Georges Marjolaine, Adler Dan, Contal Olivier, Espa Fabrice, Perrig Stephen, Pépin Jean-Louis, Janssens Jean-Paul
Division of Pulmonary Diseases, Department of Medical Specialties.
Sleep Laboratory, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
Respir Care. 2015 Jul;60(7):1051-6. doi: 10.4187/respcare.03633. Epub 2015 Mar 3.
Ventilators designed for home care provide clinicians with built-in software that records items such as compliance, leaks, average tidal volume, total ventilation, and indices of residual apnea and hypopnea. Recent studies have showed, however, an important variability between devices regarding reliability of data provided. In this study, we aimed to compare apnea-hypopnea indices (AHI) provided by home ventilators (AHINIV) versus data scored manually during polysomnography (AHIPSG) in subjects on noninvasive ventilation (NIV) for obesity-hypoventilation syndrome.
Stable subjects with obesity-hypoventilation syndrome on NIV, all using the same device, underwent 3 consecutive polysomnographic sleep studies with different backup breathing frequencies (spontaneous mode, low and high backup breathing frequencies). During each recording, AHINIV was compared with AHIPSG.
Ten subjects (30 polysomnogram tracings) were analyzed. For each backup breathing frequency (spontaneous mode, low and high backup breathing frequencies), AHI values were 62 ± 7/h, 26 ± 7/h, and 17 ± 5/h (mean ± SD), respectively. Correlation between AHINIV and AHIPSG was highly significant (r(2) = 0.89, P < .001). As determined by Bland-Altman analysis, mean bias was 6.5 events/h, and limits of agreement were +26.0 and -12.9 events/h. Bias increased significantly with higher AHI values. Using a threshold AHI value of 10/h to define appropriate control of respiratory events, the ventilator software had a sensitivity of 90.9%, a specificity and positive predictive value of 100%, and a negative predictive value of 71%.
In stable subjects with obesity-hypoventilation syndrome, the home ventilator software tested was appropriate for determining if control of respiratory events was satisfactory on NIV or if further testing or adjustment of ventilator settings was required. (ClinicalTrials.gov registration NCT01130090.).
为家庭护理设计的呼吸机为临床医生提供了内置软件，可记录诸如顺应性、漏气量、平均潮气量、总通气量以及残余呼吸暂停和呼吸不足指数等项目。然而，最近的研究表明，不同设备所提供数据的可靠性存在显著差异。在本研究中，我们旨在比较肥胖低通气综合征无创通气（NIV）患者中，家用呼吸机提供的呼吸暂停低通气指数（AHI）（AHINIV）与多导睡眠图期间手动评分的数据（AHIPSG）。
稳定的肥胖低通气综合征NIV患者，均使用同一设备，进行了3次连续的多导睡眠图睡眠研究，采用不同的备用呼吸频率（自主模式、低和高备用呼吸频率）。在每次记录期间，将AHINIV与AHIPSG进行比较。
分析了10名受试者（30份多导睡眠图记录）。对于每个备用呼吸频率（自主模式、低和高备用呼吸频率），AHI值分别为62±7次/小时、26±7次/小时和17±5次/小时（平均值±标准差）。AHINIV与AHIPSG之间的相关性非常显著（r(2)=0.89，P<.001）。根据Bland-Altman分析确定，平均偏差为6.5次/小时，一致性界限为+26.0和-12.9次/小时。随着AHI值升高，偏差显著增加。使用10次/小时的AHI阈值来定义对呼吸事件的适当控制，呼吸机软件的敏感性为90.9%，特异性和阳性预测值为100%，阴性预测值为71%。
在稳定的肥胖低通气综合征患者中，所测试的家用呼吸机软件适用于确定NIV时呼吸事件的控制是否令人满意，或者是否需要进一步测试或调整呼吸机设置。（ClinicalTrials.gov注册号NCT01130090.）
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