Characteristics and risk factors of children with sleep-disordered breathing in Wuxi, China

Pediatric sleep-disordered breathing in Shanghai: characteristics, independent risk factors and its association with malocclusion

OBJECTIVES

This study aimed to determine the prevalence and independent risk factors of SDB, and explore its association with malocclusion among 6-11-year-old children in Shanghai, China.

METHODS

A cluster sampling procedure was adopted in this cross-sectional study. Pediatric Sleep Questionnaire (PSQ) was applied to evaluate the presence of SDB. Questionnaires including PSQ, medical history, family history, and daily habits/environment were completed by parents under instruction, and oral examinations were implemented by well-trained orthodontists. Multivariable logistic regression was applied to identify independent risk factors for SDB. Chi-square tests and Spearman's Rank Correlation were used to estimate the relationship between SDB and malocclusion.

RESULTS

A total of 3433 subjects (1788 males and 1645 females) were included in the study. The SDB prevalence was about 17.7%. Allergic rhinitis (OR 1.39, 95% CI 1.09-1.79), adenotonsillar hypertrophy (OR 2.39, 95% CI 1.82-3.19), paternal snoring (OR 1.97, 95% CI 1.53-2.53), and maternal snoring (OR 1.35, 95% CI 1.05-1.73) were independent risk factors for SDB. The SDB prevalence was higher in children with retrusive mandibles than in proper or excessive ones. No significant difference was observed in the correlation between SDB and lateral facial profile, mandible plane angle, constricted dental arch form, the severity of anterior overjet and overbite, degree of crowding and spacing, and the presence of crossbite and open bite.

CONCLUSIONS

The prevalence of SDB in primary students in the Chinese urban population was high and highly associated with mandible retrusion. The independent risk factors included Allergic rhinitis, adenotonsillar hypertrophy, paternal snoring, and maternal snoring. More efforts should be made to enhance public education about SDB and related dental-maxillofacial abnormalities.

Facial Emotion Recognition Deficit in Children with Moderate/Severe Obstructive Sleep Apnea

Although previous studies have reported a facial expression classification deficit among adults with SDB, we do not know whether these findings can be generalized to children. In our study, children with sleep-disordered breathing (SDB) were divided into three groups: primary snoring (n = 51), mild obstructive sleep apnea (OSA) (n = 39), and moderate/severe OSA (n = 26). All participants, including 20 healthy controls, underwent an overnight polysomnography recording and the Emotional Expression Recognition Task. Psychosocial problems were evaluated using the parent-reported Strengths and Difficulties Questionnaire (SDQ). There was a borderline significant interaction between expression category and group on reaction times. Further analysis revealed that positive classification advantage (PCA) disappeared in the moderate/severe OSA group, whereas it persisted in the control, primary snoring, and mild OSA groups. Emotional symptoms were positively correlated with OAHI. In both the happy and sad conditions, RT was negatively related to age and body mass index (BMI) but was independent of the obstructive apnea-hypopnea index (OAHI), arterial oxygen (SaO2) and total sleep time. The accuracy of identifying a sad expression was negatively related to conduct problems. Children with moderate/severe OSA exhibited dysfunction in facial expression categorization, which could potentially affect social communication ability.

Paediatric asthma and non-allergic comorbidities: A review of current risk and proposed mechanisms

It is increasingly recognized that children with asthma are at a higher risk of other non-allergic concurrent diseases than the non-asthma population. A plethora of recent research has reported on these comorbidities and progress has been made in understanding the mechanisms for comorbidity. The goal of this review was to assess the most recent evidence (2016-2021) on the extent of common comorbidities (obesity, depression and anxiety, neurodevelopmental disorders, sleep disorders and autoimmune diseases) and the latest mechanistic research, highlighting knowledge gaps requiring further investigation. We found that the majority of recent studies from around the world demonstrate that children with asthma are at an increased risk of having at least one of the studied comorbidities. A range of potential mechanisms were identified including common early life risk factors, common genetic factors, causal relationships, asthma medication and embryologic origins. Studies varied in their selection of population, asthma definition and outcome definitions. Next, steps in future studies should include using objective measures of asthma, such as lung function and immunological data, as well as investigating asthma phenotypes and endotypes. Larger complex genetic analyses are needed, including genome-wide association studies, gene expression-functional as well as pathway analyses or Mendelian randomization techniques; and identification of gene-environment interactions, such as epi-genetic studies or twin analyses, including omics and early life exposure data. Importantly, research should have relevance to clinical and public health translation including clinical practice, asthma management guidelines and intervention studies aimed at reducing comorbidities.

Sex differences in the relationship of sleep-disordered breathing and asthma control among children with severe asthma

OBJECTIVE

Children with severe asthma are underrepresented in studies of the relationship of sleep-disordered breathing (SDB) and asthma and little is known about sex differences of these relationships. We sought to determine the relationship of SDB with asthma control and lung function among boys and girls within a pediatric severe asthma cohort.

METHODS

Patients attending clinic visits at the Boston Children's Hospital Pediatric Severe Asthma Program completed the Pediatric Sleep Questionnaire (PSQ), Asthma Control Test (ACT) and Spirometry. The prevalence of SDB was defined as a PSQ score >0.33. We analyzed the association between PSQ score and both ACT score and spirometry values in mixed effect models, testing interactions for age and sex.

RESULTS

Among 37 subjects, mean age was 11.8 years (4.4) and 23 (62.2%) were male, the prevalence of SDB was 43.2% (16/37). Including all 80 observations, there was a moderate negative correlation between PSQ and ACT scores (r=-0.46, p < 0.001). Multivariable linear regression models revealed a significant sex interaction with PSQ on asthma control (p = 0.003), such that for each 0.10 point increase in PSQ there was a 1.88 point decrease in ACT score for females but only 0.21 point decrease in ACT score for males. A positive PSQ screen was associated with a 9.44 point (CI 5.54, 13.34, p < 0.001) lower ACT score for females and a 3.22 point (CI 0.56, 5.88, p = 0.02) lower score for males.

CONCLUSIONS

SDB is common among children with severe asthma. Among children with severe asthma, SDB in girls portends to significantly worse asthma control than boys.

Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.1897838.

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

PURPOSE

To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB).

PATIENTS AND METHODS

Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model.

RESULTS

The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively.

CONCLUSION

This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.

Prevalence of sleep-disordered breathing and associated risk factors in primary school children in urban and rural environments

INTRODUCTION

Sleep-disordered breathing (SDB) in primary school children is a significant problem, yet its prevalence is not well known outside large urban settings. Information on the burden and risk factors of SDB in children could be used to improve resource allocation when providing care across a large country. The objectives of this study were to assess the prevalence of SDB among school-aged children comparing rural and urban settings, and to investigate associated risk factors.

METHODS

In this cross-sectional study, a random sample of primary school students in Turkey were selected from urban and rural areas and data were collected using the Pediatric Sleep Questionnaire, asthma, and allergic rhinitis questionnaires completed by the parents.

RESULTS

Questionnaires were collected from a total of 139 schools from 58 provinces. A total of 11,013 students were contacted, and 9045 (73%) completed the study. There was no difference in the prevalence of SDB between rural and urban settings (16% vs. 15%, p = 0.612). Multivariate logistic regression analysis revealed that current wheezing, current rhinoconjunctivitis, being overweight, parental snoring, and current mold at home were significant risk factors for SDB in both rural and urban children. Current tobacco smoke exposure (OR = 1.48, 95%CI = 1.19-1.85), near roadway air pollution exposure (OR = 1.40, 95%CI = 1.108-1.791), and mold at home in the first year of life (OR = 1.68, 95%CI = 1.26-2.23) were associated with SDB in urban children. History of maternal/paternal adenotonsillectomy was a significant predictor of SDB in the rural setting (OR = 1.63, 95%CI = 1.12-2.39).

CONCLUSION

The prevalence of SDB is high in children living in both settings but associated risk factors may vary. Children residing in rural areas should also be screened for sleep-disordered breathing during routine health visits.