Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Eur Respir J. 2017 Sep 20;50(3). doi: 10.1183/13993003.00345-2017. Print 2017 Sep.
Obstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep.23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals.Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated.This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions.
阻塞性睡眠呼吸暂停(OSA)的特征是咽腔在不同部位发生阻塞。内窥镜研究表明,会厌塌陷使患者在口腔矫治器治疗失败或持续气道正压通气时塌陷加剧的风险更高。目前,诊断会厌塌陷需要进行侵入性研究(影像学和内窥镜检查)。作为替代方法,我们提出可以通过会厌塌陷在睡眠期间产生的独特气流模式来检测到它。23 名 OSA 患者接受了自然睡眠内窥镜检查。对 1232 次呼吸进行了评分,分为会厌/无会厌塌陷。从气流信号(与内窥镜检查同时记录)中确定了几个流量特征,并将其用于构建一个预测模型,以区分会厌塌陷和无会厌塌陷。此外,还对 10 名 OSA 患者进行了研究,使用鼻压信号验证了呼吸量计的流量特征。会厌塌陷的特征是吸气流量急剧下降(s),吸气和呼气流量更具可变性,潮气量减少。交叉验证的准确率为 84%。从呼吸量计流量和鼻压获得的预测特征具有很强的相关性。这项研究表明,会厌塌陷可以从睡眠研究中测量的气流信号中识别出来。这种方法可以使临床医生利用临床收集的数据来描述潜在的生理状况,并改善治疗决策。