Martinot Jean-Benoît, Le-Dong Nhat-Nam, Tamisier Renaud, Bailly Sébastien, Pépin Jean-Louis
Sleep Laboratory, CHU Université Catholique de Louvain (UCL), Namur Site Sainte-Elisabeth, Namur, Belgium; Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium.
Sunrise, Namur, Belgium.
Sleep Med. 2023 Nov;111:86-93. doi: 10.1016/j.sleep.2023.09.002. Epub 2023 Sep 12.
A single-night attended in-laboratory polysomnography or home sleep testing are common approaches for obstructive sleep apnea (OSA) diagnosis. However, internight variability in apnea-hypopnea index value is common, and may result in misclassification of OSA severity and inapropriate treatment decisions.
To investigate factors determining short-term apnea-hypopnea index variability using multi-night automated home sleep testing, and to determine how this variability impacts clinical decisions.
PATIENTS/METHODS: Adults with suspected OSA who successfully performed three home sleep tests using measurements of mandibular jaw movements (Sunrise, Namur, Belgium) combined with automated machine learning analysis were enrolled. Data analysis included principal component analysis, generalized estimating equation regression and qualitative agreement analysis.
160 individuals who performed three sleep tests over a mean of 8.78 ± 8.48 days were included. The apnea-hypopnea index varied by -0.88 events/h (5th-95th percentile range: -14.33 to 9.72 events/h). Based on a single-night recording, rates of overtreatment and undertreatment would have been of 13.5% and 6.0%, respectively. Regression analysis adjusted for age, sex, body mass index, total sleep time, and time between home sleep tests showed that time spent in deep non-rapid eye movement sleep and with head in supine position were independent significant predictors of the apnea-hypopnea index variability.
At the individual level, short-term internight variability in the apnea-hypopnea index was significantly associated with time spent in deep non-rapid eye movement sleep and head in supine position. Clinical decisions based on a single-night testing may lead to errors in OSA severity classification and incorrect therapeutic decisions.
单次夜间在实验室进行的多导睡眠监测或家庭睡眠测试是阻塞性睡眠呼吸暂停（OSA）诊断的常用方法。然而，呼吸暂停低通气指数值的夜间变异性很常见，可能导致OSA严重程度的错误分类和不恰当的治疗决策。
使用多晚自动家庭睡眠测试来研究决定短期呼吸暂停低通气指数变异性的因素，并确定这种变异性如何影响临床决策。
患者/方法：纳入怀疑患有OSA且使用下颌运动测量（比利时那慕尔的Sunrise）结合自动机器学习分析成功进行了三次家庭睡眠测试的成年人。数据分析包括主成分分析、广义估计方程回归和定性一致性分析。
纳入了160名在平均8.78±8.48天内进行了三次睡眠测试的个体。呼吸暂停低通气指数变化为-0.88次/小时（第5至95百分位数范围：-14.33至9.72次/小时）。基于单次夜间记录，过度治疗和治疗不足的发生率分别为13.5%和6.0%。对年龄、性别、体重指数、总睡眠时间和家庭睡眠测试之间的时间进行调整后的回归分析表明，深度非快速眼动睡眠中的时间和头部处于仰卧位是呼吸暂停低通气指数变异性的独立显著预测因素。
在个体层面，呼吸暂停低通气指数的短期夜间变异性与深度非快速眼动睡眠中的时间和头部处于仰卧位显著相关。基于单次夜间测试的临床决策可能导致OSA严重程度分类错误和治疗决策不正确。
