青春期前正畸异常儿童患儿童阻塞性睡眠呼吸暂停的风险：一项诊断性研究

文献信息

中文摘要

背景： 本研究旨在探讨通过家庭睡眠呼吸暂停检测（HSAT）测量的呼吸暂停低通气指数（AHI）作为儿童阻塞性睡眠呼吸暂停（POSA）可能性的筛查指标、正畸错𬌗畸形的存在以及口呼吸之间的关系。
方法： 研究纳入6至12岁、(国籍)或(国籍)国籍、被转诊进行正畸检查的儿童。评估的颅颌面特征包括上颌牙弓狭窄、牙齿数量和位置异常、牙弓关系、骨骼分类、下颌生长模式以及舌活动度比率。使用夜间家庭呼吸多导图（HSAT）筛查POSA的可能性，以呼吸暂停低通气指数（AHI）作为定量主要结局；HSAT是一种筛查工具，不能替代整夜多导睡眠图（PSG）这一诊断金标准。建立逐步线性回归模型以评估临床显著变量与呼吸暂停低通气指数（AHI）之间的关系。
结果： 共有100名儿童参与研究，在HSAT筛查中分别有61%的儿童AHI符合轻度POSA，7%符合中度POSA。AHI（作为连续变量处理）与上颌牙弓狭窄、覆盖、ANB角增大、下颌拥挤和口呼吸偏好呈正相关，与舌活动度比率呈负相关（相关系数0.2-0.47）。最终线性回归模型仅保留四个参数（上颌牙弓狭窄、下颌拥挤、ANB角增大和口呼吸偏好），其识别有POSA风险儿童和中度POSA儿童的AUC分别为0.72和0.91。
结论： 我们的研究证实了POSA的多因素性质以及正畸人群中POSA风险升高。将效应量较小的颅面特征纳入模型提高了其性能，凸显了在评估POSA风险时考虑多个 contributing 因素而非孤立预测因素的重要性。

英文摘要

BACKGROUND: This study aimed to investigate the relationships among the apnea-hypopnea index (AHI) measured by home sleep apnea testing (HSAT) as a screening indicator of the likelihood of pediatric obstructive sleep apnea (POSA), the presence of orthodontic malocclusions, and oral breathing.
METHODS: Children aged 6 to 12 years, of (nationality) or (nationality) nationality, who were referred for orthodontic examination were included in the study. Evaluated cranio-maxillofacial features included maxillary arch constriction, anomalies in the number and position of teeth, dental arch relationship, skeletal class, mandibular growth pattern, and tongue range of motion ratio. The likelihood of POSA was screened using overnight home respiratory polygraphy (HSAT), with the apnea-hypopnea index (AHI) as the quantitative primary outcome; HSAT is a screening tool and does not replace full-night polysomnography (PSG), the diagnostic gold standard. A stepwise linear regression model was developed to evaluate the relationship between clinically significant variables and apnea-hypopnea index (AHI).
RESULTS: A total of 100 children participated in the study, with 61% having an AHI consistent with mild POSA and 7% with moderate POSA on HSAT screening, respectively. AHI (treated as a continuous variable) positively correlated with maxillary arch constriction, overjet, increased ANB angle, mandibular crowding, and oral breathing preference, and negatively with the tongue range of motion ratio (correlation coefficients 0.2-0.47). The final linear regression model retaining only four parameters (maxillary arch constriction, mandibular crowding, increased ANB angle, and oral breathing preference) yielded AUC of 0.72 and 0.91 for the identification of children at risk of POSA and moderate POSA, respectively.
CONCLUSIONS: Our study confirmed the multifactorial nature of POSA and the elevated risk of POSA in the orthodontic population. Including craniofacial features with small effect sizes in the model improved its performance, highlighting the importance of considering multiple contributing factors rather than isolated predictors when assessing POSA risk.