Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

Tom, Chloe; Roy, Bhaswati; Vig, Ruchi; Kang, Daniel W.; Aysola, Ravi; Woo, Mary A.; Harper, Ronald M.; Kumar, Rajesh

doi:10.1007/s41782-018-0041-1

Cited by 31 publications

(26 citation statements)

References 45 publications

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“…Loudness of snoring usually correlates with the severity of OSA (i.e., AHI) [ 44 ]. Waist circumference is also an important risk factor and predictor of OSA, along with body weight, body mass index, and neck circumference, and is significantly correlated with OSA severity [ 45 ]. In a previous study, this correlation was reported in Koreans [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

et al. 2021

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This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.

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Section: Discussionmentioning

confidence: 99%

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

et al. 2021

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“…Upper body obesity may directly augment narrowing of the upper airway 45 Studies have shown that the dimensions of neck and waist circumference predict OSAS severity in adults. 46 The most common cause of OSAS in children is considered to be narrowing of the upper airway due to adenotonsillar hypertrophy, 11 with more recent data indicating that it may also be related to airway size and structure. [47][48][49][50][51] During childhood growth and development, the upper airway is a site for progressive alteration, including changes in growth of tissues [52][53][54] as well as functional adaptations in neuromuscular tone and ventilatory drive.…”

Section: Sleep-disordered Breathingmentioning

confidence: 99%

“…Upper body obesity may directly augment narrowing of the upper airway 45 Studies have shown that the dimensions of neck and waist circumference predict OSAS severity in adults. 46 …”

Section: Sleep-disordered Breathingmentioning

confidence: 99%

<p>Sleep-disordered breathing and comorbidities: role of the upper airway and craniofacial skeleton</p>

Brennan

Kirkham

Gavlak

2020

NSS

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Obstructive sleep-disordered breathing (SDB), which includes primary snoring through to obstructive sleep apnea syndrome (OSAS), may cause compromise of respiratory gas exchange during sleep, related to transient upper airway narrowing disrupting ventilation, and causing oxyhemoglobin desaturation and poor sleep quality. SDB is common in chronic disorders and has significant implications for health. With prevalence rates globally increasing, this condition is causing a substantial burden on health care costs. Certain populations, including people with sickle cell disease (SCD), exhibit a greater prevalence of OSAS. A review of the literature provides the available normal polysomnography and oximetry data for reference and documents the structural upper airway differences between those with and without OSAS, as well as between ethnicities and disease states. There may be differences in craniofacial development due to atypical growth trajectories or extramedullary hematopoiesis in anemias such as SCD. Studies involving MRI of the upper airway illustrated that OSAS populations tend to have a greater amount of lymphoid tissue, smaller airways, and smaller lower facial skeletons from measurements of the mandible and linear mental spine to clivus. Understanding the potential relationship between these anatomical landmarks and OSAS could help to stratify treatments, guiding choice towards those which most effectively resolve the obstruction. OSAS is relatively common in SCD populations, with hypoxia as a key manifestation, and sequelae including increased risk of stroke. Combatting any structural defects with appropriate interventions could reduce hypoxic exposure and consequently reduce the risk of comorbidities in those with SDB, warranting early treatment interventions.

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“…In another study, the signi cant mean larger waist circumference was observed in a severe OSAS group (N = 437) compared with a control group (N = 72) in the Turkish subjects (OSAS group: 111.74 ± 12.47 cm; control group: 91.67 ± 12.00 cm, p < 0.001) (19). In a further study, which involved OSAS subjects (N = 59) recruited in the USA, signi cant associations were observed between the apnea-hypopnea index (AHI) and BMI (r = 0.349, p = 0.008), neck circumference (r = 0.276, p = 0.038), and waist circumference (r = 0.459, p < 0.001) (20). Despite epidemiological reports showing that excess body pro les are associated with the severity of OSAS, to the best of the authors' knowledge, there is still no applicable screening model for accessing OSAS risk by considering the anthropometric data, gender and age effects.…”

mentioning

confidence: 93%

Risk Screening of Obstructive Sleep Apnea Syndrome by Body Profiles via Random Forests Model

Tsai

Lin

et al. 2020

Preprint

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Background Obstructive Sleep Apnea Syndrome (OSAS) is a major global health concern and is typically diagnosed by in-lab polysomnography (PSG). This examination though has high medical manpower costs and alternative portable methods have further limitations. This paper develops a new model for screening the risk of OSAS in different age groups and gender by using body profiles. The effects of body profiles for different subgroups in sleep stage alteration and OSAS severity are also investigated. Methods The data is derived from 6614 Han-Taiwanese subjects who have previously undergone PSG in order to assess the severity of OSAS in the sleep center of Taipei Medical University Shuang-Ho Hospital between March 2015 and October 2019. Characteristics of subjects, including age, gender, body mass index (BMI), neck circumference, and waist circumference, were obtained from a questionnaire. Pearson regression was used to evaluate the correlations between body profiles and sleep stages as well as sleep disorder indexes. To develop an age and gender independent model, random forests (RF), which is an ensemble learning method with high explainability, were trained by the four groups by gender and age (older or younger than 50 years old) with ratios of 70% (training dataset) and 30% (testing dataset), respectively. Prediction performance was evaluated by sensitivity, specificity and accuracy. Variable importance was assessed by averaging the impurity decrease to account for the effect of different factors. Results Results indicate that high BMI, neck circumference and waist circumference decreased the duration of slow-wave sleep and increased the sleep disorder indices and the percentage of wake and N1. Additionally, screening models for different gender and age utilizing anthropometric features as predictors via RF were established and demonstrated to have high accuracy (75.63% for younger males, 74.72% for elder males, 78.81% for younger females, and 72.10% for elder females). Feature importance indicated that waist circumference was the highest contributing factor in females and elder males, whereas the BMI was the highest contribution in younger males. Conclusions The authors recommend the use of the prediction models for those with Han-Taiwanese craniofacial features.

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Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

Cited by 31 publications

References 45 publications

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

<p>Sleep-disordered breathing and comorbidities: role of the upper airway and craniofacial skeleton</p>

Risk Screening of Obstructive Sleep Apnea Syndrome by Body Profiles via Random Forests Model

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