Nature and science of sleep · 2023
Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea.
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Wang Huijun, Xu Wen, Zhao Anqi, Sun Dance, Li Yanru, Han Demin
Аннотация
PURPOSE: Distinguishing obstructive sleep apnea (OSA) in a high-risk population remains challenging. This study aimed to investigate clinical features to identify children with OSA combined with craniofacial photographic analysis.
METHODS: One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis.
RESULTS: The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5-98.4%).
CONCLUSION: A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.
METHODS: One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis.
RESULTS: The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5-98.4%).
CONCLUSION: A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.