive sleep apnea. Open Respir Med J 2012;6:20-7.
16Sauter C, Asenbaum S, Popovic R, Bauer H, Lamm C, Klösch G, et al. Excessive daytime sleepiness in patients suffering from different levels of obstructive sleep apnoea syndrome. J Sleep Res 2000;9:293-301.
17Mediano O, Barceló A, de la Peña M, Gozal D, Agustí A, Barbé F. Daytime sleepiness and polysomnographic variables in sleep apnoea patients. Eur Respir J 2007;30:110-3.
18Bianchi MT, Cash SS, Mietus J, Peng CK, Thomas R. Obstructive sleep apnea alters sleep stage transition dynamics. PLoS One 2010;5:e11356.
19Oksenberg A, Arons E, Nasser K, Shneor O, Radwan H, Silverberg DS. Severe obstructive sleep apnea: Sleepy versus nonsleepy patients. Laryngoscope 2010;120:643-8.