scholarly journals Clinical Features of Obstructive Sleep Apnea That Determine Its High Prevalence in Resistant Hypertension

2015 ◽  
Vol 56 (5) ◽  
pp. 1258 ◽  
Author(s):  
Hyun Jin Min ◽  
Yang-Je Cho ◽  
Chang-Hoon Kim ◽  
Da Hee Kim ◽  
Ha Yan Kim ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Clinical Features ◽  
Resistant Hypertension ◽  
Obstructive Sleep ◽  
High Prevalence

scholarly journals Development of a pediatric obstructive sleep apnea triage algorithm

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
D. S. Heath ◽  
H. El-Hakim ◽  
Y. Al-Rahji ◽  
E. Eksteen ◽  
T. C. Uwiera ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Univariate Analysis ◽  
Individual Risk ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
Grade Scale ◽  
Overnight Oximetry ◽  
Triage Algorithm ◽  
Pediatric Sleep

Abstract Introduction Diagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists. Method We used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT. Results From the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm. Conclusion Tonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist. Graphical abstract

scholarly journals Ehlers-Danlos Syndrome and Narcolepsy: An Incidental Relationship?

2019 ◽  
Vol 1 (3) ◽  
pp. 16-20
Author(s):  
Lindsay Miliken ◽  
Karim Sedky
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Literature Review ◽  
Excessive Daytime Sleepiness ◽  
Ehlers Danlos Syndrome ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
Different Types ◽  
Sporadic Cases ◽  
Danlos Syndrome

Ehlers Danlos syndrome (EDS) is a collagenic disease that has often been associated with different types of sleep disorders ranging from insomnia to obstructive sleep apnea (OSA). EDS usually has associated fatigue and excessive daytime sleepiness (ES), thus narcolepsy should be excluded as a cause. Literature review suggests a high prevalence of hypersomnia disorders in this population. We present two sporadic cases presenting with typical symptoms of narcolepsy.

scholarly journals Tormented Sunrises

2019 ◽  
pp. 418-434
Author(s):  
Maha Alattar
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
General Population ◽  
Sleep Disorders ◽  
Headache Disorders ◽  
Distinct Subset ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
The Relationship

This chapter covers the relationship between sleep-related headaches and sleep disorders such as obstructive sleep apnea (OSA). Sleep apnea headache (SAH), a type of sleep-related headache that is classified in the International Classification of Headache Disorders, is a distinct subset of headache that is caused by OSA and occurs distinctly on awakening. Once recognized, treatment of OSA is associated with significant improvement in, and often resolution of, SAH. Given the high prevalence of headaches in the general population, sleep disorders must be considered in the evaluation of patients with headaches. A comprehensive sleep evaluation should be an integral part of the assessment of headache disorders. Sleep apnea headache and other types of headaches associated with sleep are reviewed in this chapter.

High Prevalence of Obstructive Sleep Apnea among Patients with Head and Neck Cancer

2005 ◽  
Vol 34 (05) ◽  
pp. 304 ◽  
Author(s):  
Richard J. Payne ◽  
Michael P. Hier ◽  
Karen M. Kost ◽  
Martin J. Black ◽  
Anthony G. Zeitouni ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Head And Neck Cancer ◽  
Sleep Apnea ◽  
Head And Neck ◽  
Neck Cancer ◽  
Obstructive Sleep ◽  
High Prevalence

scholarly journals Clinical Features and Contributing Factors of Excessive Daytime Sleepiness in Chinese Obstructive Sleep Apnea Patients: The Role of Comorbid Symptoms and Polysomnographic Variables

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chuan Shao ◽  
Huan Qi ◽  
Ruyi Lang ◽  
Biyun Yu ◽  
Yaodong Tang ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Clinical Features ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Memory Loss ◽  
Apnea Hypopnea Index ◽  
Contributing Factors ◽  
Obstructive Sleep ◽  
Work Commute

Background. The occurrence and severity of excessive daytime sleepiness (EDS) vary considerably among obstructive sleep apnea (OSA) patients. This study was designed to investigate the characteristics of EDS and identify its contributing factors in OSA patients. Methods. This was a cross-sectional study from a tertiary medical center in China. A total of 874 consecutive patients with newly diagnosed OSA were included. Subjective daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). The subjects were assigned to the non-EDS group (582 patients), mild to moderate EDS group (227 patients), and severe EDS group (65 patients) according to the ESS scores. The clinical features and polysomnographic parameters were acquired and analyzed to identify the differences between groups and the determinants of EDS. Results. The age of patients with severe EDS (49.5 ± 11.3) was slightly greater than that of patients with mild to moderate EDS (44.5 ± 10.2) (p<0.05) and non-EDS patients (45.2 ± 12.0) (p<0.05). Body mass index (BMI) was highest in the severe EDS group (29.1 ± 3.6 kg/m2) (p<0.0001), intermediate in the mild to moderate EDS group (27.9 ± 3.3 kg/m2), and lower in the non-EDS group (26.8 ± 3.3 kg/m2). Logistic regression analysis showed waist circumference, memory loss, work/commute disturbances, and sleep efficiency were independently associated with mild to moderate EDS, and the microarousal index, apnea-hypopnea index (AHI), and saturation impair time below 90% were independent contributing factors of mild to moderate EDS. Meanwhile, age, neck circumference, gasping/choking, memory loss, work/commute disturbances, and sleep latency were independently associated with severe EDS, and the AHI and mean SpO2 were independent contributing factors of severe EDS. Conclusions. OSA patients with various severities of EDS are more obese and have more comorbid symptoms compared to patients without EDS. Sleep fragmentation, respiratory events, and nocturnal hypoxia may be predictors of EDS. Comprehensive consideration of demographic, clinical, and polysomnographic factors is required when evaluating OSA patients.

scholarly journals Oropharyngeal examination as a predictor of obstructive sleep apnea: pilot study of gag reflex and palatal reflex

2011 ◽  
Vol 69 (5) ◽  
pp. 805-808 ◽  
Author(s):  
Juliana Spelta Valbuza ◽  
Márcio Moysés de Oliveira ◽  
Cristiane Fiquene Conti ◽  
Lucila Bizari F. Prado ◽  
Luciane B.C. Carvalho ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Noninvasive Methods ◽  
Noninvasive Test ◽  
Obstructive Apnea ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
Gag Reflex ◽  
The Impact

Obstructive sleep apnea (OSA) has high prevalence and may cause serious comorbities. The aim of this trial was to show if simple noninvasive methods such as gag reflex and palatal reflex are prospective multivariate assessments of predictor variables for OSA. METHOD: We evaluate gag reflex and palatal reflex, of fifty-five adult patients, and their subsequent overnight polysomnography. RESULTS: Forty-one participants presented obstructive sleep apnea. The most relevant findings in our study were: [1] absence of gag reflex on patients with severe obstructive apnea (p=0.001); [2] absence of palatal reflex on moderate obstructive apnea patients (p=0.02). CONCLUSION: Gag reflex and palatal reflex, a simple noninvasive test regularly performed in a systematic neurological examination can disclose the impact of the local neurogenic injury associated to snoring and/or obstructive sleep apnea syndrome.

scholarly journals Factors predictive of obstructive sleep apnea in patients undergoing pre-operative evaluation for bariatric surgery and referred to a sleep laboratory for polysomnography

2015 ◽  
Vol 41 (5) ◽  
pp. 440-448 ◽  
Author(s):  
Ricardo Luiz de Menezes Duarte ◽  
Flavio José Magalhães-da-Silveira
Keyword(s):  
Bariatric Surgery ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Good Accuracy ◽  
Apnea Hypopnea Index ◽  
Roc Curve Analysis ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
Risk Patients ◽  
Operative Evaluation

Objective: To identify the main predictive factors for obtaining a diagnosis of obstructive sleep apnea (OSA) in patients awaiting bariatric surgery. Methods: Retrospective study of consecutive patients undergoing pre-operative evaluation for bariatric surgery and referred for in-laboratory polysomnography. Eight variables were evaluated: sex, age, neck circumference (NC), BMI, Epworth Sleepiness Scale (ESS) score, snoring, observed apnea, and hypertension. We employed ROC curve analysis to determine the best cut-off value for each variable and multiple linear regression to identify independent predictors of OSA severity. Results: We evaluated 1,089 patients, of whom 781 (71.7%) were female. The overall prevalence of OSA-defined as an apnea/hypopnea index (AHI) ≥ 5.0 events/h-was 74.8%. The best cut-off values for NC, BMI, age, and ESS score were 42 cm, 42 kg/m2, 37 years, and 10 points, respectively. All eight variables were found to be independent predictors of a diagnosis of OSA in general, and all but one were found to be independent predictors of a diagnosis of moderate/severe OSA (AHI ≥ 15.0 events/h), the exception being hypertension. We devised a 6-item model, designated the NO-OSAS model (NC, Obesity, Observed apnea, Snoring, Age, and Sex), with a cut-off value of ≥ 3 for identifying high-risk patients. For a diagnosis of moderate/severe OSA, the model showed 70.8% accuracy, 82.8% sensitivity, and 57.9% specificity. Conclusions: In our sample of patients awaiting bariatric surgery, there was a high prevalence of OSA. At a cut-off value of ≥ 3, the proposed 6-item model showed good accuracy for a diagnosis of moderate/severe OSA.

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