Wang Huijun, Xu Wen, Zhao Anqi, Sun Dance, Li Yanru, Han Demin
Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.
Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China.
Nat Sci Sleep. 2023 Mar 15;15:115-125. doi: 10.2147/NSS.S400745. eCollection 2023.
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.
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.
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%).
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.
在高危人群中鉴别阻塞性睡眠呼吸暂停(OSA)仍然具有挑战性。本研究旨在调查临床特征,以识别患有OSA的儿童,并结合颅面部摄影分析。
纳入145名儿童(30名对照,62名原发性打鼾儿童,53名OSA儿童)。比较各组一般人口统计学特征和面部表面形态的差异。使用逻辑回归分析建立确定OSA(阻塞性睡眠呼吸暂停低通气指数>1)存在的危险因素和预测模型。
与原发性打鼾儿童和对照相比,OSA儿童的BMI(z评分)、扁桃体肥大和下脸宽度(校正年龄、性别和BMI z评分)有显著差异(校正p<0.05)。基于临床特征和摄影测量的筛查模型正确分类了79.3%的儿童,敏感性为64.2%,特异性为89.1%。该模型的曲线下面积为81.0(95%CI,73.5-98.4%)。
如果在资源紧张的医疗系统中无法进行多导睡眠图检查,基于临床特征和摄影测量的筛查模型将有助于对高度怀疑OSA的儿童进行临床决策。