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已发表论文
儿童阻塞性睡眠呼吸暂停的头影测量和多导睡眠图特征的综合评估：一项横断面研究
Authors Liu A, Lu Q, Huang X, Li H, Hou Z, Lin W, Huang T, Cui Y, Wu T, Xiao Y, Patil S , Yang Q
Received 20 October 2025
Accepted for publication 14 December 2025
Published 8 January 2026 Volume 2026:18 574045
DOI https://doi.org/10.2147/NSS.S574045
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Ahmed BaHammam
Ailiang Liu,1 Qinghua Lu,1 Xiao Huang,1 Hairui Li,1 Zhijiang Hou,1 Weinan Lin,1 Ting Huang,1 Yunfei Cui,1 Tao Wu,1 Yaxin Xiao,1 Sandip Patil,2,3,* Qin Yang1,*
1Department of Respiratory Medicine, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 2Department of Haematology and Oncology, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 3Paediatric Research Institute, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Sandip Patil, Email sandippatil1309@yahoo.com Qin Yang, Email yangqin0539@sina.com
Objective: This study aimed to characterize pediatric patients with suspected sleep-disordered breathing (SDB) and evaluate whether cephalometric features and clinical indicators can predict obstructive sleep apnea (OSA) severity, as confirmed by polysomnography (PSG).
Methods: A cross-sectional study was conducted among 228 children referred for sleep-related breathing concerns. Data collected included demographics, clinical symptoms (snoring, mouth-breathing, open-mouth posture, malocclusion), and cephalometric measurements (ANB, SNA, SNB angles, and nasopharyngeal soft tissue ratio). Overnight PSG assessed apnea–hypopnea index (AHI), rapid eye movement (REM) sleep proportion, and oxygen saturation. Children were classified as OSA-positive (AHI > 1 event/h) or non-OSA. Statistical analyses included t-tests, ANOVA, Pearson correlations, logistic regression, receiver operating characteristic (ROC) analysis, principal component analysis (PCA), and decision-tree modelling to identify predictors and stratify risk.
Results: Among 228 children (mean age 7.4 ± 2.9 years; 62.7% male), 109 underwent PSG. The OSA group showed significantly higher ANB angles (5.8° ± 1.9 vs 3.9° ± 1.4, p < 0.01), increased nasopharyngeal soft tissue ratios (0.72 ± 0.09 vs 0.59 ± 0.07, p < 0.001), and elevated BMI compared with non-OSA children. AHI correlated positively with ANB (r = 0.48) and soft tissue ratio (r = 0.62), and negatively with minimum SaO2 (r = – 0.47). Habitual snoring (OR = 4.3), mouth-breathing (OR = 2.6), and incomplete lip closure (OR = 3.1) independently predicted OSA. A combined clinical–cephalometric model achieved an AUC of 0.86. PCA revealed distinct anatomical–functional clusters, and a decision-tree model using ANB > 5°, tissue ratio ≥ 0.68, and snoring classified OSA with 82% sensitivity and 78% specificity.
Conclusion: Pediatric OSA is strongly linked to craniofacial imbalance, adenoidal hypertrophy, and functional symptoms. Cephalometric parameters, particularly ANB angle and nasopharyngeal soft tissue ratio, combined with simple clinical indicators, offer reliable, low-cost screening for early OSA detection where PSG access is limited.
Keywords: paediatric obstructive sleep apnea, cephalometric analysis, polysomnography, airway anatomy, clinical predictors
1Department of Respiratory Medicine, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 2Department of Haematology and Oncology, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 3Paediatric Research Institute, Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Sandip Patil, Email sandippatil1309@yahoo.com Qin Yang, Email yangqin0539@sina.com
Objective: This study aimed to characterize pediatric patients with suspected sleep-disordered breathing (SDB) and evaluate whether cephalometric features and clinical indicators can predict obstructive sleep apnea (OSA) severity, as confirmed by polysomnography (PSG).
Methods: A cross-sectional study was conducted among 228 children referred for sleep-related breathing concerns. Data collected included demographics, clinical symptoms (snoring, mouth-breathing, open-mouth posture, malocclusion), and cephalometric measurements (ANB, SNA, SNB angles, and nasopharyngeal soft tissue ratio). Overnight PSG assessed apnea–hypopnea index (AHI), rapid eye movement (REM) sleep proportion, and oxygen saturation. Children were classified as OSA-positive (AHI > 1 event/h) or non-OSA. Statistical analyses included t-tests, ANOVA, Pearson correlations, logistic regression, receiver operating characteristic (ROC) analysis, principal component analysis (PCA), and decision-tree modelling to identify predictors and stratify risk.
Results: Among 228 children (mean age 7.4 ± 2.9 years; 62.7% male), 109 underwent PSG. The OSA group showed significantly higher ANB angles (5.8° ± 1.9 vs 3.9° ± 1.4, p < 0.01), increased nasopharyngeal soft tissue ratios (0.72 ± 0.09 vs 0.59 ± 0.07, p < 0.001), and elevated BMI compared with non-OSA children. AHI correlated positively with ANB (r = 0.48) and soft tissue ratio (r = 0.62), and negatively with minimum SaO2 (r = – 0.47). Habitual snoring (OR = 4.3), mouth-breathing (OR = 2.6), and incomplete lip closure (OR = 3.1) independently predicted OSA. A combined clinical–cephalometric model achieved an AUC of 0.86. PCA revealed distinct anatomical–functional clusters, and a decision-tree model using ANB > 5°, tissue ratio ≥ 0.68, and snoring classified OSA with 82% sensitivity and 78% specificity.
Conclusion: Pediatric OSA is strongly linked to craniofacial imbalance, adenoidal hypertrophy, and functional symptoms. Cephalometric parameters, particularly ANB angle and nasopharyngeal soft tissue ratio, combined with simple clinical indicators, offer reliable, low-cost screening for early OSA detection where PSG access is limited.
Keywords: paediatric obstructive sleep apnea, cephalometric analysis, polysomnography, airway anatomy, clinical predictors