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Published on: December 6, 2016
Craniofacial Development Characteristics in Children with Obstructive Sleep Apnea for Establishment and External
Yonglong Su1, Zitong Wang1, Huanhuan Chang2
1Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Insights
This study identifies key craniofacial differences in children with obstructive sleep apnea (OSA), developing prediction models to aid in early identification of pediatric OSA.
Area of Science:
- Craniofacial development
- Pediatric sleep medicine
- Otolaryngology
Background:
- Obstructive sleep apnea (OSA) in children can impact craniofacial development.
- Understanding these developmental characteristics is crucial for diagnosis and management.
- Current diagnostic methods may not fully capture the craniofacial contributions to pediatric OSA.
Purpose of the Study:
- To analyze craniofacial and soft tissue developmental characteristics in children with obstructive sleep apnea (OSA).
- To establish and evaluate a clinical prediction model for pediatric OSA based on these characteristics.
- To investigate age-specific variations in predictive parameters for pediatric OSA.
Main Methods:
- Retrospective study of 747 children (2-12 years): 337 with OSA and 410 controls.
- Lateral head radiographs analyzed for cephalometric measurements.
- Clinical prediction model developed using LASSO regression and externally validated.
Main Results:
- Children with OSA exhibited distinct craniofacial features, including higher body mass, larger tonsils, altered airway dimensions (AN ratio, soft palate), and specific skeletal angles (SNA, SNB).
- A prediction model for ages 2-12 years showed an AUC of 0.812.
- Age-specific models demonstrated varying predictive accuracy: preschool (AUC 0.769) and school-aged (AUC 0.854).
Conclusions:
- Craniofacial structures (hyoid, maxilla, mandible, soft palate) play a significant role in pediatric OSA.
- Age-stratified predictive models are essential due to varying parameters across different age groups.
- The developed prediction model can assist healthcare practitioners in the rapid identification of pediatric OSA.
Purpose:
Aimed to analyze the developmental characteristics of craniofacial structures and soft tissues in children with obstructive sleep apnea (OSA) and to establish and evaluate prediction model.
Methods:
It's a retrospective study comprising 747 children aged 2-12 years (337 patients and 410 controls) visited the Department of Otolaryngology-Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University (July 2017 to March 2024). Lateral head radiographs were obtained to compare the cephalometric measurements. The clinical prediction model was constructed using LASSO regression analysis. We analyzed 300 children from the Xi'an Children's Hospital for external validation.
Results:
Children with OSA had a higher body mass, a higher tonsil grade, larger AN ratio (ratio of the adenoids to the skeletal upper airway width), larger radius of the tonsils, a smaller angle between the skull base and maxilla (SNA) and smaller angle between the skull base and mandible (SNB), a larger distance from the hyoid to the mandibular plane (H-MP) and smaller distance between the third cervical vertebra and hyoid (H-C), a larger thickness of the soft palate (SPT) and smaller inclination angle of the soft palate than those of the controls (all p < 0.05). A prediction model was constructed for 2-12 years group (AUC of 0.812 [95% CI: 0.781-0.842]). Age-specific prediction models were developed for preschool children (AUC of 0.769 [95% CI: 0.725-0.814]), for school-aged children (AUC of 0.854 [95% CI: 0.812-0.895]).
Conclusion:
Our study findings support the important role of craniofacial structures such as the hyoid, maxilla, mandible, and soft palate in pediatric OSA. Age-stratified predictive models for pediatric OSA indicated varying parameters across different age groups which underscore the necessity for stratifying by age in future research. The prediction model designed will greatly assist health care practitioners with rapidly identifying.
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