阻塞性睡眠呼吸暂停低通气综合征的临床预测模型

A predictive model for the obstructive sleep apnea hypopnea syndrome

目的根据阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者的常见临床表现和脉搏血氧仪监测的数据,建立可靠性较高的临床预测、筛检模型。方法采用病例对照研究,随机抽取非OSAHS组受试者129例,OSAHS组受试者107例,用logistic回归法建立回归方程。作出ROC曲线,选择预测患病概率的诊断点。结果患病概率P=ex/(1+ex),x=-1977+127a-113b+013c,其中a代表有无夜间憋醒(有夜间憋醒=1,无夜间憋醒=0),b代表性别(男性=1,女性=2),c代表氧减饱和指数,将P>015作为OSAHS的诊断点,敏感性为963%,特异性约50%。结论疑诊为OSAHS患者的临床特点结合氧减饱和指数,可以较好的预测、筛检OSAHS患者。

Objective To establish a relatively reliable predictive model for obstructive sleep apnea hypopnea syndrome (OSAHS) based on clinical presentations and pulse oximetry data in OSAHS patients.MethodsA case control study was conducted in 107 OSAHS patients and 129 non OSAHS patients.Their data were analysized by logistic regression and regression equation was established.The cutoff point was determined by receive operator characteristic curve (ROC).Results The predicted probability of OSAHS was calculated using the following equation:P=e x /(1+ e x ),x=-1 977+1 27a-1 13b+0 13c a=1 when respiratory effort related arousal is reported and a=0 when it is not reported;b=1 when the subjects is male and 2 when the subjects is female;c=DI (desaturation index ).A probability cutoff point=0 15 was determined.When the probabilities were greater than 0 15,the diagnosis of OSAHS was made with a sensitivity of 96 3% and a specificity of about 50%.Conclusion This study demonstrate that a predictive model based on clinical features and pulse oximetry data is a valuable tool to predict and screen OSAHS in suspected patients.