International journal of pediatric otorhinolaryngology · 2019
Identification of different clinical faces of obstructive sleep apnea in children.
СтатьяНазвание и аннотация: Перевод в очереди
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Wu Yunxiao, Feng Guoshuang, Xu Zhifei, Li Xiaodan, Zheng Li, Ge Wentong, Ni Xin
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.
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.