Acoustic Pharyngometry Measurement of Minimal Cross-Sectional Airway Area Is a Significant Independent Predictor of Moderate-To-Severe Obstructive Sleep Apnea
Acoustic Pharyngometry Measurement of Minimal Cross-Sectional Airway Area Is a Significant Independent Predictor of Moderate-To-Severe Obstructive Sleep Apnea
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DOI：
10.5664/jcsm.3158
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发表时间：
2013-01-01
影响因子：
4.3
通讯作者：
Malhotra, Atul
中科院分区：
文献类型：
--
作者：
DeYoung, Pamela N.;Bakker, Jessie P.;Malhotra, Atul
研究目标：目前诊断阻塞性睡眠呼吸暂停 (OSA) 的金标准方法是多导睡眠图，但效率较低。因此，我们试图确定一种对有 OSA 风险的患者进行分类的方法，而不使用容易出现误报的主观数据。我们假设咽声测量与年龄、性别和颈围相结合可以预测中度至重度 OSA 的存在。方法：从当地睡眠诊所招募未经治疗的疑似 OSA 受试者并进行多导睡眠图检查。我们还设立了一个对照组来验证差异。坐直并通过嘴呼吸时，使用声学咽喉计测量呼气末时上呼吸道的最小横截面积 (MCA)。结果：招募了 60 名受试者（35 名男性，平均年龄 42 岁，范围 21-81 岁；呼吸暂停低通气指数 (AHI) 33 +/- 30 次事件/小时（平均值 +/- 标准差），Epworth 嗜睡量表得分 11 +/- 6，体重指数 34 +/- 8 kg/m(2))。在单变量逻辑回归中，MCA 是轻度无 OSA (AHI < 15) 的显着预测因子。包括 MCA、年龄、性别和颈围的多变量逻辑回归模型显着预测 AHI < 15，解释了总方差的大约三分之一 (chi(2)(4) = 37，p < 0.01），只有 MCA 是显着的独立预测因子（调整优势比 54，标准误差 130；p < 0.01）。 结论：这些数据表明，客观解剖学评估独立于年龄、性别和颈部尺寸可以在临床环境中显着区分轻度和中度至重度 OSA 患者，并且可以作为 OSA 风险分层的一个组成部分。
Study Objectives: The current gold-standard method of diagnosing obstructive sleep apnea (OSA) is polysomnography, which can be inefficient. We therefore sought to determine a method to triage patients at risk of OSA, without using subjective data, which are prone to mis-reporting. We hypothesized that acoustic pharyngometry in combination with age, gender, and neck circumference would predict the presence of moderate-to-severe OSA.Methods: Untreated subjects with suspected OSA were recruited from a local sleep clinic and underwent polysomnography. We also included a control group to verify differences. While seated in an upright position and breathing through the mouth, an acoustic pharyngometer was used to measure the minimal cross-sectional area (MCA) of the upper airway at end-exhalation.Results: Sixty subjects were recruited (35 males, mean age 42 years, range 21-81 years; apnea-hypopnea index (AHI) 33 +/- 30 events/h (mean +/- standard deviation), Epworth Sleepiness Scale score 11 +/- 6, body mass index 34 +/- 8 kg/m(2)). In univariate logistic regression, MCA was a significant predictor of mild-no OSA (AHI < 15). A multivariate logistic regression model including MCA, age, gender, and neck circumference significantly predicted AHI < 15, explaining approximately one-third of the total variance (chi(2)(4) = 37, p < 0.01), with only MCA being a significant independent predictor (adjusted odds ratio 54, standard error 130; p < 0.01).Conclusions: These data suggest that independent of age, gender, and neck size, objective anatomical assessment can significantly differentiate those with mild versus moderate-to-severe OSA in a clinical setting, and may have utility as a component in stratifying risk of OSA.