Kim Seon Tae, Park Kee Hyung, Shin Seung-Heon, Kim Ji-Eun, Pae Chi-Un, Ko Kwang-Pil, Hwang Hee Young, Kang Seung-Gul
Department of Otolaryngology, Gil Medical Center, College of Medicine, Gachon University, Incheon, Republic of Korea.
Department of Neurology, Gil Medical Center, College of Medicine, Gachon University, Incheon, Republic of Korea.
Sleep Breath. 2017 Dec;21(4):885-892. doi: 10.1007/s11325-017-1506-5. Epub 2017 Apr 29.
This study developed formulas to predict obstructive sleep apnea (OSA) and the Apnea-Hypopnea Index (AHI) in Korean patients with suspected OSA using clinical, anthropometric, and cephalometric variables.
We evaluated relevant variables in 285 subjects with suspected OSA. These included demographic characteristics, sleep-related symptoms, medical history, clinical scales, anthropometric measurements including facial surface measurements, and cephalometric measurements. All participants underwent full-night laboratory polysomnography. The prediction formula for the probability of OSA was created by logistic regression analysis and confirmed by the bootstrap resampling technique. The formula for predicting the AHI was developed using multiple linear regression analysis.
The probability of having OSA was as follows: p = 1 / (1 + exponential (exp) ), where f = -16.508 + 1.445 × loudness of snoring 4 + 0.485 × loudness of snoring 3 + 0.078 × waist circumference + 0.209 × subnasale-to-stomion distance + 0.183 × thickness of the uvula (UTH) supine + 0.041 × age. The AHI prediction formula was as follows: -112.606 + 3.516 × body mass index + 0.683 × mandibular plane-hyoid supine + 10.915 × loudness of snoring 4 + 6.933 × loudness of snoring 3 + 1.297 × UTH supine + 0.272 × age.
This is the first study to establish formulas to predict OSA and the AHI in Koreans with suspected OSA using cephalometric and other variables. These results will contribute to prioritizing the order in which patients with suspected OSA are referred for polysomnography.
本研究利用临床、人体测量学和头影测量学变量，开发了预测韩国疑似阻塞性睡眠呼吸暂停（OSA）患者的OSA及呼吸暂停低通气指数（AHI）的公式。
我们评估了285名疑似OSA受试者的相关变量。这些变量包括人口统计学特征、睡眠相关症状、病史、临床量表、人体测量指标（包括面部表面测量）以及头影测量指标。所有参与者均接受了整夜实验室多导睡眠监测。通过逻辑回归分析创建了OSA概率的预测公式，并采用自助重采样技术进行了验证。使用多元线性回归分析开发了预测AHI的公式。
患OSA的概率如下：p = 1 / (1 + 指数函数（exp）)，其中f = -16.508 + 1.445×打鼾响度4 + 0.485×打鼾响度3 + 0.078×腰围 + 0.209×鼻下点至口裂距离 + 0.183×悬雍垂厚度（UTH）仰卧位 + 0.041×年龄。AHI预测公式如下：-112.606 + 3.516×体重指数 + 0.683×下颌平面至舌骨仰卧位 + 10.915×打鼾响度4 + 6.933×打鼾响度3 + 1.297×UTH仰卧位 + 0.272×年龄。
这是第一项利用头影测量学和其他变量建立预测韩国疑似OSA患者的OSA及AHI公式的研究。这些结果将有助于确定疑似OSA患者进行多导睡眠监测的优先顺序。
