Prevalence of obstructive sleep apnea risk and associated factors among patients with type 2 diabetes mellitus on follow up at Jimma Medical Center, Southwest Ethiopia

Abdissa, Daba

doi:10.1016/j.jcte.2020.100234

Cited by 11 publications

(19 citation statements)

References 32 publications

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“…It is similar to different studies that BMI, snoring and smoking are significant risk factors (Cho et al, 2020;Foroughi et al, 2017). In contrast, our study did not reveal that gender, observed apnea, blood pressure, and neck size are significantly associated with risk of OSA (Abdissa, 2020;Wong et al, 2020).…”

Section: Discussioncontrasting

confidence: 99%

Prevalence and Associated Factors of Obstructive Sleep Apnea in Saudi Arabia: A Web Based Questionnaire Based Study

Alsalamah¹,

Abu-Saris²

2022

GJHS

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BACKGROUND: Obstructive sleep apnea (OSA) is a common condition that is prevalent among males. Though less is known about its prevalence among females. Furthermore, STOP-BANG score test is a self-reported survey that is widely used for diagnosing risks of obstructive sleep apnea. Due to the lack of determining the prevalence of OSA among females and its risk factors using STOP-BANG score test, the study was brought off. METHODS: A web-based cross-sectional study was conducted using modified STOP-BANG questionnaire that was distributed through WhatsApp, Twitter, Snapchat, and Telegram to determine the prevalence and associated factors of OSA among Saudi females in comparison to males. RESULTS: A total of 1377 participants completed the survey, total of 819 (59.4%) were females. The results showed that Prevalence of OSA among females is much less and the number of who had whether mild or moderate risk of OSA was 44 (3.9%) while 78 (14%) were males. Among the survey variables, the only significant factors were Smoking, Snoring, Body mass index, and tiredness. CONCLUSION: OSA is a common sleeping disorder among men. Contrastingly, its prevalence is much less among females. STOP-BANG score is a good, cheap, and easy to use for diagnosing risk of OSA. Finally, OSA is associated with smoking, BMI, tiredness, and snoring.

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Section: Discussioncontrasting

confidence: 99%

Prevalence and Associated Factors of Obstructive Sleep Apnea in Saudi Arabia: A Web Based Questionnaire Based Study

Alsalamah¹,

Abu-Saris²

2022

GJHS

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“…The prevalence of high-risk OSA among T2DM patients was 42.2%. This result was in line with the studies conducted in Ethiopia (45.5%) [ 40 ], Kenya (44.4%) [ 41 ], Saudi Arabia (44.3%) [ 39 ], and Jordan (48.5%) [ 38 ]. This resemblance could be due to similarity in the population studied as well as study tools and procedures: Kenya, Saudi Arabia, and Jordan used the Berlin questionnaire to diagnose high-risk OSA which was similar to the current study.…”

Section: Discussionsupporting

confidence: 91%

“…The study also identified factors: age, neck grasp, waist circumference, and the presence of T2DM-related complications were independent predictors of high-risk OSA among T2DM patients. Although neck circumference was a common independent predictor of high-risk OSA in prior studies [ 37 – 40 ], this study couldn’t find an independent association. The reason could be the gender and size normative neck gap measure might predict better and veneer the predicting ability of neck circumference, which are two parameters that measure the neck mass independently, to predict high-risk OSA.…”

Section: Discussioncontrasting

confidence: 68%

Obstructive sleep apnea risk and its associated factors among type 2 diabetes mellitus patients at wolkite university specialized hospital, Wolkite, Southern Ethiopia, 2021. A comparative cross-sectional study

Wondie

Taderegew

Girma

et al. 2022

Diabetol Metab Syndr

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Background Obstructive sleep apnea is a syndrome characterized by recurrent partial, or complete upper airway collapse during sleep. Although obstructive sleep apnea is common in type 2 diabetes mellitus, the majority of patients remain undiagnosed because of the prohibitive cost of the test and paucity of the sleep clinic, especially in developing nations. The study aimed to assess high-risk obstructive sleep apnea and its associated factors among type 2 diabetes mellitus patients at Wolkite University Specialized Hospital. Methods A Hospital-based comparative cross-sectional study was employed from October 15 to December 5, 2021, among 204 participants. Data collection was done by semi-structured interviewer-administered questionnaires. Data was entered into the Epi data version 4.6 and exported to SPSS version 25.0 for analysis. Independent t-test and chi-square test were used to compare continuous and categorical variables accordingly. Binary and multiple logistic regression analysis was used to assess factors associated with high-risk obstructive sleep apnea. Statistical significance was set at P-value < 0.05. Results A total of 204 participants with an equal proportion of the two groups were included with a 100% response rate. About 56.9% of the participants were males. The mean age of type 2 diabetes mellitus patients was 57.1 (± 12.0) years and the non-diabetic group was 55.3 (± 10.9) years. The prevalence of high-risk obstructive sleep apnea among type 2 diabetes mellitus was 42.2%, and that of non-diabetics was 13.7% (p < 0.001). Age (AOR: 1.13; 95%CI: 1.04, 1.22), neck grasp (AOR: 6.48; 95%CI: 1.56, 26.96), waist circumference (AOR: 4.44; 95%CI: 1.12, 17.61) and the presence of diabetes-related complications (AOR: 4.18; 95%CI: 1.13, 15.43) were independently associated with high-risk obstructive sleep apnea among type 2 diabetes mellitus patients. Conclusion The prevalence of high-risk obstructive sleep apnea among type 2 diabetes mellitus was higher with a significant difference from their comparison group. Age, neck grasp, waist circumference, and diabetes-related complications were significantly associated with high-risk obstructive sleep apnea among type 2 diabetes mellitus patients. Therefore, type 2 diabetes mellitus patients should be screened for obstructive sleep apnea in the early course of their follow-up to take preventive measures and early treatments.

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“…Due to the limitation of sleep labs in hospitals and taking into account the intricacy of the above diagnostic technique, the procedure is quite expensive and cumbersome to use. A minimum of 22 lead wires are connected to the patient's body to acquire 12 different PSG signals, which makes the signal analysis part quite tricky and also cause discomfort to the patient [ 4 , 5 ]. Apnea-Hypopnea Index (AHI) is used to analyze the severity of the disease, which is measured as the number of apneic episodes in the PSG recordings per hour of sleep, in comparison with the American Academy of Sleep Medicine (AASM).…”

Section: Clinical Backgroundmentioning

confidence: 99%

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

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Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night’s sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

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Prevalence of obstructive sleep apnea risk and associated factors among patients with type 2 diabetes mellitus on follow up at Jimma Medical Center, Southwest Ethiopia

Abstract: Highlights Obstructive sleep apnea is a periodic, partial or complete obstruction in the upper airway during sleep. It is highly prevalent among obese patients with type two diabetes mellitus. There were high numbers of participants with high risk of OSA among the study participants.

Cited by 11 publications

References 32 publications

Prevalence and Associated Factors of Obstructive Sleep Apnea in Saudi Arabia: A Web Based Questionnaire Based Study

Prevalence and Associated Factors of Obstructive Sleep Apnea in Saudi Arabia: A Web Based Questionnaire Based Study

Obstructive sleep apnea risk and its associated factors among type 2 diabetes mellitus patients at wolkite university specialized hospital, Wolkite, Southern Ethiopia, 2021. A comparative cross-sectional study

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

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