Clinical risk factors for obstructive sleep apnea in a Korean sleep clinic

2019 ◽  
Vol 64 ◽  
pp. S175
Author(s):  
J.-Y. Jeon ◽  
H.-J. Moon ◽  
K.T. Kim ◽  
Y.W. Cho
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Obstructive Sleep

scholarly journals Obstructive sleep apnea and stroke severity: Impact of clinical risk factors

2021 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Thomas Nathaniel ◽  
CarolynBreauna Sanders ◽  
Krista Knisely ◽  
Camron Edrissi ◽  
Chase Rathfoot ◽  
...  
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Clinical Risk Factors ◽  
Stroke Severity ◽  
Clinical Risk ◽  
Obstructive Sleep

scholarly journals The Prioritization of Clinical Risk Factors of Obstructive Sleep Apnea Severity Using Fuzzy Analytic Hierarchy Process

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Thaya Maranate ◽  
Adisak Pongpullponsak ◽  
Pimon Ruttanaumpawan
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Analytic Hierarchy Process ◽  
Clinical Risk Factors ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analytic Hierarchy ◽  
Clinical Risk ◽  
Obstructive Sleep ◽  
Hierarchy Process

Recently, there has been a problem of shortage of sleep laboratories that can accommodate the patients in a timely manner. Delayed diagnosis and treatment may lead to worse outcomes particularly in patients with severe obstructive sleep apnea (OSA). For this reason, the prioritization in polysomnography (PSG) queueing should be endorsed based on disease severity. To date, there have been conflicting data whether clinical information can predict OSA severity. The 1,042 suspected OSA patients underwent diagnostic PSG study at Siriraj Sleep Center during 2010-2011. A total of 113 variables were obtained from sleep questionnaires and anthropometric measurements. The 19 groups of clinical risk factors consisting of 42 variables were categorized into each OSA severity. This study aimed to array these factors by employing Fuzzy Analytic Hierarchy Process approach based on normalized weight vector. The results revealed that the first rank of clinical risk factors in Severe, Moderate, Mild, and No OSA was nighttime symptoms. The overall sensitivity/specificity of the approach to these groups was 92.32%/91.76%, 89.52%/88.18%, 91.08%/84.58%, and 96.49%/81.23%, respectively. We propose that the urgent PSG appointment should include clinical risk factors of Severe OSA group. In addition, the screening for Mild from No OSA patients in sleep center setting using symptoms during sleep is also recommended (sensitivity = 87.12% and specificity = 72.22%).

Screening for Obstructive Sleep Apnea in Preschool Children with Cleft Palate

2009 ◽  
Vol 46 (2) ◽  
pp. 117-123 ◽  
Author(s):  
J. E. MacLean ◽  
K. Waters ◽  
D. Fitzsimons ◽  
P. Hayward ◽  
D. A. Fitzgerald
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Risk Factor ◽  
Preschool Children ◽  
Sleep Apnea ◽  
Cleft Lip ◽  
Clinical Risk Factor ◽  
Questionnaire Data ◽  
Clinical Risk ◽  
Obstructive Sleep

Objective: The objective of this study was to explore the prevalence, range of reported symptoms, and clinical risk factors of obstructive sleep apnea in preschool children with cleft lip and/or palate. Design: Questionnaires were distributed to parents/guardians of all children from birth to 5 years of age who were followed by the cleft clinic. Results: Questionnaire data and cleft classification were available for 248 children, with a mean age of 33.4 months. Obstructive sleep apnea was identified in 31.4% of the children. Only 29.5% of children with obstructive sleep apnea had undergone an investigation of these symptoms. The three most common symptoms reported in children with a questionnaire diagnosis of obstructive sleep apnea were (1) “heavy or loud breathing,” (2) “easily distracted,” and (3) “on the go” or “driven by a motor.” The only clinical risk factor associated with a questionnaire diagnosis of obstructive sleep apnea was the presence of a syndrome (χ2  =  3.5, p  =  .05). There were no significant differences in risk of obstructive sleep apnea by age, cleft classification, and surgical status. Conclusion: Preschool children with cleft lip and/or palate have a risk of obstructive sleep apnea that is as much as five times that of children without cleft. Obstructive sleep apnea appears to be underrecognized in this group of children. Further research is needed to investigate important risk factors for obstructive sleep apnea in children with cleft lip and/or palate.

Obstructive Sleep Apnea: Epidemiology, Causes, and Consequences

2020 ◽  
Author(s):  
Diane C Lim ◽  
Richard J Schwab
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cushing Syndrome ◽  
Upper Airway ◽  
Fat Distribution ◽  
Epidemiologic Studies ◽  
Obstructive Sleep ◽  
Upper Airway Muscles ◽  
Underlying Diseases

As part one of the three chapters on sleep-disordered breathing, this chapter reviews obstructive sleep apnea (OSA) epidemiology, causes, and consequences. When comparing OSA prevalence between 1988 to 1994 and 2007 to 2010, we observe that OSA is rapidly on the rise, paralleling increasing rates in obesity. Global epidemiologic studies indicate that there are differences specific to ethnicity with Asians presenting with OSA at a lower body mass index than Caucasians. We have learned that structural and physiologic factors increase the risk of OSA and both can be influenced by genetics. Structural risk factors include craniofacial bony restriction, changes in fat distribution, and the size of the upper airway muscles. Physiologic risk factors include airway collapsibility, loop gain, pharyngeal muscle responsiveness, and arousal threshold. The consequences of OSA include daytime sleepiness and exacerbation of many underlying diseases. OSA has been associated with cardiovascular diseases including hypertension, coronary heart disease, stroke, atrial fibrillation, and other cardiac arrhythmias; pulmonary hypertension; metabolic disorders such as type 2 diabetes, hypothyroidism, acromegaly, Cushing syndrome, and polycystic ovarian syndrome; mild cognitive impairment or dementia; and cancer. This review contains 4 figures, 1 table and 48 references. Key Words: cardiac consequences, craniofacial bony restriction, epidemiology, fat distribution, metabolic disease, neurodegeneration, obesity, obstructive sleep apnea

Abstract P195: Lack of Association between Sleep Duration and ECG Arrhythmia Risk Measures: Chicago Area Sleep Study

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Jason Ng ◽  
Phyllis C Zee ◽  
Jeffrey J Goldberger ◽  
Kristen L Knutson ◽  
Kiang Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Low Risk ◽  
Qtc Interval ◽  
Sleep Study ◽  
Chicago Area ◽  
Obstructive Sleep ◽  
Pr Interval

Introduction Sleep duration is significantly associated with cardiovascular disease risk factors such as hypertension, diabetes, and obesity in adults at low risk for obstructive sleep apnea. Although it is known that apnea increases the risk for sudden cardiac death, it is not known whether adults with short sleep duration independent of apnea have a higher risk for cardiac arrhythmias Hypothesis We tested the hypothesis that sleep duration in adults at low risk for obstructive sleep apnea would be associated with ECG measures that are known risk factors for ventricular arrhythmias. Methods The Chicago Area Sleep Study recruited 610 participants via commercially available telephone listings. Participants were screened using in-home apnea detection equipment (ApneaLinkTM) for one night to exclude subjects with apnea/hypopnea index ≥ 15. Participants wore wrist actigraphs for 7 days to objectively determine sleep duration. A 10-minute 12-lead ECG was recorded for each subject. Standard measures of heart rate, PR interval, and QTc interval were obtained along with markers of ventricular repolarization, Tpeak to Tend interval (Tpe) and spatial QRS-T angle. Signal-averaged ECG analysis was performed to measure filtered QRS duration (fQRSd), RMS voltage of terminal 40 ms (RMS), and duration of terminal QRS signals <40μV (LAS). Participants with atrial fibrillation, >20% ectopic beats and those using antihypertensive and sleep medications were excluded from analysis. The effect of sleep duration on the ECG parameters was estimated using a multiple linear regression model adjusting for demographics (sex, age, and race) and cardiovascular risk factors (BMI, hypertension, coronary heart disease, and diabetes). Results ECGs from a total of 504 participants (200 male, 48±8 years old) were analyzed. Mean sleep duration was 7±1 hrs, heart rate was 64±9 bpm, PR interval was 165±18 ms, and QTc interval was 424±23 ms. Mean Tpe interval was 83±14 ms and spatial QRS-T angle was 29±26 deg. The signal-averaged ECG measures of fQRSd, RMS, and LAS had mean values of 78±12 ms, 58±34 μV, and 24±9 ms, respectively. In an unadjusted model, there was a borderline association between sleep duration and QTc (β=0.004 ms/hr, SE=0.0023, p=0.08). However, that association was no longer significant following adjustment with demographics and cardiovascular risk factors. No other ECG measures were associated with sleep duration. Conclusions In a population at low risk of obstructive sleep apnea, ECG-based measures of cardiovascular risks were not associated with sleep duration. Previously reported associations between short sleep and cardiovascular events may not be arrhythmic in origin.

scholarly journals Which oropharyngeal factors are significant risk factors for obstructive sleep apnea? An age-matched study and dentist perspectives

2016 ◽  
Vol Volume 8 ◽  
pp. 215-219 ◽  
Author(s):  
Kittisak Sawanyawisuth ◽  
Supanigar Ruangsri ◽  
Teekayu Plangkoon Jorns ◽  
Subin Puasiri ◽  
Thitisan Luecha ◽  
...  
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Significant Risk ◽  
Obstructive Sleep ◽  
Matched Study

scholarly journals Apnea Monitor based on Bluetooth with Android Interface

2020 ◽  
Vol 1 (2) ◽  
pp. 50-56
Author(s):  
I Dewa Made Wirayuda ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sudden Death ◽  
Respiration Rate ◽  
Average Error ◽  
Obstructive Sleep ◽  
Testing And Measurement ◽  
Mit App Inventor

Apnea monitor is a device that is used to give a warning if there is stop breathing. Stop breathing while sleeping is one form of obstructive sleep apnea. This cessation of breath cannot be underestimated, this is related to the main risk factors for health implications and increased cardiovascular disease and sudden death. The purpose of this study is to design an apnea monitor with the Android interface. This device allows the users to get how many times sleep apnea happens while sleeping and got data to analysis before continuing with a more expensive and advanced sleep test. This device used a flex sensor to detect the respiration rate, the sensor placed on the abdomen or belly so it can measure expand and deflate while breathing. The microcontroller uses an Arduino chip called AT-Mega328. Bluetooth HC-05 used to send respiration data to Android, MIT app inventor used for the android programmer, and on the android, there are plotting of respiration value and when the device detected apnea so the android also gives a warning to the user. Based on the results of testing and measurement then compare with another device, the results of the average% error were 3.61%. This apnea monitor design is portable but there are needs some improvement by using another sensor for detected respiration and using a module other than Bluetooth.

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