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DOI 10.1016/j.ijporl.2019.109621

Медицинская библиотека · International journal of pediatric otorhinolaryngology · 2019

Identification of different clinical faces of obstructive sleep apnea in children.

Wu Yunxiao, Feng Guoshuang, Xu Zhifei, Li Xiaodan, Zheng Li, Ge Wentong, Ni Xin

СтатьяПеревод в очереди

Аннотация

OBJECTIVE

This study aimed to identify the heterogeneity of obstructive sleep apnea syndrome clinical presentation in children.

PARTICIPANTS

Children who were 3-14 years old and with obstructive sleep apnea syndrome after polysomnography monitoring (apnea and hypopnea index>5 or obstructive apnea index>1) in the sleep center of Beijing Children's Hospital were included.

METHODS

A sleep disorder questionnaire including different combinations of symptoms and co-morbidities of obstructive sleep apnea syndrome in children was used. A cluster analysis was used to classify the data.

RESULTS

The apnea hypopnea index alone is not adequate to predict clinical phenotypes. Based on symptoms and co-morbidities of obstructive sleep apnea syndrome, three distinct clusters were identified. They were "nocturnal snoring and daytime sleepiness group" (cluster 1), "hyperactivity group" (cluster 2), and "minimally symptomatic group" (cluster 3). A prediction model was built according to eight variables which showed statistical significance by pairwise comparison among clusters. Overall accuracy of the prediction model could reach 86%. Both the sensitivity and specificity of cluster 2 and 3 prediction were around 90%.

CONCLUSION

Children with obstructive sleep apnea syndrome have different patterns of clinical presentation and the identification of the different clinical profiles of obstructive sleep apnea syndrome can provide clues for more personalised diagnoses and therapies.

Для цитирования: Wu Yunxiao, Feng Guoshuang, Xu Zhifei, Li Xiaodan, Zheng Li, Ge Wentong, Ni Xin. Identification of different clinical faces of obstructive sleep apnea in children.. International journal of pediatric otorhinolaryngology. 2019. DOI: 10.1016/j.ijporl.2019.109621.

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