PDF(Pubmed)
Abstract：
BACKGROUND: 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 (SpO2), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO2 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 SpO2 which are correlated with sleep apnea severity and predictive of postoperative respiratory depression.
METHODS: Preoperative SpO2 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 SpO2 signal; below average burden characterizing the area under the average SpO2; 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.
RESULTS: 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 SpO2 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.
CONCLUSIONS: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.
METHODS: Preoperative SpO2 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 SpO2 signal; below average burden characterizing the area under the average SpO2; 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.
RESULTS: 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 SpO2 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.
CONCLUSIONS: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.
摘要：
背景：睡眠呼吸暂停综合征，以睡眠期间呼吸的反复停止（呼吸暂停）或减少（呼吸不足）为特征，是术后呼吸抑制的主要危险因素。睡眠呼吸暂停评估中的挑战导致了从氧合血红蛋白饱和度（SpO2）得出的替代指标的提议。例如氧饱和度下降指数（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因子。然而,它们的性能受到限制，ODI曲线下面积为0.60，CT90曲线下面积为0.59。我们的目标是提出术前过夜SpO2的新特征，这些特征与睡眠呼吸暂停严重程度相关，并可预测术后呼吸抑制。
方法：对235例手术患者的术前SpO2信号进行回顾性分析，得出7个特征来表征睡眠呼吸暂停严重程度。特征包括SpO2信号的熵和标准偏差；低于平均负荷，表征平均SpO2下的面积；平均，标准偏差，和去饱和负担的熵；以及总体夜间去饱和负担。使用Pearson相关性分析评估提取的特征与睡眠呼吸暂停严重程度之间的关联。采用Logistic回归评估特征在识别术后呼吸抑制方面的预测性能。
结果：我们的研究结果表明，在评估睡眠呼吸暂停严重程度方面，所提出的特征与常规呼吸暂停低通气指数（AHI）相似。曲线下平均面积为0.77至0.81。值得注意的是,隔夜SpO2信号的熵和标准偏差以及低于平均负荷显示出与AHI相当的预测能力，但计算要求和个人负担最小，使它们有希望用于筛查目的。我们基于性别的分析表明，与熵和标准差相比，低于平均水平的负担在检测呼吸抑制方面，女性比男性表现出更高的灵敏度。
结论：本研究强调了术前SpO2特征作为AHI预测术后呼吸的替代指标的潜力。
方法：对235例手术患者的术前SpO2信号进行回顾性分析，得出7个特征来表征睡眠呼吸暂停严重程度。特征包括SpO2信号的熵和标准偏差；低于平均负荷，表征平均SpO2下的面积；平均，标准偏差，和去饱和负担的熵；以及总体夜间去饱和负担。使用Pearson相关性分析评估提取的特征与睡眠呼吸暂停严重程度之间的关联。采用Logistic回归评估特征在识别术后呼吸抑制方面的预测性能。
结果：我们的研究结果表明，在评估睡眠呼吸暂停严重程度方面，所提出的特征与常规呼吸暂停低通气指数（AHI）相似。曲线下平均面积为0.77至0.81。值得注意的是,隔夜SpO2信号的熵和标准偏差以及低于平均负荷显示出与AHI相当的预测能力，但计算要求和个人负担最小，使它们有希望用于筛查目的。我们基于性别的分析表明，与熵和标准差相比，低于平均水平的负担在检测呼吸抑制方面，女性比男性表现出更高的灵敏度。
结论：本研究强调了术前SpO2特征作为AHI预测术后呼吸的替代指标的潜力。
