Abstract
PURPOSE: 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. METHODS: 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(2)), percentage of total sleep time spent with SaO(2) < 90% (CT90), and PAP pressure were recorded. PAP pressure and other variables were analyzed using univariate correlation and multivariate linear stepwise regression analysis. RESULTS: 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(2), 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 (cmH(2)O) = 8.548 + 1.142 × sex -0.039 × age + 0.047 × AHI + 0.037 × CT90 (R(2) = 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. CONCLUSION: 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(2), and percentage of total sleep time spent with SaO(2) < 90% (CT90), which can be used to predict the optimal PAP pressure.