Pang Feng, Deng Wenmin, Huang Jingyan, Guo Yu, Lin Minmin, Zhang Xiangmin, Liu Jie
Department of Sleep Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Department of Otolaryngology-Head and Neck Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Clin Respir J. 2024 Dec;18(12):e70047. doi: 10.1111/crj.70047.
Positive airway pressure (PAP) is the primary treatment for obstructive sleep apnea (OSA). This study aims to predict the optimal PAP pressure in Chinese OSA patients by their polysomnography (PSG) variables and demographic characteristics.
Patients with an apnea-hypopnea index (AHI) ≥ 15 times/h who received PAP therapy (residual AHI < 5 times/h) and underwent PSG were included in this study. Sex, age, body mass index (BMI), Epworth Sleepiness Scale (ESS), AHI, supine AHI, lowest oxygen saturation (LSaO), percentage of total sleep time spent with SaO < 90% (CT90), and PAP pressure were recorded. PAP pressure and other variables were analyzed using univariate correlation and multivariate linear stepwise regression analysis.
A total of 167 patients were enrolled, with 122 in the study group and 45 in the validation group. Univariate correlation analysis revealed a significant correlation between PAP pressure and age, BMI, ESS, AHI, supine AHI, LSaO, and CT90. The multivariate linear regression analysis showed that PAP pressure was correlated with gender (b = 1.142, p = 0.032), age (b = -0.039, p = 0.005), AHI (b = 0.047, p = 0.000), and CT90 (b = 0.037, p = 0.000). The final PAP pressure prediction equation was PAPpre (cmHO) = 8.548 + 1.142 × sex -0.039 × age + 0.047 × AHI + 0.037 × CT90 (R = 0.553) (male is defined as 0 and female as 1). This model accounts for 55.3% of the optimal pressure variance, and the area under the ROC curve of PAP prediction pressure is 0.7419.
PSG variables can be used to predict PAP pressure in Chinese OSA patients, but for some individuals, the prediction model is not very good. PAP is correlated with age, BMI, ESS, AHI, supine AHI, LSaO, and percentage of total sleep time spent with SaO < 90% (CT90), which can be used to predict the optimal PAP pressure.
气道正压通气（PAP）是阻塞性睡眠呼吸暂停（OSA）的主要治疗方法。本研究旨在通过多导睡眠图（PSG）变量和人口统计学特征预测中国OSA患者的最佳PAP压力。
本研究纳入了接受PAP治疗（残余呼吸暂停低通气指数[AHI]<5次/小时）且进行了PSG检查、AHI≥15次/小时的患者。记录性别、年龄、体重指数（BMI）、爱泼沃斯嗜睡量表（ESS）、AHI、仰卧位AHI、最低血氧饱和度（LSaO）、血氧饱和度<90%的总睡眠时间百分比（CT90）和PAP压力。使用单变量相关性分析和多变量线性逐步回归分析对PAP压力和其他变量进行分析。
共纳入167例患者，其中研究组122例，验证组45例。单变量相关性分析显示PAP压力与年龄、BMI、ESS、AHI、仰卧位AHI、LSaO和CT90之间存在显著相关性。多变量线性回归分析表明，PAP压力与性别（b=1.142，p=0.032）、年龄（b=-0.039，p=0.005）、AHI（b=0.047，p=0.000）和CT90（b=0.037，p=0.000）相关。最终的PAP压力预测方程为PAPpre（cmH₂O）=8.548+1.142×性别-0.039×年龄+0.047×AHI+0.037×CT90（R=0.553）（男性定义为0，女性定义为1）。该模型解释了最佳压力方差的55.3%，PAP预测压力的ROC曲线下面积为0.7419。
PSG变量可用于预测中国OSA患者的PAP压力，但对于某些个体，预测模型效果不太理想。PAP与年龄、BMI、ESS、AHI、仰卧位AHI、LSaO以及血氧饱和度<90%的总睡眠时间百分比（CT90）相关，可用于预测最佳PAP压力。
