KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
Biomed Eng Online. 2024 Jul 8;23(1):63. doi: 10.1186/s12938-024-01254-8.
Sleep apnea syndrome, characterized by recurrent cessation (apnea) or reduction (hypopnea) of breathing during sleep, is a major risk factor for postoperative respiratory depression. Challenges in sleep apnea assessment have led to the proposal of alternative metrics derived from oxyhemoglobin saturation (SpO), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO below 90% (CT90), as predictors of postoperative respiratory depression. However, their performance has been limited with area under the curve of 0.60 for ODI and 0.59 for CT90. Our objective was to propose novel features from preoperative overnight SpO which are correlated with sleep apnea severity and predictive of postoperative respiratory depression.
Preoperative SpO signals from 235 surgical patients were retrospectively analyzed to derive seven features to characterize the sleep apnea severity. The features included entropy and standard deviation of SpO signal; below average burden characterizing the area under the average SpO; average, standard deviation, and entropy of desaturation burdens; and overall nocturnal desaturation burden. The association between the extracted features and sleep apnea severity was assessed using Pearson correlation analysis. Logistic regression was employed to evaluate the predictive performance of the features in identifying postoperative respiratory depression.
Our findings indicated a similar performance of the proposed features to the conventional apnea-hypopnea index (AHI) for assessing sleep apnea severity, with average area under the curve ranging from 0.77 to 0.81. Notably, entropy and standard deviation of overnight SpO signal and below average burden showed comparable predictive capability to AHI but with minimal computational requirements and individuals' burden, making them promising for screening purposes. Our sex-based analysis revealed that compared to entropy and standard deviation, below average burden exhibited higher sensitivity in detecting respiratory depression in women than men.
This study underscores the potential of preoperative SpO features as alternative metrics to AHI in predicting postoperative respiratory.
睡眠呼吸暂停综合征（Sleep Apnea Syndrome，SAS）的特征是睡眠期间呼吸反复停止（呼吸暂停）或减少（呼吸不足），是术后呼吸抑制的主要危险因素。睡眠呼吸暂停评估的挑战导致了从氧合血红蛋白饱和度（SpO2）衍生的替代指标的提出，例如氧减指数（ODI）和 SpO2 低于 90%的累积睡眠时间百分比（CT90），作为术后呼吸抑制的预测指标。然而，它们的性能受到限制，ODI 的曲线下面积为 0.60，CT90 的曲线下面积为 0.59。我们的目标是从术前夜间 SpO2 中提出与睡眠呼吸暂停严重程度相关并可预测术后呼吸抑制的新特征。
回顾性分析了 235 例手术患者的术前 SpO2 信号，以得出七个特征来描述睡眠呼吸暂停的严重程度。这些特征包括 SpO2 信号的熵和标准差；低于平均负荷，用于描述平均 SpO2 下的面积；平均、标准差和脱氧负荷的熵；以及整个夜间脱氧负荷。使用 Pearson 相关分析评估提取特征与睡眠呼吸暂停严重程度之间的关联。使用逻辑回归评估特征在识别术后呼吸抑制方面的预测性能。
我们的研究结果表明，所提出的特征在评估睡眠呼吸暂停严重程度方面与传统的呼吸暂停-低通气指数（AHI）具有相似的性能，平均曲线下面积范围为 0.77 至 0.81。值得注意的是，夜间 SpO2 信号的熵和标准差以及低于平均负荷与 AHI 具有相当的预测能力，但计算要求和个体负担最小，因此具有筛查的潜力。我们的性别分析表明，与熵和标准差相比，低于平均负荷在检测女性呼吸抑制方面比男性具有更高的敏感性。
本研究强调了术前 SpO2 特征作为替代 AHI 预测术后呼吸的潜在指标。
