Associate Consultant, Department of General Medicine, Aster Medcity, Kochi, Kerala—India

Dr Gladson C.J

IMT PG Resident, Department of General Medicine, Aster Medcity, Kochi, Kerala—India

Dr Jaisy James

Lead Consultant, Department of General Medicine, Aster Medcity, Kochi, Kerala—India

Dr Geetha Mary Philips

Consultant, Department of General Medicine, Aster Medcity, Kochi, Kerala—India

Dr Anil N.X

NREM

Insomnia is one of the major problems that cause accidents in job site and road traffic accidents. Also it is one of the main illnesses that physicians encounter. So it is mandatory to know prevalence of insomnia in out patients. 
Aim: To study the prevalence of insomnia and different types of sleep pattern in the outpatient clinic of internal medicine department in a quaternary care hospital in South India.


Introduction

A questionnaire according to Athens insomnia scale will be given to 800 patients in medical OPD of Aster Medcity hospital Kochi, with exclusion criteria. Questionnaires are tabulated and analyzed to reach a possible conclusion.
Results: The prevalence of insomnia in our studied population is 57% (460 members). The patients with more insomnia are seen in the 40-60 age groups. Late nit mobile users had 3 times risk of insomnia compared to normal population. Insomnia increases accidents in worksite. Mean sleep duration is reduced in insomnia. Most of the population have biphasic sleep pattern.


Methods And Materials

The prevalence of insomnia in our studied population is 57% (460 members). The patients with more insomnia are seen in the 40-60 age groups. Late nit mobile users had 3 times risk of insomnia compared to normal population. Insomnia increases accidents in worksite. Mean sleep duration is reduced in insomnia. Most of the population have biphasic sleep pattern.

Results

Insomnia is seen nearly equal in both sexes. Late night mobile/ computer use and daily smoking have an increased risk of having insomnia than the normal population. So cessation of late night use of mobiles can decrease the incidence of insomnia. Insomnia can cause an increased incidence of work site accidents. So we have to decrease the incidence of insomnia to decrease accidents Road traffic accidents and insomnia is controversial in our study. Most of the people in our study have a monophasic sleep pattern. The duration of sleep in our population is less than that of the recommended duration of sleep.

Conclusion

Disturbed sleep patterns and insomnia are among the most frequent health complaints that physician encounters.  Various studies noted that the symptoms of insomnia were present in about 33-50% of the population while higher rates were seen among divorcees or widowed people, older age, female general population and presence of comorbid or psychiatric illness[2]. About 30% of adults complain of acute insomnia and 10% have chronic insomnia. In a study conducted in South India found a prevalence of insomnia as 18.6% among healthy adults [6]. In developed countries like the United States, more than 50% of adults report consistently experience at least intermediate sleep disturbances[1]. Only 30% of the population is obtaining at least a sufficient amount of sleep [1].  The Institute of Medicine has estimated that 50 -70 million Americans suffer from a chronic disorder of sleep and wakefulness which can adversely affect daytime functioning as well as physical & mental health

Normal nocturnal sleep in adults displays a consistent organization from night to night. After sleep onset, sleep usually progresses through NREM stages N1–N3 sleep within 45–60 min. NREM stage N3 sleep (also known as slow-wave sleep) predominates in the first third of the night and comprises 15–25% of total nocturnal sleep time in young adults. Sleep deprivation increases the rapidity of sleep onset and both the intensity and amount of slow-wave sleep. The first REM sleep episode usually occurs in the second hour of sleep. NREM and REM sleep alternate through the night with an ave age period of 90–110 min (the "ultradian" sleep cycle). Overall, in a healthy young adult, REM sleep constitutes 20–25% of total sleep, and NREM stages N1 and N2 constitute 50–60%. Age has a profound impact on sleep state organization. N3 sleep is most intense and prominent during childhood, decreasing with puberty and across the second and third decades of life. N3 sleep declines during adulthood to the point where it may be completely absent in older adults. The remaining NREM sleep becomes more fragmented, with many more frequent awakenings from NREM sleep. It is the increased frequency of awakenings, rather than a decreased ability to fall back asleep, that accounts for the increased wakefulness during the sleep episode in older people. While REM sleep may account for 50% of total sleep time in infancy, the percentage falls off sharply over the first postnatal year as a mature REM-NREM cycle develops; thereafter, REM sleep occupies about 25% of total sleep time. Sleep deprivation degrades cognitive performance, particularly on tests that require continual vigilance. Paradoxically, older people are less vulnerable to the neurobehavioral performance impairment induced by acute sleep deprivation than young adults, maintaining their reaction time and sustaining vigilance with fewer lapses of attention.   

 The word insomnia originates from Latin words 'in'(no) and 'somnus'(sleep)[2]. The international classification of sleep disorders defines insomnia as difficulty with initiation, maintenance, duration or quality of sleep that results in the impairment of day time function, despite adequate opportunity and circumstances for sleep [3]. It is a sleep disorder that occurs acutely and may become chronic if not treated in time. The presenting complaints are often that of difficulty in falling asleep in the bed, waking up during the night and having trouble in going back to sleep, waking up too early in the morning and or not having a fresh sleep

According to the National Sleep Foundation, insomnia can be categorized into many types.

It is a brief episode of difficulty in sleeping caused by a stressful life event, such as hearing a bad news. Here insomnia resolves without any treatment

Acute insomnia

If a person has trouble falling asleep or staying asleep at least 3 nights/ week for three months or longer

Chronic insomnia

It is the difficulty in falling asleep at the beginning of the night

Onset insomnia

People with maintenance insomnia wake up during the night and have difficulty in returning to sleep

	Maintenance insomnia

Insomnia associated with comorbid conditions

Comorbid insomnia

 Chronic insomnia represents a more complex condition than acute which accompanies day time impairment of cognition, mood, performance with impact on the patient, family, coworkers[2]. They also make errors and accidents at work and fatal road traffic accidents.  20% of all accidents that cause deaths are due to drowsy drivers[1]. Chronic insomnia has a risk to develop medical and psychiatric comorbidities. So it is mandatory to evaluate sleep pattern and insomnia in outpatients. 

A healthy young adult has to sleep 7-8 hours/night[1]. Timing, duration and internal structure of sleep vary among individuals. Sleep patterns can be mainly divided into three types.

In this kind of sleep pattern person has one episode of sleep per 24 hours which is mostly at night. 

Monophasic

Here a person has two episodes of sleep, one in the afternoon and another at night

Biphasic

Here person has more than two episodes of sleep

Multiphasic

In some persons, it may be early night sleeping with early morning awakening (go to bed at 8-9pm and awakens at 4-5pm). Some others are more nocturnal, seen in urban areas, which go to bed at late night (11pm-12am) and late morning (6-8am). The pattern of sleep considerably changes over the life span as infants and young have a longer duration of sleep than the older one. So it is mandatory to assess the sleep pattern of the patient.

To study the prevalence of insomnia and different types of sleep pattern in  the outpatient clinic of internal medicine department in a quaternary care hospital in South India.

To find out the relationship between age and insomnia.

To find out the relationship between gender and insomnia.

To categorize sleep patterns of the patients.

To assess the effect of technologies in causing insomnia.

To find out the consequence of insomnia in worksite.

1)	Study area: Outpatient clinic of the Internal Medicine Department Of Aster Medcity Hospital, Kochi, Kerala 

2)	Study Population:Inclusion criteria includes all outpatients who come in the internal medicine department of Aster Medcity Hospital more than 18 years of age

Patients on long term psychiatric medications

Patient with COPD and history of orthopnea

Patients with diagnosed sleep apnea syndrome

Patients with diagnosed cirrhosis of the liver

Patients diagnosed with chronic kidney disease on dialysis

4)	Study design: It is a type of descriptive study to know the prevalence of insomnia in an unknown population. It is a cross-sectional descriptive study were we  are taking a snapshot of the study population for the prescribed time period.

)	Methods of measurement: 
This is a descriptive cross-sectional study in which the method going to use is by preparing a questionnaire including personal details of the patient, the questions revealing sleep pattern of the patients, then the questions about insomnia, to categorize insomnia and consequences of insomnia and other factors that affect insomnia. The questions for exclusion criteria will be present in a questionnaire to exclude them


7)	Data collection Method:
A questionnaire based on sleep pattern and the Athens insomnia scale in English and Malayalam will be given to the outpatients who are more than 18 years old coming in the internal medicine outpatient department. Questions in the questionnaire can be divided into 4 categories.


Personal details and questions about exclusion criteria

According to the Athens insomnia scale, if the patient had Athens insomnia score more than or equal to 6, he had insomnia. Thus we can classify patients with insomnia from the patients without insomnia. 

The sample size is calculated using formula
n= z2pq/d2     
Where n is sample size, z = 1.96, p is prevalence in previous studies, q is 1-p and d is sampling error which is considered as 3%.
Here the prevalence from previous south Indian study was 33%[13]. By using this formula, the sample size will be nearly 800.


Sample size

The information from the questionnaire were be tabulated and analyzed by using Microsoft Excel and IBM SPSS Statistics 2d version. According to Athens criteria, the classification between the patient with insomnia and the patient without insomnia was made.  All categorical variables are described as frequency and percentage. All continues variable is described as mean and standard deviation and compared using independent sample t-test. The Pearson Chi-square test is used to find the association between the categorical variable. P value <0.05 was considered to be statistically significant.

Other statistical methods

The present study reveals the prevalence of insomnia in the adult population in an outpatient clinic of internal medicine department in a multispecialty hospital. A similar study conducted in South India had found a prevalence of 18.6% among healthy adults[6].In another study on the Indian population in West Bengal, it was found that 43.2% of patients who had insomnia never seek medical advice for insomnia and only 15.3% consulted a doctor for their sleep problem.

Another international survey of sleep disorders in the general population found that many individuals with insomnia (47%–67%) did not seek medical attention for their sleep difficulties. Among those who sought medical help, 50%–90% received treatment. Thus, insomnia remains largely underdiagnosed and undertreated problem.

Another study done in Latin American countries using Athens Insomnia Scale in middle-aged women showed 56.6% of surveyed women suffered from either insomnia, poor sleep quality, or both.

A study done in the US showed that, in patients attending primary care clinics, more than 50% complained of insomnia, only if specifically asked about it. 30% visit their general practitioners on their own initiative, and only 5% go to consultation with the main objective of receiving treatment to solve their problem.

A study on Malaysian patients attending primary care clinics showed that 38.9% of patients had frequent insomnia symptoms (>3 times/week), 30.7% had chronic insomnia without daytime consequences, and 28.6% had chronic insomnia with daytime dysfunction. Indian ethnicity, age ≥50, anxiety symptoms, and depression symptoms were risk factors for chronic insomnia with daytime dysfunction.

A survey among the adult French population in 2001–2002 regarding insomnia found that 25% of randomly selected respondents were dissatisfied with their sleep and only 13% had consulted a health-care provider, especially for insomnia.

In another study conducted in South India, the prevalence of chronic insomnia was found to be 33% with a statically significant correlation with increasing age and diabetes.

In a study conducted among adolescents in Hong Kong, long hours of mobile phone use were correlated with short sleep duration, poor sleep quality, and excessive daytime sleepiness.[15] Another study in Japanese high school students reported that long hours of mobile phone use was associated with short sleep time and fatigue.

The association between insomnia symptoms or syndromes and accidents is still controversial[17], and is often limited to the debate concerning the potential side effects of hypnotics on driving ability. Some other studies showed an association of insomnia in accidents
In our study also we are including a patient with acute illness and excluded patients with a chronic disorder that causes insomnia. So the prevalence of the study will be a reflection of the prevalence of insomnia in a normal population.


Also, no recent studies are done in Kerala for the prevalence of insomnia. So this study reflects the prevalence of insomnia of Kerala state.

According to our study, there are 800 patients as sample size. Out of them, 508 members were female and 292 were male (Fig. 1). The prevalence of insomnia in our studied population is 57% (460 members).

The prevalence of insomnia in the female population is 56.7% and of the male population is 58.9 %. and the p-value is 0.553 which is not significant (Fig. 2).

The study population is divided into 3 age groups.

Out of 265 members in below 40 years of age group, 167 patients have insomnia and it is about 36. 3 % of total insomnia patients.

Out of 316 members in 40-60 years age group, 176 patient have insomnia and it is about 38.3% of total insomnia patients

Out of 219 members in the above 60 year age group, 117 patients had insomnia and it is about 25.4 % of insomnia.

From the above analysis, the patient with more insomnia is seen in the 40-60 age group (Table 1).

Late night Mobile/ computer use VS Insomnia:

Out of 266 people who use mobile at night, 72.6 % of people have insomnia. This study is significant with P value of less than 0.05. So late night mobile use is a cause for insomnia according to our study. 
The odds ratio is 3.06	
According to our study, late night use of mobiles can cause 3 times more risk of having insomnia comparing to others (Table 2, Fig. 3).


In the members who had an accident in worksite due to sleepiness(44 members), 65.9% of patients have insomnia. This analysis has a p-value of less than 0.05, which is significant. So insomnia can increase accidents in worksite (Table 3, Fig. 4).

In patients who had Road traffic accidents, only 36.7 % of patients have insomnia. So in our study insomnia is not a risk for road traffic accidents (Fig. 5).

In our population average duration of sleep in people without insomnia is 6.53±0.79.  The average duration of sleep in people with insomnia is 5.56±0.78. This study is statistically significant with p value<0.001 (Fig. 6).

By analyzing duration in sleep in different age groups, Average sleeping hour of patients with age less than 40 years is 5.83 hours and that off patients with age between 40-60 is 5.92 hours. Patients with age more than 60 have more sleeping hours than others with 6.22 hours. This study is statistically significant with P value of less than 0.05 (Fig. 7).

Out of 800 patients, 705 patients are following a monophasic pattern. Only 95 patients following a biphasic pattern of sleep. None of them is following a multiphasic pattern. So most of them are following a monophasic pattern of sleep. Mostly job and work in the house attributed to monophasic  pattern  of sleep (Fig. 8).

The prevalence of insomnia in our study is 57%. A similar study conducted in South India had found the prevalence of 18.6% among healthy adults [6] and another study on the Indian population in West Bengal, it was found that 43.2% of patients who had insomnia never seek medical advice for insomnia. Another south Indian study showed a prevalence of 33% among the normal population

But international studies almost correlate with the prevalence of our study.  A study done in Latin American countries using Athens Insomnia Scale and other sleep scales in middle-aged females showed 56.6% of surveyed women suffered from insomnia [9]. In our study also, the prevalence of insomnia in women is 56.7% which is correlating with the above study. A study done in the US showed that, in patients attending primary care clinics, more than 50% complained of insomnia, only if specifically asked about it

In our study, the prevalence of insomnia is more in the middle age group comparing to the younger and older age group. In our study, 38.3 % of total insomnia is falling under the age group 40-60 years. Work stress and family stress may be the reason for insomnia in this age group. In other studies, insomnia increases as age advances[1]. So in the Indian population, Expenditure of a family increases as the children in the family grew up for education, food, clothes, and every commodity. So there is a chance of getting mental stress thinking about expenditure for both father and mother who can lead to insomnia in this age group.

In our study, the use of late-night mobile/ computer use can attribute to increasing the incidence of insomnia. In our study persons who are using a mobile phone in the late night had a 3 fold risk of getting insomnia than the normal population. In a study conducted among adolescents in Hong Kong, long hours of mobile phone use were correlated with short sleep duration, poor sleep quality, and excessive daytime sleepiness[15]. Another study in Japanese high school students reported that long hours of mobile phone use was associated with short sleep time and fatigue[16]. Our study is also correlating with other studies in the case of mobile use. So, to avoid late night mobile use is a good practice to get good sleep and to decrease the incidence of insomnia.

Accident at the worksite, Road traffic accident, and Insomnia

In our study, In patients who had an accident at work site 63% had insomnia, which is statistically significant. So insomnia can cause an increase in accidents at the worksite. But road traffic accidents are not correlating with insomnia in our population. In other studies also, the association between insomnia symptoms or syndromes and accidents is still controversial[17], and is often limited to the debate concerning the potential side effects of hypnotics on driving ability. Some other studies showed an association of insomnia in accidents.

In our study, the mean duration of sleep in the normal population is 6.53 hours and that of people with insomnia is 5.56 hours. The duration of sleep needed for a normal man is 7-8 hours[1]. So the mean duration of sleep in our normal population is less than that of the normal duration of sleep advised. When the age increases the duration of sleep will decrease in the normal population[1]. Surprisingly, in our population duration of sleep increases while aging increases.

Athens Insomnia scale is a subjective scale. So here the definition of insomnia is taken on a subjective basis. Since other confirmatory sleep studies will increase the cost and time to be spent by the patient, this is the most acceptable tool. Patient to patient variability will be present.
The method we adopted is a questionnaire method. so there can be errors while answering questionnaire.


Many patients would  not be ready to correctly answer the question since it includes details about addictions and mobile use. Not many studies were done to correlate mobile use and accidents.

Since it is a questionnaire method, a large sample size is obtained It is a cost-effective method. We will get an idea of prevalence in Kerala population.

The prevalence of insomnia in our studying population is 57% is more than half the population. These are the cases that came to the Outpatient clinic for other acute illnesses. So prevalence will be more than what we see above. The problem is most of them are not taking medical attention for insomnia.

Insomnia is seen nearly equal in both sexes.

Late night mobile/ computer use and daily smoking have an increased risk of having insomnia than the normal population. So cessation of late night use of mobiles can decrease the incidence of insomnia.

Insomnia can cause an increased incidence of work site accidents. So we have to decrease the incidence of insomnia to decrease accidents.
Road traffic accidents and insomnia is controversial in our study.
Most of the people in our study have a monophasic sleep pattern.
The duration of sleep in our population is less than that of the recommended duration of sleep.


Charles A Czeisler(ed.), Thomas E Scammel(ed.), Clifford B. Saper(ed.). Sleep Disorders. Harrison’s Principles Of internal Medicine. 19 thedition 2015USA: McGraw Hills Education; 2015.

SahooSaddiccha. Diagnosis and treatment of chronic insomnia. Annals of Indian academy Of Neurology. 2010; 13(2): 94-102.

	Arup Kumar Misra, Pramodkumar Sharma. Pharmacotherapy of insomnia and current updates. Journal of association of Physicians of India. 2017:65:43.

National Sleep Foundation. What are the different types of insomnia? Available from: https://sleepfoundation.org/insomnia/content/what-aredifferent-types-insomnia[ Accessed on 23/05/2017]

K.Park. Preventive and social medicine, 12 th edition Jabalpur:bhanot;2011

Panda S, Taly AB, Sinha S, Gururaj G, Girish N, Nagaraja D. Sleep-related disorders among a healthy population in South India. Neurology India 2012;60:68-74. 

Léger D, Poursain B, Neubauer D, Uchiyama M. An international survey of sleeping problems in the general population. Current Medical Research and Opinion 2008;24:307-17. 

Roy SK, Bhattacharjee AK, Chakraborti C, Singh R. Prevalence of insomnia in the urban population of West Bengal: A community-based cross-sectional study. International Journalof Medicine and Public Health 2015;5:293-6. 

Blümel JE, Cano A, Mezones-Holguín E, Barón G, Bencosme A, Benítez Z, et al.A multinational study of sleep disorders during female mid-life. Maturitas 2012;72:359-66.  

Smith MT, Perlis ML, Park A, Smith MS, Pennington J, Giles DE, et al.Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. American Journal of Psychiatry 2002;159:5-11.  

Zailinawati AH, Mazza D, Teng CL. Prevalence of insomnia and its impact on daily function amongst Malaysian primary care patients. Asia Pacific Family Medicine 2012;11:9.  

Morin CM, LeBlanc M, Daley M, Gregoire JP, Mérette C. Epidemiology of insomnia: Prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors. Sleep Medicine 2006;7:123-30.  

SwapnaBhaskar, D. Hemavathy, Shankar Prasad. Prevalence of chronic insomnia in the adult patient and its correlation with medical comorbidities. Journal of family medicine and primary care 2016:4(5);780-784

Paul Jordan. The different types of sleep patterns:-Polyphasic, biphasic and monophasic. Sleep habits. Available from https://sleephabits.net/sleep-patterns

Mak Y.W., Wu C.S., Hui D.W., Lam S.P., Tse H.Y., Yu W.Y., Wong H.T. Association between screen viewing duration and sleep duration, sleep quality, and excessive daytime sleepiness among adolescents in Hong Kong. Int. J. Environ. Res. Public Health. 2014;11:11201–11219. doi: 10.3390/ijerph111111201 

M. A. Schuckit and L. I. Bernstein, Sleep time and drinking history: a hypothesis, The American Journal of Psychiatry, vol. 138, no. 4, pp. 528–530, 1981

Kucharczyk, E. R., Morgan, K. and Hall, A. P. The occupational impact of sleep quality and insomnia symptoms. Sleep Med. Rev., 2012, 16: 547–559

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272091897fig%201%20-2.jpg?alt=media&token=8becc5a6-7c26-4bb1-9f01-ca2a974e831b

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272091754fig%203.jpg?alt=media&token=0fe673c0-0443-4ea1-846e-096d0126d2be

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272091894fig%204%205.jpg?alt=media&token=5077e650-c2b1-44e6-a910-1a24285fb39b

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272091901fig%206%207.jpg?alt=media&token=70ca964d-3b06-49c7-babb-f1d1512e1531

A Cross-Sectional Study: Sleep Pattern and Insomnia in the Outpatient Clinic of a Multispecialty Hospital in South India

#111827

DNB Trainee, Aster Medcity, Aster Medcity, Kochi, Kerala—India 

Dr Linu Mary John

Senior Consultant, Department of ENT, Aster Medcity, Kochi, Kerala—India

Dr Feroze Khan

Lead Consultant, Department of ENT, Aster Medcity, Kochi, Kerala—India

Dr Vineeth Viswam

Obstructive sleep apnoea syndrome; lowest desaturation; AHI; BMI; comorbidities;
endoscopy; sleep study


Obstructive sleep apnoea syndrome (OSAS) is characterized by partial or complete recurrent upper airway obstruction during sleep, resulting in periods of apnoea, oxyhaemoglobin desaturation, and frequent night awakenings with excessive daytime sleepiness therby reducing performance at work and in social activities in many cases. The identification of co-morbidities in OSAS patients is necessary to define the clinical picture and consequently to find the more adequate treatment. However, as the onset of co-morbidities is influenced by the level of OSAS severity and duration, this relationship needs
to be well defined. This study addresses the association between OSAS and co-morbidities as a function of OSAS severity and duration, considering it as factors that could influence the morbid condition. 
The study will help in providing individualised care to OSA patients with due importance to early identification and treatment of OSAS, thereby preventing the occurrence and reducing the severity of associated comorbidities. Knowledge about the sites of collapse will help in making tailor made approaches for the effective management of obstructive sleep apnoea.


Obstructive sleep apnoea syndrome (OSAS) is a sleep disorder characterised by intermittent, complete and partial airway collapse resulting in frequent episodes of apnoea and hypopnoea. Association of OSA with serious morbidities have shown that untreated OSA is a substantial but underappreciated public health threat. Population based epidemiological studies have uncovered the high prevalence and wide severity spectrum of undiagnosed OSA and have consistently found that even mild OSA is associated with significant morbidity. The factors affecting severity of OSA include endoscopic characteristics of different sites of airway, age, sex, BMI, thyroid disorder, alcoholism, smoking etc.

About 1 billion people out of the total world adult population of 7.3 billion is estimated to be suffering from OSAS. However, there is gross disparity in the diagnosis and treatment of OSAS. Untreated OSAS can result in significant morbidity and mortality in the long run. It is associated with significantly reduced quality of life (QoL) and depression. There is also an increased need for clinical consultations and health care services. The increased day time sleepiness and compromised vigilance leads to reduced work performance, decreased productivity, absenteeism and workplace and motor vehicle accidents, resulting in unemployment and underemployment.

The identification of comorbidities in OSAS patients is necessary to define the clinical picture and consequently to find the more adequate treatment. However, as the onset of comorbidities is influenced by the level of OSAS severity and duration, this relationship needs to be well defined.

To determine if the severity of OSA (determined by AHI and lowest desaturation) along with its duration (years), has any role in the development of comorbidities-hypertension, diabetes mellitus, dyslipidemia and stroke (number of comorbidities in a person).

Correlation of flexible endoscopic evaluation of sites of collapse at nose, palate, tongue base and larynx (number of sites and pattern of collapse) on the severity of OSA (AHI and lowest desaturation).

The study was conducted in the ENT department of Aster Medcity, Kochi. Patients aged 18 and above reporting to ENT OP with complaints of snoring and increased day  time sleepiness satisfying Epworth Sleepiness Score > 10 and polysomnographic finding of AHI greater than 5 were considered under inclusion criteria. The study was designed to be a prospective study and was conducted for a duration of 8 months from May 2023 to December 2023. with 95% confidence level and 10% allowable error (precision) the minimum sample size required for the present study was calculated to be 87. All the patients underwent sleep study and flexible fibreoptic nasopharyngo-laryngo-scopic examination and the findings were noted.

Descriptive statistics was used to assess the baseline characteristics of the data obtained from a questionnaire. All quantitative variables presented as mean and standard deviation and qualitative variables in frequency and percentages with diagrams and graphs. The association of severity of OSAS with comorbidities was evaluated using Chi-square test, point biserial and Pearson correlation test using SPSS tool.

Out of 100 patients enrolled in the study, 15 patients were above the age of 61, 75 patients were between the age of 31-60 and the remaining 10 were below the age of 30.

The youngest patient was 19 years old and the oldest patient was 85 years old. The Mean age was 46.29 years with Standard Deviation (SD) of 13.49.

AHI, Lowest Desaturation and Duration of snoring and BMI Distribution

Out of 100 patients, 32 patients (32%) reported to have Mild: AHI (5-15 per hour), 25 patients (25%) reported to have Moderate: AHI (15-30 per hour), and 43 patients (43%) reported to have Severe: AHI (> 30 per hour). 7 patients were reported to have lowest desaturation between 40-55%, whereas majority of the patients (40%) of the patients were reported to have lowest desaturation in range of 70-85%.

Majority of patients (40) were reported to have duration of snoring less than 5 years whereas the number of patients who are reported to have duration of snoring between 5 – 10 years is 26 and the number of patients who are reported to have duration of snoring between both 10 to 15 years and greater than 15 years are 17 respectively.

Most of the patients (45) were reported to have overweight whereas the number of patients were obese were totally 43 with 30 patients classified as Obese-1 and 13 patients as Obese-2. The number of patients who are extremely obese are 2 patients.

Only one person presented with stroke and the patient had other comorbidities of hypertension, diabetes mellitus and dyslipidemia also.

Prevalence of comorbidities in  study population

The most common comorbidity is hypertension occurring in 60% of the patients, followed by dyslipidemia in 50% and diabetes mellitus in 31%. The percentage of patients with both hypertension and diabetes mellitus is 3%, diabetes mellitus & dyslipidemia is 5% whereas percentage of patients with hypertension & dyslipidemia is 18%. The percentage of patients with all three comorbidities of hypertension, diabetes mellitus and dyslipidemia are 18%.

Correlation with respect to cumulative number of comorbidities: The direction of the relationship between AHI, lowest desaturation, duration of snoring BMI with the number of comorbidities were analysed using Pearson correlation

Correlation of BMI with various comorbidities

The evaluation of correlation with cumulative number of comorbidities with hypertension, diabetes mellitus, dyslipidemia was conducted using Pearson correlation.

It is observed that there is significant association between BMI with hypertension, and dyslipidemia. Hence, a multivariate analysis is conducted to ascertain the most significant correlation. It is seen that BMI has the strongest influence on the development of dyslipidemia.

Association with respect to presence of comorbidities using Chi-square test

The evaluation of association with AHI, lowest desaturation, duration of snoring and cumulative number of comorbidities using Chi-square test was conducted.

multivariate analysis is conducted to ascertain the most significant association between the various risk factors to the development of comorbidities The strongest variable determining the development of comorbidity is duration of snoring.

Distribution of sites of obstruction according to flexible endoscopy evaluation

The most common site of obstruction is at the level of palate – circumferential, followed by obstruction at the level of larynx and then in the tongue base. The most common direction of obstruction in the palate is circumferential, followed by lateral, followed by anteroposterior.

Association Between the levels of obstruction and AHI

The association between the levels of collapse viz. nose, palate-circumferential, palate-lateral, palate anteroposterior, tongue base and larynx evaluated using Chi-square test using univariate analysis.

Significant association was found between levels of collapse in palate-circumferential, palate-lateral and AHI. Hence multivariate analysis using binary logistics was done and the most significant site of collapse affecting AHI was determined to be circumferential collapse at the level of palate.

Correlation between sites of collapse and lowest desaturation

A significant correlation was found between levels of collapse in palate-anteroposterior and palate-lateral and lowest desaturation. Hence multivariate analysis using binary logistics was conducted and the most significant site of collapse affecting lowest desaturation was determined to be at lateral collapse at level of palate.

The primary objective of the study was to determine if the severity of OSAS (as determined by AHI and lowest desaturation) along with its duration (years), has any role in the development of comorbidities- hypertension, diabetes, dyslipidemia and stroke (number of comorbidities in a person). It is found that 61% of population with obstructive sleep apnoea had at least one comorbidity. Out of 32 patients with mild OSA, 65% had comorbidities, out of 25 patients with moderate OSA, 76% had comorbidities and out of 43 patients with severe OSA, 93% had comorbidities. This is comparable to the study by Luis Silva, Daniela Cunha et al. wherein of a total of 128 patients with OSA,75% had comorbidities. The percentage of comorbidities in severely apnoeic patients in the current study are higher than that found in the study of Luis Silva et al. where, out of the 39 patients with severe OSA, 31 implying 79.5% had comorbidities.

The present study of positive correlation between AHI and hypertension is further corroborated by the data from the ESADA study in which 4372 subjects with mild OSA were found to have an independent association with hypertension. As per the study by Meslier et al., 494 patients with sleep apnoea were evaluated and diabetes was present in 30.1% patients. This is comparable to present study where the prevalence of diabetes is about 48%. As per the study by Yu -Ting Chou et al. on 236 patients with OSA, the overall prevalence of hypercholesterolaemia was 61.1% and hypertriglyceridemia was 55.3%. Desaturation index was a significant independent risk factor for the development of dyslipidemia in OSA patients which was also found in the present study.

Hyo Yeol Kim et al. recruited 33 patients with sleep disordered breathing and did flexible fibreoptic endoscopic evaluation with Mullers manouever and found that retroglossal collapse correlated significantly with AHI. But, in the present study, larynx and anteroposterior collapse correlates significantly with AHI. As per the study by Miljeteig et al., in which patients were classified based on their nasal resistance, no significant correlation could be found. In the present study, a positive significant correlation was found between obstruction at the level of nose and AHI.

The maximum number of comorbidities are in patients with severe OSA. A significant positive correlation was observed to be present between AHI, BMI and duration of snoring with the cumulative number of comorbidities. There also is a significant negative correlation between the lowest desaturation and cumulative number of comorbidities.

Comorbidities evaluated individually implies there is a positive significant correlation between AHI and duration of snoring on duration of hypertension. A positive significant correlation exists between AHI and diabetes and between AHI with dyslipidemia and a significant negative correlation between lowest desaturation and dyslipidemia. BMI is significantly correlated with development of hypertension, diabetes and dyslipidemia; with the strongest association with dyslipidemia. The strongest risk factor contributing to development of comorbidities is dyslipidemia.

With respect to flexible endoscopy findings, the most common site of collapse is at the level of palate at circumferential, followed by collapse at the level of tongue, nose and subsequently larynx. The collapse at the level of palate (anteroposterior) and nose shows a positively significant association with AHI. Tongue collapse also shows a positive correlation with AHI and the most significant effect on AHI is by the collapse at the level of palate (circumferential). Similarly, there is a significant negative correlation between lowest desaturation and palatal (anteroposterior and lateral) collapse.

The comorbidities studies showed a positive association with OSA with significant associations between severity of OSA and duration of snoring in the development of comorbidities hypertension and dyslipidemia. Obesity is a risk factor in the development of comorbidities in patients with OSA. Lowest desaturation is also a determinant in the development of comorbidities with maximum effects on development of hypertension and dyslipidemia. The strongest risk factor determining the development of comorbidity is the duration of snoring. The most common level of collapse is palatal collapse circumferential. There is significant correlation between collapse at the level of nose, anteroposterior palatal collapse and AHI. Anteroposterior and lateral collapse show negative correlation with lowest desaturation.

Millman RP, Redline S, Carlisle CC, Assaf AR, Levinson PD. Daytime hypertension in obstructive sleep apnea. Prevalence and contributing risk factors. Chest. 1991 Apr;99(4):861–6.

Foster GD, Sanders MH, Millman R, Zammit G, Borradaile KE, Newman AB, et al. Obstructive Sleep Apnea Among Obese Patients With Type 2 Diabetes. Diabetes Care. 2009 Jun 1;32(6):1017–9.

	Bouloukaki I, Grote L, McNicholas WT, Hedner J, Verbraecken J, Parati G, et al. Mild obstructive sleep apnea increases hypertension risk, challenging traditional severity classification. J Clin Sleep Med. 2020 Jun 15;16(6):889–98.

Baguet JP, Barone-Rochette G, Pépin JL. Hypertension and obstructive sleep apnoea syndrome: current perspectives. J Hum Hypertens. 2009 Jul;23(7):431–43.

Vgontzas AN, Li Y, He F, Fernandez-Mendoza J, Gaines J, Liao D, et al. Mild-tomoderate sleep apnea is associated with incident hypertension: age effect. Sleep. 2019 Apr 1;42(4):zsy265.

Nagayoshi M, Punjabi NM, Selvin E, Pankow JS, Shahar E, Iso H, et al. Obstructive sleep apnea and incident type 2 diabetes. Sleep Med. 2016 Sep;25:156–61.

Meslier N, Gagnadoux F, Giraud P, Person C, Ouksel H, Urban T, et al. Impaired glucose-insulin metabolism in males with obstructive sleep apnoea syndrome. Eur Respir J. 2003 Jul;22(1):156–60.

Reichmuth KJ, Austin D, Skatrud JB, Young T. Association of sleep apnea and type II diabetes: a population-based study. Am J Respir Crit Care Med. 2005 Dec 15;172(12):1590–

Tamura A, Kawano Y, Watanabe T, Kadota J. Relationship between the severity of obstructive sleep apnea and impaired glucose metabolism in patients with obstructive sleep apnea. Respir Med. 2008 Oct;102(10):1412–6.

Pinto JA, Ribeiro DK, Cavallini AF da S, Duarte C, Freitas GS. Comorbidities Associated with Obstructive Sleep Apnea: a Retrospective Study. Int Arch Otorhinolaryngol. 2016 Apr;20(2):145–50.

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272194390fig%20a.jpg?alt=media&token=10634163-7ab1-47d3-b4d8-0ed5e0cba5bc

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272194317fig%20b.jpg?alt=media&token=3bed4cde-abd1-441a-907d-755d7d9c9409

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272194392fig%20c.jpg?alt=media&token=7b249ee0-9b58-4280-9426-ad098f9d14e3

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272194450table%20b.jpg?alt=media&token=74508145-5aeb-4917-b5ce-01fc9d7ea196

https://firebasestorage.googleapis.com/v0/b/article-app-d985d.appspot.com/o/blogmedia%2F1732272194446table%20c.jpg?alt=media&token=ff935976-5f68-493f-8830-70b8c42997b4

A Study of Risk Factors for Cardiovascular and Metabolic Comorbidities in Patients with Obstructive Sleep Apnoea Syndrome

PG Registrar- IMT, Internal Medicine, Aster Medcity, Kochi, Kerala—India

Dr Vinay Ravikumar

Renato D. Lopes, M.D., Ph.D., Gretchen Heizer, M.S., Ronald Aronson, M.D.,Amit N. Vora, M.D., M.P.H., Tyler Massaro, Ph.D., Roxana Mehran, M.D.,Shaun G. Goodman, M.D., Stephan Windecker, M.D., Harald Darius, M.D., Jia Li, Ph.D., Oleg Averkov, M.D., Ph.D., M. Cecilia Bahit, M.D., et al., for the AUGUSTUS Investigators.

A trial fibrillation (AF) has strong associations with other cardiovascular diseases, such as heart failure, coronary artery disease (CAD), valvular heart disease, diabetes mellitus, and hypertension. Whether a person has, Paroxysmal, persistent, long standing or permanent atrial fibrillation, select patients require a an anticoagulation regimen in order to prevent cardioembolic consequences. Choosing antithrombotic therapy for patients with atrial fibrillation who have an acute coronary syndrome or have undergone percutaneous coronary intervention (PCI) is challenging. Dual antiplatelet therapy is proven to reduce the incidence of recurrent ischemic events and stent thrombosis but is less effective in reducing the incidence of cardioembolic stroke associated with atrial fibrillation. Regimens including non–vitamin K antagonist oral anticoagulants appear to offer a number of advantages over vitamin K antagonists, including the potential for less bleeding.  The Augustus trial (a two-by-two factorial, randomized, controlled clinical trial) to assess the safety and efficacy of standard-dose apixaban as compared with a vitamin K antagonist and of low-dose aspirin as compared with placebo, on a background of concomitant P2Y12 inhibitor therapy for 6 months in patients with atrial fibrillation and recent acute coronary syndrome or PCI.

AUGUSTUS was a prospective, multicenter, two-by-two factorial, randomized clinical trial comparing apixaban with a vitamin K antagonist and comparing aspirin with placebo in patients with atrial fibrillation who had a recent acute coronary syndrome or underwent PCI (or both). The 2 x 2 factorial design calls for randomizing each participant to treatment A or B to address one question and further assignment at random within each group to treatment C or D to examine a second issue, permitting the simultaneous test of two different hypotheses.

Patients underwent randomization within 14 days after having an acute coronary syndrome or undergoing PCI, with explicit guidance provided to enroll eligible patients and start the trial regimen as soon as possible after the index event once parenteral anticoagulation had been stopped. To be eligible, patients were required to be planning to use an approved P2Y12 inhibitor for at least 6 months. The choice of P2Y12 inhibitor was left to the discretion of the treating physician.

Patients were randomly assigned by means of an interactive voice-response system to receive apixaban or a vitamin K antagonist and to receive aspirin or matching placebo. The treatment regimen comparing apixaban with a vitamin K antagonist was open-label; however, the regimen comparing aspirin with matching placebo was double-blind. The dose of Apixaban was fixed based on patient factors like Age, creatinine, and weight . The dose of Vitamin K antagonist was optimised to achieve a target PTINR between 2-3 . Aspirin dosing was set at 81 mg for those receiving aspirin instead of the placebo.

An age of at least 18 years; previous, persistent, permanent, or paroxysmal atrial fibrillation and planned long-term use of an oral anticoagulant

Recent acute coronary syndrome or PCI; and planned use of a P2Y12 inhibitor for at least 6 months.

Patients who were using anticoagulation for other conditions (e.g., prosthetic valves, venous thromboembolism, and mitral stenosis) were not eligible). 

Recent or planned coronary-artery bypass graft surgery, coagulopathy or ongoing bleeding, and contraindication to a vitamin K antagonist, apixaban, all P2Y12 inhibitors, or aspirin

Stroke and bleeding risks were assessed with the use of two scores:

CHA2DS2-VASc scores reflect the risk of stroke among patients with atrial fibrillation who are not receiving anticoagulant therapy; scores range from 0 to 9, with higher scores indicating greater risk.

HAS-BLED scores reflect the risk of bleeding among patients with atrial fibrillation who are receiving anticoagulant therapy; scores range from 0 to 9, with higher scores indicating greater risk.

The primary outcome for both factorial comparisons was major or clinically relevant nonmajor bleeding as defined by the International Society on Thrombosis and Haemostasis (ISTH). ISTH major bleeding was defined as bleeding that resulted in death, occurred in a critical organ (intracranial, intraspinal, intraocular, retroperitoneal, intraarticular, intramuscular with compartment syndrome, or pericardial), or was associated with either a decrease in the hemoglobin level of at least 2 g per deciliter or a transfusion of at least 2 units of packed red cells.12 Clinically relevant nonmajor bleeding was defined as bleeding that resulted in hospitalization, medical or surgical intervention for bleeding, an unscheduled clinic visit, or a change in physician-directed antithrombotic therapy. Bleeding was also classified according to the Global Use of Strategies to Open Occluded Arteries (GUSTO) and Thrombolysis in Myocardial.

Included the composite of death or hospitalization and the composite of death or ischemic events (stroke, myocardial infarction, stent thrombosis [definite or probable], or urgent revascularization).

From September 2015 through April 2018, a total of 4614 patients from 492 sites in 33 countries were randomly assigned to receive open-label apixaban or a vitamin K antagonist and to receive double-blind aspirin or matching placebo.
The median age among all the patients was 70.7 years, and 29.0% of the patients were women.Among the patients who underwent randomization, 1714 of 4595 (37.3%) had acute coronary syndrome and underwent PCI, 1097 (23.9%) had medically managed acute coronary syndrome, and 1784 (38.8%) underwent elective PCI.


july-2024

QUICK LINKS

CATEGORY

DEPARTMENT

ISSUE

HELP