高血压是我国重点防治的心血管疾病, 血压的控制率备受关注。在一些血压控制不良的患者中睡眠呼吸暂停是导致顽固性高血压的重要原因。以睡眠过程中反复、频繁出现呼吸暂停和低通气为特点的睡眠呼吸暂停低通气综合征( sleep apneahypopnea syndrome, SAHS) 自20 世纪80 年代以来也受到广泛关注, 临床和基础研究取得了迅速发展。目前, 多项临床、流行病学和基础研究证实SAHS可以导致和/ 或加重高血压, 与高血压的发生发展密切相关。
Objective To investigate the correlation between serum level of visfatin and obesity in patients with obstructive sleep apnea hypopnea syndrome ( OSAHS) . Methods Forty-seven patients with OSAHS and 20 healthy controls were recruited in this study. Polysomnography was performed in all subjects to detect apnea-hypopnea index ( AHI) . The serumlevels of cisfatin, C-reactive protein ( CRP) , TNF-α, and IL-6 were measured by enzyme linked immunosorbent assay. The body mass inex ( BMI) was calculated.The level of cisfatin was compared between the OSAHS patients with different severity and the controls, and its relationship with the levels of AHI, BMI, CRP, TNF-α, and IL-6 was analyzed. Results The serumlevel of visfatin in the OSAHS patients was higher significantly than that in the controls ( P lt;0. 01) and increased by the severity of OSAHS. There were positive correlations between the serum level of visfatin and AHI,BMI, CRP, TNF-α, IL-6 in the OSAHS patients ( P lt;0. 05) . Conclusion The expression of visfatin may play an important role in the pathogenesis of OSAHS.
目的 总结多导睡眠监测的监测方法及护理要点。 方法 2010年3月－2011年3月采用美国伟康多导睡眠呼吸监测仪对睡眠中心78例患者进行不少于7 h的整夜连续监测和护理。 结果 76例患者顺利完成监测，确诊阻塞性睡眠呼吸暂停低通气综合征73例（重度17例，中度31例，轻度25例），单纯鼾症3例。1例因环境陌生、导联多无法入睡而监测失败，另1例因鼻气流导管脱落而监测失败。 结论 对症有效的护理方法是多导睡眠监测得以顺利完成的根本保证。
Objective To investigate the changes and clinical relationship of plasma adrenomedullin( ADM) , atrial natriuretic polypeptide( ANP) , and heart rate variability( HRV) in patients with obstructive sleep apnea-hypopnea syndrome ( OSAHS) . Methods Seventy-five inpatients with OSAHS were enrolled in this study. According to the apnea hypopnea index ( AHI) by polysomnography, the subjects were divided into a mild group, a moderate group, and a severe group. Meanwhile, HRV was screened bydynamic electrocardiogram in sleep laboratory. HRV parameters were obtained including LF ( low frequency power) , HF( high frequency power) , pNN50( percentage of NN50 in the total number of N-N intervals) ,SDNN( standard deviation of the N-N intervals) , rMSSD( square root of the mean squared differences of successive N-N intervals ) . Plasma levels of ADM/ANP were measured by radioimmunoassay. Results The levels of SDNN ( P lt;0. 05) , rMSSD, pNN50, LF ( P lt; 0. 05) and HF were gradually reduced, and the levels of ADM ( P lt;0. 05) and ANP ( P lt; 0. 05) were increased with increasing severity of OSAHS. Linear correlation analysis demonstrated that SDNN was negatively correlated with ADM( r = - 0. 423, P lt;0. 05)and ANP( r = - 0. 452, P lt; 0. 05) , and LF was also negatively correlated with ADM( r = - 0. 348, P lt;0. 05) . Conclusion Lower HRV is associated with more sever OSAHS, and it may be modulated neurohumorally by ADM and ANP.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
In recent years, more and more studies have shown that obstructive sleep apnea hypopnea syndrome (OSAHS) and metabolic syndrome are closely related and interact with each other, while hypertension, abnormal glucose metabolism, lipid metabolism disorders and obesity, as the main components of metabolic syndrome, have been further studied. Continuous positive airway pressure is currently the main treatment for OSAHS. This review focuses on the association between OSAHS and hypertension, glucose metabolism abnormalities, lipid metabolism disorders, obesity and the effects of treatment with positive airway pressure, aiming to provide a theoretical basis for the pathogenesis and treatment of OSAHS complicated with metabolic syndrome.
Objective To investigate the levels of IL-6 and TNF-α in children with obstructive sleep apnea syndrome (OSAS) and to determine their clinical significance. Methods One hundred children with OSAS in our department from August 2005 to February 2006, and 40 healthy children were enrolled in the study. The serum levels of IL-6 and TNF-α were measured. Results Serum levels of IL-6 and TNF-α were significantly higher in patients with OSAS than those in the control group (Plt;0.05). Both IL-6 and TNF-α were not correlated with AHI. Conclusion It is concluded that OSAS is a chronic inflammatory process. A close correlation was observed between high levels of IL-6 and TNF-α and OSAS. High levels of IL-6 and TNF-α account for the risk factors in the development of cardiovascular diseases in children with OSAS.
In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.