Department of Otolaryngology, Ajou University School of Medicine, Suwon, Republic of Korea.
Sleep Center, Ajou University Hospital, Suwon, Republic of Korea.
PLoS One. 2021 Feb 2;16(2):e0246399. doi: 10.1371/journal.pone.0246399. eCollection 2021.
Obstructive sleep apnea is a highly prevalent cyclic repetitive hypoxia-normoxia respiratory sleep disorder characterized by intermittent upper-airway collapse. It is mainly diagnosed using in-laboratory polysomnography. However, the time-spatial constraints of this procedure limit its application. To overcome these limitations, there have been studies aiming to develop clinical prediction formulas for screening of obstructive sleep apnea using the risk factors for this disorder. However, the applicability of the formula is restricted by the group specific factors included in it. Therefore, we aimed to assess the risk factors for obstructive sleep apnea and develop clinical prediction formulas, which can be used in different situations, for screening and assessing this disorder. We enrolled 3,432 Asian adult participants with suspected obstructive sleep apnea who had successfully undergone in-laboratory polysomnography. All parameters were evaluated using correlation analysis and logistic regression. Among them, age, sex, hypertension, diabetes mellitus, anthropometric factors, Berlin questionnaire and Epworth Sleepiness Scale scores, and anatomical tonsil and tongue position were significantly associated with obstructive sleep apnea. To develop the clinical formulas for obstructive sleep apnea, the participants were divided into the development (n = 2,516) and validation cohorts (n = 916) based on the sleep laboratory visiting date. We developed and selected 13 formulas and divided them into those with and without physical examination based on the ease of application; subsequently, we selected suitable formulas based on the statistical analysis and clinical applicability (formula including physical exam: sensitivity, 0.776; specificity, 0.757; and AUC, 0.835; formula without physical exam: sensitivity, 0.749; specificity, 0.770; and AUC, 0.839). Analysis of the validation cohort with developed formulas showed that these models and formula had sufficient performance and goodness of fit of model. These tools can effectively utilize medical resources for obstructive sleep apnea screening in various situations.
阻塞性睡眠呼吸暂停是一种高度流行的周期性反复低氧-正常氧呼吸睡眠障碍,其特征为间歇性上气道塌陷。它主要通过实验室多导睡眠图进行诊断。然而,该程序的时空限制限制了其应用。为了克服这些限制,已经有研究旨在开发用于阻塞性睡眠呼吸暂停筛查的临床预测公式,这些公式使用该疾病的危险因素。然而,公式的适用性受到其中包含的组特定因素的限制。因此,我们旨在评估阻塞性睡眠呼吸暂停的危险因素,并开发可用于不同情况的临床预测公式,以筛查和评估这种疾病。我们纳入了 3432 名亚洲成年疑似阻塞性睡眠呼吸暂停患者,他们均成功进行了实验室多导睡眠图检查。使用相关分析和逻辑回归评估所有参数。其中,年龄、性别、高血压、糖尿病、人体测量因素、柏林问卷和 Epworth 嗜睡量表评分以及解剖扁桃体和舌位与阻塞性睡眠呼吸暂停显著相关。为了开发阻塞性睡眠呼吸暂停的临床公式,根据睡眠实验室就诊日期,将参与者分为开发队列(n = 2516)和验证队列(n = 916)。我们开发并选择了 13 个公式,并根据应用的便利性将其分为包含和不包含体检的公式;随后,根据统计分析和临床适用性选择合适的公式(包含体检的公式:敏感性为 0.776;特异性为 0.757;AUC 为 0.835;不包含体检的公式:敏感性为 0.749;特异性为 0.770;AUC 为 0.839)。对开发公式的验证队列进行分析表明,这些模型和公式具有足够的性能和模型拟合优度。这些工具可以有效地利用医疗资源在各种情况下进行阻塞性睡眠呼吸暂停筛查。