Wang Yuyu, Zou Juanjuan, Xu Huajun, Jiang Cuiping, Yi Hongliang, Guan Jian, Yin Shankai
Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai, China.
J Thorac Dis. 2022 Aug;14(8):3066-3074. doi: 10.21037/jtd-20-2027.
This study aimed to develop a more effective screening model for moderate-to-severe obstructive sleep apnea (OSA) based on the best tool among Epworth Sleepiness Scale (ESS), NoSAS score and STOP-BANG questionnaire (SBQ).
This study screened 2,031 consecutive subjects referred with suspected OSA from 2012 to 2016, including the test cohort from 2012 to 2014 and the validation cohort from 2014 to 2016. Anthropometric measurements, polysomnographic data, ESS, NoSAS scores and SBQ scores were recorded. Receiver operating characteristic curve analyses were performed and the final predictive models were verified in a validation cohort.
A total of 1,840 adults were finally included. The performance of ESS, NoSAS score and SBQ in screening OSA was compared. The diagnostic accuracy of SBQ was superior to ESS and NoSAS. A predictive model based on SBQ yielded an area under the curve (AUC) of 0.931 (95% CI: 0.915-0.946), and the sensitivity and specificity were 84.47 (95% CI: 81.4-87.2) and 87.36 (95% CI: 83.9-90.3) respectively. In the validation cohort, the AUC was 0.955 (95% CI: 0.938-0.969), with a sensitivity and specificity of 86.79 (95% CI: 83.2-89.9) and 90.88 (95% CI: 87.2-93.8) respectively. In addition, the model performed moderately in screening mild OSA with the AUC being 0.771 (95% CI: 0.721-0.815).
The SBQ was effective in screening moderate-to-severe OSA. And a SBQ -based predictive model afforded excellent diagnostic efficacy, which could be applied in clinical practice.
本研究旨在基于爱泼沃斯思睡量表（ESS）、NoSAS评分和STOP-BANG问卷（SBQ）中最佳工具，开发一种更有效的中重度阻塞性睡眠呼吸暂停（OSA）筛查模型。
本研究对2012年至2016年连续转诊的2031例疑似OSA患者进行筛查，包括2012年至2014年的测试队列和2014年至2016年的验证队列。记录人体测量数据、多导睡眠图数据、ESS、NoSAS评分和SBQ评分。进行受试者操作特征曲线分析，并在验证队列中验证最终预测模型。
最终纳入1840名成年人。比较了ESS、NoSAS评分和SBQ在筛查OSA中的表现。SBQ的诊断准确性优于ESS和NoSAS。基于SBQ的预测模型的曲线下面积（AUC）为0.931（95%CI：0.915-0.946），敏感性和特异性分别为84.47（95%CI：81.4-87.2）和87.36（95%CI：83.9-90.3）。在验证队列中，AUC为0.955（9%CI：0.938-0.969），敏感性和特异性分别为86.79（95%CI：83.2-89.9）和90.88（95%CI：87.2-93.8）。此外，该模型在筛查轻度OSA时表现中等，AUC为0.771（95%CI：0.721-0.815）。
SBQ在筛查中重度OSA方面有效。基于SBQ的预测模型具有出色的诊断效能，可应用于临床实践。
