Zou Jianyin, Guan Jian, Yi Hongliang, Meng Lili, Xiong Yuanping, Tang Xulan, Su Kaiming, Yin Shankai
Department of Otolaryngology, The Affiliated Sixth People's Hospital, Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai, China.
PLoS One. 2013 Dec 2;8(12):e80704. doi: 10.1371/journal.pone.0080704. eCollection 2013.
Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible.
This study sought to develop a simple and efficient model for identifying OSA in Chinese adult population.
In this study, the efficiency of Epworth Sleepiness Scale (ESS) and a new established prediction model for screening OSA were evaluated in the test cohort (2,032 participants) and confirmed in an independent validation cohort (784 participants).
In the test cohort, a high specificity (82.77%, 95% confidence interval [CI], 77.36-87.35) and a moderate sensitivity (61.65%, 95% CI, 59.35-63.91) were obtained at the threshold of nine for the ESS alone. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. The new generated screening model, including age, waist circumference, ESS score, and minimum oxygen saturation (SaO2) as independent variables, revealed a higher sensitivity (89.13%, 95% CI, 87.60-90.53) and specificity (90.34%, 95% CI, 85.85-93.77) at the best cut-off point. Through receiver operating characteristics curve analysis, the area under the receiver operating characteristics curve of the model was found significantly larger than that of the ESS alone (0.955 vs. 0.774, P<0.0001). All these results were confirmed in the validation cohort.
A practical screening model comprising minimum SaO2 and other parameters could efficiently identify undiagnosed OSA from the high-risk patients. Additionally, a sex-specific difference should be considered if the ESS alone is used.
阻塞性睡眠呼吸暂停（OSA）导致高发病率和死亡率，并且与多种并发症发生风险增加独立相关。目前，OSA的诊断耗时、费力且难以实现。
本研究旨在开发一种简单有效的模型，用于识别中国成年人群中的OSA。
在本研究中，对爱泼华嗜睡量表（ESS）和一个新建立的用于筛查OSA的预测模型在测试队列（2032名参与者）中的有效性进行了评估，并在一个独立的验证队列（784名参与者）中进行了验证。
在测试队列中，仅ESS以9分为阈值时，获得了较高的特异性（82.77%，95%置信区间[CI]，77.36-87.35）和中等敏感性（61.65%，95%CI，59.35-63.91）。值得注意的是，按性别分层分析显示了不同的最佳截断点：男性为9分，女性为6分。新生成的筛查模型，包括年龄、腰围、ESS评分和最低氧饱和度（SaO2）作为自变量，在最佳截断点时显示出更高的敏感性（89.13%，95%CI，87.60-90.53）和特异性（90.34%，95%CI，85.85-93.77）。通过受试者工作特征曲线分析，发现该模型的受试者工作特征曲线下面积显著大于仅ESS的曲线下面积（0.955对0.774，P<0.0001）。所有这些结果在验证队列中均得到证实。
一个包含最低SaO2和其他参数的实用筛查模型可以有效地从高危患者中识别出未诊断的OSA。此外，如果仅使用ESS，则应考虑性别差异。
