Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Laboratorio do Sono, Instituto do Coracao (InCor), Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.
Sleep. 2020 Jul 13;43(7). doi: 10.1093/sleep/zsaa004.
Oral appliance therapy is an increasingly common option for treating obstructive sleep apnea (OSA) in patients who are intolerant to continuous positive airway pressure (CPAP). Clinically applicable tools to identify patients who could respond to oral appliance therapy are limited.
Data from three studies (N = 81) were compiled, which included two sleep study nights, on and off oral appliance treatment. Along with clinical variables, airflow features were computed that included the average drop in airflow during respiratory events (event depth) and flow shape features, which, from previous work, indicates the mechanism of pharyngeal collapse. A model was developed to predict oral appliance treatment response (>50% reduction in apnea-hypopnea index [AHI] from baseline plus a treatment AHI <10 events/h). Model performance was quantified using (1) accuracy and (2) the difference in oral appliance treatment efficacy (percent reduction in AHI) and treatment AHI between predicted responders and nonresponders.
In addition to age and body mass index (BMI), event depth and expiratory "pinching" (validated to reflect palatal prolapse) were the airflow features selected by the model. Nonresponders had deeper events, "pinched" expiratory flow shape (i.e. associated with palatal collapse), were older, and had a higher BMI. Prediction accuracy was 74% and treatment AHI was lower in predicted responders compared to nonresponders by a clinically meaningful margin (8.0 [5.1 to 11.6] vs. 20.0 [12.2 to 29.5] events/h, p < 0.001).
A model developed with airflow features calculated from routine polysomnography, combined with age and BMI, identified oral appliance treatment responders from nonresponders. This research represents an important application of phenotyping to identify alternative treatments for personalized OSA management.
口腔矫治器治疗是不耐受持续气道正压通气（CPAP）的阻塞性睡眠呼吸暂停（OSA）患者的一种越来越常见的选择。临床上可用于识别可能对口腔矫治器治疗有反应的患者的工具有限。
编译了三项研究的数据（N=81），其中包括两个睡眠研究夜，分别在使用和不使用口腔矫治器治疗时进行。除了临床变量外，还计算了气流特征，包括呼吸事件期间气流平均下降量（事件深度）和流量形状特征，从之前的工作中可以看出，这些特征表明了咽塌陷的机制。建立了一个模型来预测口腔矫治器治疗反应（与基线相比，呼吸暂停低通气指数[AHI]降低>50%，且治疗 AHI<10 次/小时）。使用（1）准确性和（2）预测应答者和非应答者之间的口腔矫治器治疗效果（AHI 降低百分比）和治疗 AHI 的差异来量化模型性能。
除了年龄和体重指数（BMI）外，事件深度和呼气“捏合”（经验证反映了硬腭前突）也是模型选择的气流特征。无反应者的事件深度更深，呼气时“捏合”流量形状（即与硬腭塌陷有关），年龄更大，BMI 更高。预测准确率为 74%，预测应答者的治疗 AHI 明显低于非应答者（8.0[5.1 至 11.6]与 20.0[12.2 至 29.5]次/小时，p<0.001）。
使用从常规多导睡眠图计算得出的气流特征与年龄和 BMI 相结合的模型，可以从无反应者中识别出口腔矫治器治疗的应答者。这项研究代表了表型分析在识别个性化 OSA 管理替代治疗方法方面的重要应用。
