@article {Mann1802262，作者= {Mann, Dwayne L.和Terrill, Philip I.和Azarbarzin, Ali和Mariani, Sara和Franciosini, Angelo和Camassa, Alessandra和Georgeson, Thomas和Marques, Melania和Taranto-Montemurro, Luigi和Messineo, Ludovico和Redline, Susan和Wellman, Andrew和Sands, Scott A.}，题目={量化睡眠期间咽喉阻塞的大小使用气流形状}，volume = {54}， number = {1}， location-id ={1802262}，年份= {2019}，doi ={10.1183/13993003.02262-2018}，出版商={欧洲呼吸学会}，188bet官网地址对咽部气流梗阻的严重程度进行无创量化，可以识别阻塞性与中央性睡眠呼吸暂停的表现，并识别有症状的个体，尽管有低呼吸暂停低呼吸指数(AHI)，但仍有严重的气流梗阻。在这里，我们提供了一种新方法，利用个体呼吸的简单气流与时间({\textquotedblleft}形状{\textquotedblright})特征在夜间睡眠研究中自动和非侵入性地量化气流阻塞的严重程度，而不需要食管导管。方法41例疑似/诊断阻塞性睡眠呼吸暂停患者(AHI范围0{\textendash}91事件{\ textperiod中心}-1)接受夜间多导睡眠图检查，采用金标准气流测量(口鼻肺炎:{\textquotedblleft}流{\textquotedblright})和通气驱动(校准食管内膈肌电图:{\textquotedblleft}驱动{\textquotedblright})。阻塞严重程度被定义为一个连续变量(流量:传动比)。多变量回归利用气流形状特征(吸气/呼气时间、平直度、挖空、扑动)来估计136 264次呼吸中的气流:驱动比(基于离开一个病人交叉验证的性能)。在一个子集(n=17)中同时使用鼻压记录进行重复分析。结果金标准梗阻严重程度(流量:驱动比)在不同个体间差异很大，与AHI无关。一个多变量模型(25个特征)估计呼吸阻塞严重程度(R2=0.58 vs金标准，p\<0.00001; mean absolute error 22\%) and the median obstruction severity across individual patients (R2=0.69, p\<0.00001; error 10\%). Similar performance was achieved using nasal pressure.Conclusions The severity of pharyngeal obstruction can be quantified non-invasively using readily available airflow shape information. Our work overcomes a major hurdle necessary for the recognition and phenotyping of patients with obstructive sleep disordered breathing.The degree of pharyngeal airflow obstruction varies widely for any given OSA severity (apnoea{\textendash}hypopnoea index) and is challenging to measure. Here we combine information from automated flow shape to accurately estimate the severity of airflow obstruction. http://bit.ly/2uYD0rf}, issn = {0903-1936}, URL = {//www.qdcxjkg.com/content/54/1/1802262}, eprint = {//www.qdcxjkg.com/content/54/1/1802262.full.pdf}, journal = {European Respiratory Journal} }