Therapy Center of Obstructive Sleep Apnea, Department of Otorhinolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai, China.
Sleep Breath. 2021 Mar;25(1):171-179. doi: 10.1007/s11325-020-02014-3. Epub 2020 Apr 18.
To evaluate whether a predictive model based on nocturnal minimal oxygen saturation (SpO) alone can accurately detect the presence of obstructive sleep apnea (OSA) in a population with suspected OSA.
A total of 4297 participants with suspected OSA were enrolled in this study, and laboratory-based polysomnography (PSG) tests were performed at sea level in all subjects. Nocturnal minimal SpO was obtained automatically as part of the PSG test. Stratified sampling was used to divide the participants' data into the training set (75%) and the test set (25%). An OSA detection model based on minimal SpO alone was created using the training set data and its performance was evaluated using the independent test set data ("hold-out" evaluation). Gender-specific models, and models based on minimal SpO in combination with other predictive factors (age, body mass index, waist-to-hip ratio, snoring grade, Epworth Sleepiness Scale score, and comorbidities), were also created and compared in terms of OSA detection performance.
The prevalence of OSA was 85.6% in our study population. The models including multiple predictors, and the gender-specific models, failed to outperform the model based solely on minimal SpO, which showed good predictive performance (C statistic, 0.922) having an overall accuracy rate of 0.86, sensitivity of 0.87, specificity of 0.84, positive predictive value of 0.97, and positive likelihood ratio of 5.34. In addition, the model based on minimal SpO alone could also accurately predict the presence of moderate-to-severe OSA and severe OSA, with C statistics of 0.914 and 0.900, respectively.
A predictive model based on nocturnal minimal SpO alone may be an alternative option to detect the presence of OSA in a high-risk population when standard diagnostic tests are unavailable.
评估仅基于夜间最低血氧饱和度（SpO）的预测模型是否能够准确检测疑似阻塞性睡眠呼吸暂停（OSA）患者中 OSA 的存在。
本研究共纳入 4297 名疑似 OSA 患者，所有患者均在海平面进行实验室多导睡眠图（PSG）检查。夜间最低 SpO 作为 PSG 检查的一部分自动获取。采用分层抽样法将参与者的数据分为训练集（75%）和测试集（25%）。使用训练集数据创建基于最小 SpO 的 OSA 检测模型，并使用独立测试集数据对其性能进行评估（“保留”评估）。还创建并比较了基于最小 SpO 与其他预测因素（性别、年龄、体重指数、腰臀比、打鼾程度、Epworth 睡眠量表评分和合并症）相结合的预测模型，以评估 OSA 检测性能。
本研究人群中 OSA 的患病率为 85.6%。包括多个预测因素的模型和性别特异性模型未能优于仅基于最小 SpO 的模型，后者表现出良好的预测性能（C 统计量为 0.922），总准确率为 0.86，灵敏度为 0.87，特异性为 0.84，阳性预测值为 0.97，阳性似然比为 5.34。此外，仅基于最小 SpO 的模型还可以准确预测中重度 OSA 和重度 OSA 的存在，C 统计量分别为 0.914 和 0.900。
当标准诊断测试不可用时，基于夜间最低 SpO 的预测模型可能是一种替代方法，可用于检测高危人群中 OSA 的存在。
