Perri Rita A, Kairaitis Kristina, Cistulli Peter, Wheatley John R, Amis Terence C
Ludwig Engel Centre for Respiratory Research, Department of Respiratory and Sleep Medicine, Sydney, Australia,
Sleep Breath. 2014 Mar;18(1):39-52. doi: 10.1007/s11325-013-0845-0. Epub 2013 Apr 13.
We used statistical modelling to probe the contributions of anthropometric and surface cephalometric variables to the OSA phenotype.
The design is prospective cohort study.
The setting is community-based and sleep disorder laboratory.
Study #1-Model development study: 147 healthy asymptomatic volunteers (62.6 % Caucasian; age, 18-76 years; 81 females; median multivariable apnea prediction index=0.15) and 140 diagnosed OSA patients (84.3 % Caucasian; age, 18-83 years; 41 females; polysomnography [PSG] determined apnea-hypopnea index >10 events/h). Study #2-Model test study: 345 clinic patients (age, 18-86 years; 129 females) undergoing PSG for diagnosis of OSA.
We measured 10 anthropometric and 34 surface cephalometric dimensions (calipers) and calculated mandibular enclosure volumes for study #1 and recorded age and neck circumference for study #2. Statistical modelling included principal component (PC), logistic regression, and receiver-operator curve analyses.
Model development study: A regression model incorporating three identified PC predicted OSA with 88 % sensitivity and specificity. However, a simplified model based on age and NC alone was equally effective (87 % sensitivity and specificity). Model test study: The simplified model predicted OSA with high sensitivity (93 %) but poor specificity (21 %).
We conclude that in our clinic-based cohort, craniofacial bony and soft tissue structures (excluding neck anatomy) do not play a substantial role in distinguishing patients with OSA from those without. This may be because craniofacial anatomy does not contribute greatly to the pathogenesis of OSA in this group or because referral bias has created a relatively homogeneous phenotypic population.
我们使用统计模型来探究人体测量学和表面头影测量变量对阻塞性睡眠呼吸暂停（OSA）表型的影响。
前瞻性队列研究。
基于社区的睡眠障碍实验室。
研究1-模型开发研究：147名健康无症状志愿者（62.6%为白种人；年龄18 - 76岁；81名女性；多变量呼吸暂停预测指数中位数 = 0.15）和140名确诊的OSA患者（84.3%为白种人；年龄18 - 83岁；41名女性；多导睡眠图[PSG]确定呼吸暂停低通气指数>10次/小时）。研究2-模型测试研究：345名接受PSG以诊断OSA的临床患者（年龄18 - 86岁；129名女性）。
我们测量了研究1中的10项人体测量学指标和34项表面头影测量尺寸（卡尺测量）并计算下颌围合体积，以及记录研究2中的年龄和颈围。统计模型包括主成分（PC）、逻辑回归和受试者工作特征曲线分析。
模型开发研究：一个纳入三个已识别主成分的回归模型预测OSA的灵敏度和特异度为88%。然而，一个仅基于年龄和颈围的简化模型同样有效（灵敏度和特异度为87%）。模型测试研究：简化模型预测OSA的灵敏度高（93%）但特异度低（21%）。
我们得出结论，在我们基于临床的队列中，颅面骨和软组织结构（不包括颈部解剖结构）在区分OSA患者和非OSA患者方面不起重要作用。这可能是因为颅面解剖结构对该组中OSA的发病机制贡献不大，或者是因为转诊偏倚导致了一个相对同质的表型群体。
