台灣地區阻塞型睡眠呼吸中止症病患之臨床預測模式

A Clinical Predictive Model in the Evaluation of Patients with Obstructive Sleep Apnea in Taiwan

楊啟坤(Yang, Chi_Kun)

指導教授 ： 王俊毅

亞洲大學/健康學院/長期照護研究所在職專班/碩士(2011年)

摘要

目的: 以人體測量學、人口學及臨床變項來建立台灣地區阻塞型睡眠呼吸中止症風險及嚴重程度之預測模式。

方法: 於2009年1月至2009年12月期間，蒐集台灣台中六家睡眠檢查中心因疑似睡眠呼吸中止症就診之病患，共有4027名病患納入本研究。在進行多重睡眠生理檢查前，先記錄其性別、年齡、身高、體重、頸圍、血壓、Friedman舌位分類(Friedman tongue position, FTP)及嗜睡量表(Epworth sleepiness scale, ESS)。建立羅吉斯迴歸來判別阻塞型睡眠呼吸中止症，並透過複迴歸尋找睡眠暫停-低息指數(apnea-hypopnea index, AHI)之預測因子及發展AHI之預測公式。

結果: Friedman舌位分類、頸圍、年齡、身體質量指數(Body mass index, BMI)、性別、嗜睡量表及血壓為阻塞型睡眠呼吸中止症之預測因子。利用此預測因子所建立的診斷模式，其正確率可達 92%，其敏感度和特異度分別為83.4%和88.8%。利用相同預測因子建立出AHI之預測公式: lnAHI = -0.982+ FTP分數 ( FTP I: 0, FTP II: 1.171, FTP III: 1.989, FTP IV 2.419) + 0.024×頸圍 + 0.008×年齡 + 0.029×身體質量指數 + 0.269×性別 + 0.010×嗜睡量表 + 血壓分數 (正常: 0，高血壓前期: 0.108，高血壓: 0.158)。以AHI ≥ 5為切點，來判斷患者是否罹患阻塞型睡眠呼吸中止症，有84.7%的病患可以被正確判斷，模式敏感度和特異度分別為86.7%和82.7%。
結論: FTP、頸圍、年齡、身體質量指數、男性、嗜睡量表及血壓為台灣地區阻塞型睡眠呼吸中止症風險性之預測因子。我們利用此研究發展出來的模式可以運用於台灣地區做為篩檢阻塞型睡眠呼吸中止症之工具。

關鍵字

阻塞型性睡眠呼吸中止症 ； 預測模式 ； Friedman舌位分類

並列摘要

Objectives: We sought to establish a predictive model, based on anthropometric, clinical and epidemiological parameters, to predict the risk and the severity of obstructive sleep apnea (OSA) in Taiwan.

Methods: Four thousand and twenty seven patients with suspected OSA from six sleep laboratories in Taichung in Taiwan during the period between January 2009 and December 2009 were enrolled. We obtained gender, age, weight, height, neck circumference (NC), blood pressure, Friedman tongue position (FTP) and Epworth sleepiness scale (ESS) before diagnostic polysomnography (PSG). Multivariate logistic regression was used to establish a diagnostic model of OSA and stepwise linear regression was used to identify independent predictors of apnea-hypopnea index (AHI) and develop a predictive formula.

Results: Friedman tongue position, NC, age, BMI, gender, ESS and blood pressure were significant predictors for OSA. The diagnostic model derived from logistic regression displayed an accuracy rate of 92.2% for OSA, with a sensitivity of 83.4% and a specificity of 88.8%. Based on the same predictors, we obtained a formula for AHI prediction: lnAHI = -0.982+ FTP score (FTP I: 0, FTP II: 1.171, FTP III: 1.989, FTP IV 2.419) + 0.024×NC + 0.008×Age + 0.029×BMI + 0.269×Gender + 0.010×ESS + BP3 score (normal: 0, prehypertensive: 0.108, hypertension: 0.158). With a cutoff as AHI ≥ 5, this model correctly predicted 84.7% of patients with or without OSA and obtained a sensitivity of 86.7% and a specificity of 84.7%.

Conclusions: The presented study provided a clinical predictive model for OSA in Taiwan subjects based on the significant predictors as FTP, NC, age, BMI, gender, ESS and BP. This clinical predictive model provides a fast and inexpensive tool to screen OSA with an accuracy of 92.2%.

並列關鍵字

Obstructive sleep apnea ； predictive model ； Friedman tongue position

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