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。
Neuromuscular disease (NMD) encompasses a group of disorders that affect motor neurons, peripheral nerves, neuromuscular junctions, and skeletal muscles, potentially leading to respiratory muscle impairment and decline in respiratory function, significantly impacting patients' quality of life. In March 2023, clinical practice guideline titled Respiratory Management of Patients with Neuromuscular Weakness was released by the American College of Chest Physicians. This article summarizes, categorizes, and interprets the contents and key points of the guideline, aiming to provide more targeted guidance for clinical healthcare professionals and ultimately enhance the effectiveness of respiratory management for patients with NMD.
Sleep disorder is related to many comorbidities, such as diabetes, obesity, cardiovascular diseases, and hypertension. Because of its increasing prevalence rate, it has become a global problem that seriously threatens people’s health. Various forms of sleep disorder can cause increased insulin resistance and/or decreased sensitivity, thus affecting the occurrence, development and prognosis of diabetes. However, sleep health has not been paid attention to in recent years. Therefore, this article summarizes the findings of the correlation between sleep disorder and diabetes mellitus in recent years, by elaborating the relationship between various types of sleep disorder (including sleep apnea syndrome) and diabetes mellitus, as well as their mechanisms and intervention measures, in order to enhance the attention of clinical workers to sleep health, and to provide basis for reducing the risk of diabetes.
Sleep deprivation can cause hyperalgesia, and the mechanisms involve glutamic acid, dopamine, serotonin, metabotropic glutamate receptor subtype 5, adenosine A2A receptor, nicotinic acetylcholine receptor, opioid receptor, brain-derived neurotrophic factor, melatonin, etc. The mechanisms of hyperalgesia caused by sleep deprivation are complex. The current treatment methods are mainly to improve sleep and relieve pain. This paper reviews the mechanism and treatment progress of hyperalgesia induced by sleep deprivation, and aims to provide scientific evidence for the treatment of hyperalgesia caused by sleep deprivation.
Objective To prospectively verify the accuracy and reliability of the diagnostic model of obstructive sleep apnea (OSA), including the probability model and disease severity model, and to explore a simple and cost-effective method for screening of OSA. Methods A total of 996 patients who underwent polysomnography in Zigong Fourth People’s Hospital(590 cases) and West China Hospital of Sichuan University(406 cases) were consecutively and prospectively included as the research subjects. Firstly, the OSA diagnostic model was used for the diagnostic test; then polysomnography was performed; Finally, taking polysomnography as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio and area under the ROC curve of OSA diagnostic model were calculated, and the reliability analysis of the model’s results was carried out. Results The sensitivity, specificity and accuracy of the OSA diagnostic model were 76.38%(595/779), 83.41%(181/217) and 77.91%(776/996) respectively, the positive predictive value is 94.29%, negative predictive value is 45.49%, positive likelihood ratio is 4.604, negative likelihood ratio is 0.283; and the area under the ROC curve was 0.866. The reliability analysis of OSA diagnostic model showed that there was no significant difference in the bias comparison of AHI; the intra-class correlation coefficient(ICC) between AHI in the OSA diagnostic model and AHI in polysomnography was 0.659, with a relatively strong consistency degree; the intra-class correlation coefficient between the lowest SpO2 in the OSA diagnostic model and the lowest SpO2 in polysomnography was 0.563, with a moderate consistency degree. Conclusions The OSA diagnostic model can better predict the probability of illness and assess the severity of the disease, which is helpful for the early detection, diagnosis and treatment of OSA. The OSA diagnostic model is suitable for popularization and application in primary hospitals and when polysomnography is not available in time.
ObjectiveTo systematically review the correlation between sleep quality and social support of the elderly.MethodsDatabases including PubMed, MEDLINE, The Cochrane Library, Springerlink, ProQuest, CMB, CNKI, VIP, and WanFang Data were searched to collect studies on the correlation between sleep quality and social support of the elderly from January 1996 to January 2020. Two reviewers independently screened literature, extracted data and evaluated risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 software.ResultsA total of 9 studies involving 2 427 elderly people were included. The meta-analysis showed that the combined correlation coefficient between sleep quality and social support was -0.40 (95%CI −0.54 to −0.26). The correlation between sleep quality and social support of the elderly varied with the year of publication and sample size, however without regular change. The correlation coefficient of the elderly from institutions (hospital or pension institutions) was higher than that of the community (−0.33 vs. −0.26); the correlation coefficient of the elderly with health problems was higher than those without health problems (−0.32 vs. −0.25); the results measured by non-random sampling method were higher than those measured by random sampling (−0.37 vs. −0.23); and the results measured by Pittsburgh sleep quality index (PSQI) and social support rating scale (SSRS) were higher than those measured by PSQI and perceived social support scale (PSSS) (−0.30 vs. −0.13).ConclusionsThe higher the level of social support of the elderly in China, the lower the score of PSQI, and the better the sleep quality, in which there are differences in different sample sources and physical conditions.
ObjectiveTo analyse the hundred top-cited articles in obstructive sleep apnea hypopnea syndrome (OSAHS), and summarize the development trend of OSAHS research.MethodsWe searched the Web of Science core collection for all published articles on OSAHS or sleep disorders from January 1st, 1992 to May 23th, 2018. The hundred top-cited articles with the most frequent citation were selected. The publication time, country of origin, journal, institution, professional field of corresponding author, funding type, publication type, etc. were analyzed.ResultsThe hundred top-cited articles were published between 1992 and 2013, with 300～5 980 citations and a total of 65 719 citations. The main types of articles were clinical studies (73 articles), reviews (20 articles), guidelines (4 articles) and basic research (3 articles). Fourteen authors published more than one first-author paper, and fifteen authors published more than one articles as corresponding authors. These authors were distributed across 22 subject areas. The most cited country was the United States (60 articles), and the most cited institution was the University of Wisconsin (10 articles). The hundred top-cited articles were published in 31 journals, most of which were cited less than 1 000 times, and a few articles were cited more than 2 000 times.ConclusionsOSAHS has attracted much attention in respiratory medicine, neurology, epidemiology and other fields, and many articles about clinical research types of OSAHS have been cited. In addition, most of the highly cited articles in the OSAHS field come from the developed countries; our country needs to devote more resources to OSAHS research.
ObjectiveTo systematically review the risk factors associated with sleep disorders in ICU patients.MethodsWe searched The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, Wanfang Data, VIP and CBM databases to collect cohort studies, case-control studies and cross-sectional studies on the risk factors associated with sleep disorders in ICU patients from inception to October, 2018. Two reviewers independently screened literature, extracted data and evaluated the bias risk of included studies. Then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 9 articles were included, with a total of 1 068 patients, including 12 risk factors. The results of meta-analysis showed that the combined effect of equipment noise (OR=0.42, 95%CI 0.26 to 0.68, P=0.000 4), patients’ talk (OR=0.53, 95%CI 0.42 to 0.66, P<0.000 01), patients’ noise (OR=0.39, 95%CI 0.21 to 0.74, P=0.004), light (OR=0.29, 95%CI 0.18 to 0.45, P<0.000 01), night treatment (OR=0.36, 95%CI 0.26 to 0.50, P<0.000 01), diseases and drug effects (OR=0.17,95%CI 0.08 to 0.36, P<0.000 01), pain (OR=0.37, 95%CI 0.17 to 0.82, P=0.01), comfort changes (OR=0.34,95%CI 0.17 to 0.67,P=0.002), anxiety (OR=0.31,95%CI 0.12 to 0.78, P=0.01), visit time (OR=0.72, 95%CI 0.53 to 0.98, P=0.04), economic burden (OR=0.63, 95%CI 0.48 to 0.82, P=0.000 5) were statistically significant risk factors for sleep disorders in ICU patients.ConclusionCurrent evidence shows that the risk factors for sleep disorders in ICU patients are environmental factors (talking voices of nurses, patient noise, and light), treatment factors (night treatment), disease factors (disease itself and drug effects, pain,) and psychological factors (visiting time, economic burden). Due to the limited quality and quantity of included studies, more high quality studies are needed to verify the above conclusions.
Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen’s Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.
Objective To understand the incidence of frailty in maintenance hemodialysis (MHD) patients, and to explore the correlation and influencing factors of frailty in MHD patients, so as to provide some basis for the intervention of frailty in MHD patients. Methods Patients who underwent MHD in the Department of Nephrology of West China Hospital of Sichuan University from January to March 2021 were selected. Frail scale and Pittsburgh Sleep Quality Index (PSQI) were used for evaluation, and the influencing factors of frail in patients with MHD and its correlation with frail were analyzed. Results A total of 141 patients with MHD were included, including 57 cases without frailty (40.43%), 71 cases in early frailty (50.35%), and 13 cases in frailty (9.22%). 54 cases (38.30%) had very good sleep quality, 56 cases (39.72%) had good sleep quality, 24 cases (17.02%) had average sleep quality, and 7 cases (4.96%) had very poor sleep quality. The frailty of MHD patients was positively correlated with age (rs=0.265, P=0.002), PSQI (rs=0.235, P=0.005) and magnesium (rs=0.280, P=0.001). Logistic regression analysis showed that the influencing factors of MHD patients’ frailty were gender [odds ratio (OR) =4.321, 95%confidence interval (CI) (1.525, 12.243), P=0.006], PSQI [OR=1.110, 95%CI (1.009, 1.222), P=0.032], magnesium [OR=122.072, 95%CI (4.752, 3 135.528), P=0.004], hypertension [OR=0.112, 95%CI (0.023, 0.545), P=0.007] and other diseases [OR=0.102, 95%CI (0.019, 0.552), P=0.008]. Conclusions The incidence of frailty in MHD patients is high. Gender, PSQI, magnesium, hypertension and other diseases are the influencing factors of frailty in MHD patients, and there is a correlation between frailty and sleep. It is suggested that renal medical staff should pay more attention to the assessment of MHD frailty and sleep, and carry out multi-disciplinary personalized intervention to improve the quality of life of MHD patients.