Clinical features and related risk factors of sleep hypopnea in obstructive sleep apnea hypoventilation syndrome / 天津医药

ABSTRACT

Objective To analyze the clinical features and related risk factors of sleep hypopnea (SH) in obstructive sleep apnea hypoventilation syndrome (OSAHS). Methods A total of 63 patients with OSAHS who were underwent polysomnography (PSG) and transcutaneous carbon dioxide partial pressure (TCPCO2) monitoring were selected in this study. All patients were divided into pure OSAHS group (n=35) and OSAHS with SH group (n=28) according to the diagnostic criteria of SH. The clinical features of nocturnal carbon dioxide and related risk factors were compared between two groups, including gender, age, complications, body mass index (BMI), Epworth sleepiness scale (ESS), micro awakening index, arterial blood gas analysis, PSG and TCPCO2. Correlation analysis were used to analyze the correlation between the highest TCPCO2 and other variables. The influencing factors of the highest TCPCO2 were analyzed by multiple linear regression analysis. Receiver operating characteristic (ROC) curve analysis was used to analyze the value for related variables in the diagnosis of SH. Results Twenty-eight patients were diagnosed as SH in all the 63 patients with OSAHS, the proportion was 44.4%. There were no significant differences in gender, age and smoking proportion between the two groups. Data of BMI, arterial carbon dioxide partial pressure [p(CO2)], prevalence of hypertension, ESS, apnea hypopnea index, micro arousal index, percentage of nighttime sleep with blood oxygen saturation less than 90%, highest TCPCO2 and TCPCO2 during each sleep stage were significantly higher in the OSAHS with SH group than those in the pure OSAHS group (P<0.05), while arterial oxygen partial pressure [p(O2)] and the lowest pulse oxygen saturation (SpO2) were significantly lower than those in pure OSAHS group (P<0.05). The highest TCPCO2 was positively correlated with p(CO2), ESS and BMI (P<0.01). Multiple linear regression analysis showed that the highest TCPCO2 was affected by BMI and ESS. As a possible predictor for OSAHS with SH, BMI>31.43 kg/m2 showed a sensitivity of 64.3%and specificity of 91.4%, and ESS score>12 showed a sensitivity of 78.6%and specificity of 71.4%. Conclusion The patients of OSAHS with SH have more severe nocturnal hypercapnia and hypoxemia. OSHAS patients are recommend to undergo TcPCO2 monitoring, when BMI is greater than 31.43 kg/m2 and ESS is greater than 12 scores.