Upper airway effective compliance during wakefulness and sleep in obese adolescents studied via two-dimensional dynamic MRI and semiautomated image segmentation.
Upper airway effective compliance during wakefulness and sleep in obese adolescents studied via two-dimensional dynamic MRI and semiautomated image segmentation.
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通过二维动态 MRI 和半自动图像分割研究肥胖青少年清醒和睡眠期间上呼吸道的有效顺应性。
DOI：
10.1152/japplphysiol.00839.2020
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wootton,DavidM
中科院分区：
文献类型：
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作者：
Choy,KokRen;Sin,Sanghun;Tong,Yubing;Udupa,JayaramK;Luchtenburg,DirkM;Wagshul,MarkE;Arens,Raanan;Wootton,DavidM
上呼吸道生物力学的新生物标志物可能提高阻塞性睡眠呼吸暂停综合征（OSAS）的诊断。上气道有效顺应性（EC），即使用计算流体动力学（CFD）估计的横截面积与压力的斜率，与呼吸暂停低通气指数（AHI）和临界关闭压（Pcrit）相关。本研究的目的是开发一种快速，简化的方法，估计EC使用动态MRI和生理测量，并探讨假设OSAS的严重程度与机械顺应性在清醒和睡眠。5名肥胖儿童OSAS和5名对照组肥胖儿童，年龄12-17岁，进行了前动脉测压，多导睡眠图，动态MRI与同步气流测量在觉醒和睡眠。采用一种新的半自动方法分割腭后和舌后切片图像中的气道横截面，该方法使用优化的奇异值分解（SVD）图像滤波和k均值聚类结合形态学操作。使用测压Rohrer系数和流速估计压力，并在五次正常呼吸期间从面积压力斜率计算EC。采用斯皮尔曼等级相关法计算呼吸暂停低通气指数（AHI）、EC和横截面积（CSA）变化之间的相关性。与专家手动分割相比，半自动方法有效地分割了气道，平均Dice系数超过89%。AHI与睡眠时舌后区EC呈正相关（rs= 0.74，P = 0.014），与舌后区EC的变化呈正相关（rs= 0.77，P = 0.01）。CSA变化单独与AHI无显著相关性。EC是一种机械生物标志物，包括CSA变化和压力变化，是一种潜在的诊断生物标志物，用于研究和管理OSAS.NEW & NOTEWORTHThis研究调查了清醒和睡眠期间腭后和舌后部位上气道的动态，通过评估每个部位的有效顺应性（EC）及其与呼吸暂停低通气指数（AHI）的相关性，使用新型半自动图像处理。AHI与睡眠时舌后区EC及清醒至睡眠时舌后区EC的变化显著相关。结果表明，EC作为一个有前途的非侵入性诊断标志物，用于估计OSAS患者上气道各区域的机械性能。
Novel biomarkers of upper airway biomechanics may improve diagnosis of obstructive sleep apnea syndrome (OSAS). Upper airway effective compliance (EC), the slope of cross-sectional area versus pressure estimated using computational fluid dynamics (CFD), correlates with apnea-hypopnea index (AHI) and critical closing pressure (Pcrit). The study objectives are to develop a fast, simplified method for estimating EC using dynamic MRI and physiological measurements and to explore the hypothesis that OSAS severity correlates with mechanical compliance during wakefulness and sleep. Five obese children with OSAS and five control subjects with obesity aged 12–17 yr underwent anterior rhinomanometry, polysomnography, and dynamic MRI with synchronized airflow measurement during wakefulness and sleep. Airway cross section in retropalatal and retroglossal section images was segmented using a novel semiautomated method that uses optimized singular value decomposition (SVD) image filtering andk-means clustering combined with morphological operations. Pressure was estimated using rhinomanometry Rohrer’s coefficients and flow rate, and EC was calculated from the area-pressure slope during five normal breaths. Correlations between apnea-hypopnea index (AHI), EC, and cross-sectional area (CSA) change were calculated using Spearman’s rank correlation. The semiautomated method efficiently segmented the airway with average Dice Coefficient above 89% compared with expert manual segmentation. AHI correlated positively with EC at the retroglossal site during sleep (rs= 0.74,P= 0.014) and with change of EC from wake to sleep at the retroglossal site (rs= 0.77,P= 0.01). CSA change alone did not correlate significantly with AHI. EC, a mechanical biomarker which includes both CSA change and pressure variation, is a potential diagnostic biomarker for studying and managing OSAS.NEW & NOTEWORTHYThis study investigated the dynamics of the upper airway at retropalatal and retroglossal sites during wakefulness and sleep by evaluating the effective compliance (EC) of each site and its correlation with apnea-hypopnea index (AHI) using novel semiautomated image processing. AHI correlated significantly with retroglossal EC during sleep and change of retroglossal EC from wake to sleep. The results suggest EC as a promising noninvasive diagnostic marker for estimating the mechanical properties of various upper airway regions in patients with OSAS.