ObjectiveTo investigate the renal impairment and the risk factors of renal impairment in patients with OSA. MethodsData from patients who underwent polysomnography (PSG) in our department from July 2022 to January 2023 were collected, totaling 178 cases. Based on the results of the polysomnography, the patients were divided into an OSA group (145 cases) and a non-OSA group (33 cases). According to the severity of the condition, the OSA group was further divided into mild OSA (21 cases), moderate OSA (28 cases), and severe OSA (96 cases). The Pearson correlation analysis was further conducted to analyze the relationships between serum urea nitrogen (BUN), serum cystatin C (Cys-C) concentrations, and estimated Glomerular Filtration Rate (eGFR) with various risk factors that may influence renal impairment. Moreover, multiple linear regression analysis was used to identify the risk factors affecting BUN, Cys-C, and eGFR. ResultsWhen comparing the two groups, there were statistically significant differences in age, weight, BMI, neck circumference, waist circumference, eGFR、Cys-C、BUN, LSaO2, CT90% (all P<0.05). Univariate analysis of variance was used to compare differences in BUN, Serum creatinine (SCr), Cys-C, and eGFR among patients with mild, moderate, and severe OSA, indicating that differences in eGFR and Cys-C among OSA patients of varying severities were statistically significant. Further analysis with Pearson correlation was conducted to explore the associations between eGFR, BUN, and Cys-C with potential risk factors that may affect renal function. Subsequently, multiple linear regression was utilized, taking these three indices as dependent variables to evaluate risk factors potentially influencing renal dysfunction. The results demonstrated that eGFR was negatively correlated with age, BMI, and CT90% (β=−0.95, P<0.001; β=−1.36, P=0.01; β=−32.64, P<0.001); BUN was positively correlated with CT90% (β=0.22, P=0.01); Cys-C was positively correlated with CT90% (β=0.58, P<0.001. Conclusion Chronic intermittent hypoxia, age, and obesity are risk factors for renal dysfunction in patients with OSA.
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.
Objectives To systematically review the prevalence of sleep disorders in Chinese elderly population. Methods CNKI, Wanfang, VIP, PubMed and Web of Science were searched to collect studies on the prevalence of sleep disorders the Chinese elderly from January 2000 to November 2017. Two reviewers independently screened literatures, extracted data and evaluated risk of bias of the included studies, then meta-analysis was performed by Stata 14.0 software. Results A total of 19 cross-sectional studies were included. The results of the meta-analysis showed that, the overall prevalence of sleep disorders was 41.2% (95% CI 36.2% to 46.2%). Male and female prevalence rates were 35.7% and 45.0%, respectively. For individuals aged between 60 to 70, 70 to 80 and above 80, the prevalence rates were 29.9%, 42.0%, 44.2%, respectively. For individuals with primary school education and below, junior/high school education, college degree or above, the prevalence rates were 29.0%, 23.1%, 22.4%, respectively. The prevalence rate of individuals with normal marital status was 31.5%, and those with abnormal marital status (widowed, divorced, single, etc.) was 41.0%. The prevalence rate in individuals with in people with physical illness was 45.7%, and those without physical illness was 32.4%. For the urban population, the prevalence rate was 36.4%, while for the rural population, the prevalence rate was 42%. Conclusions The overall prevalence of sleep disorders in the Chinese elderly is high. The prevalence rate of sleep disorders among gender, age, educational level, marital status, physical illness, and living space is different.
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.
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.
Objective To systematically review the correlation between obstructive sleep apnea syndrome (OSAS) and the incidence of carotid atherosclerosis. Methods PubMed, EMbase, CNKI, WanFang Data, CBM, and VIP databases were electronically searched to collect studies on the correlation between OSAS and carotid atherosclerosis and carotid intima-media thickness (CIMT) from inception to August 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using Stata 16.0 software and RevMan 5.3 software. Results A total of 32 studies, including 2 915 patients were included. The results of the meta-analysis showed a higher incidence of carotid atherosclerotic plaque in OSAS patients than in the control group (OR=5.56, 95%CI 0.27 to 8.38, P<0.000 01); subgroup analysis revealed that, compared with the control group, patients who were male (OR=5.38, 95%CI 2.79 to 10.38, P<0.000 01) or had mild-to-moderate OSAS (OR=3.9, 95%CI 1.66 to 9.15, P=0.002) or severe OSAS (OR=19.86, 95%CI 6.49 to 60.82, P<0.000 01) had a higher risk of carotid atherosclerosis. The CIMT of the OSAS group was significantly higher than that of the control group (SMD=1.24, 95%CI 0.97 to 1.51, P<0.000 01). There was a positive correlation between the apnea hypopnea index (AHI) and CIMT in OSAS patients (r=0.52, 95%CI 0.44 to 0.60, <0.000 1), and the CIMT increased with OSAS severity. Conclusion OSAS is associated with a high incidence of carotid atherosclerotic plaque that is highly correlated with CIMT, which increases with an increase in the AHI. These findings are required to be verified in prospective high-quality studies to overcome the limitations of quantity and quality of studies included in this systematic review.
ObjectiveThe aim of this study was to investigate the changes in peripheral blood metabolites and transcriptomes in patients with obstructive sleep apnea (OSA) and to assess their diagnostic value as biomarkers. MethodsIn this study, we utilized liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipid-targeted metabolomics to compare the metabolic profiles of 30 OSA patients with those of 30 healthy controls, identifying differential lipid metabolites. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we determined that the glycerolipid metabolism pathway was significantly different. Furthermore, we conducted transcriptome analysis on peripheral blood mononuclear cells (PBMCs) from six OSA patients and six healthy controls to evaluate the expression of molecules related to the pathway. ResultsA total of 168 differential lipid metabolites were identified, with significant differences in the glycerolipid metabolism pathway between OSA patients and healthy controls. Transcriptome analysis revealed that glycerolipid metabolism-related molecules GPAT, AGPAT, and LPIN were under expressed in OSA patient PBMCs, suggesting that the glycerolipid metabolism pathway is suppressed in OSA patients. Additionally, diagnostic value analysis showed that GPAT and AGPAT had high AUC values, indicating their potential as biomarkers for OSA. ConclusionThe suppression of the glycerolipid metabolism pathway is closely related to the development of OSA, and the under expression of key genes in this pathway, such as GPAT, AGPAT, and LPIN, may be involved in the pathophysiological process of OSA. These findings not only provide a new perspective for understanding the pathogenesis of OSA but also offer new scientific evidence for the treatment of OSA from the perspective of glycerolipid metabolism regulation.
ObjectiveTo assess the polysomnographic characteristics of insomnia patients with comorbid obstructive sleep apnea-hypopnea syndrome (OSAHS). MethodsWe performed a comparative analysis on the polysomnographic features among patients with pure insomnia (n=80), patients with pure OSAHS (n=80), and patients with insomnia and OSAHS (n=50) between August and December 2013. ResultsCompared with OSAHS group, patients with insomnia and comorbid OSAHS had a higher percentage of female, older age, lower body mass index, shorter total sleep time during the night, longer sleep latent period and wake after sleep onset (WASO), lower sleep efficacy, lower arousal index and apnea hypoventilation index (AHI), higher average and the lowest oxygen saturation of blood, lower Epworth Sleepiness Scale scores and sleep perception (P < 0.05). Compared with the insomnia group, patients with insomnia and comorbid OSAHS had a lower percentage of female, shorter total sleep time, lower sleep efficacy, longer WASO and higher AHI (P < 0.05). ConclusionPatients with insomnia and comorbid OSAHS have all the characteristics of insomnia and OSAHS patients:nocturnal hypoxia, sleep fragmentation, broken sleep continuity, decreased sleep efficiency, damaged perception of sleep time and sleep perception.
Objective To systematically review the rate of sleep deprivation in children and adolescents in China from 2004 to 2019. Methods PubMed, The Cochrane Library, EMbase, Web of Science, CBM, CNKI and WanFang Data databases were searched to collect cross-sectional studies on the sleep deprivation rate of children and adolescents in China from inception to July 15th, 2021. Two researchers independently screened literature, extracted data and evaluated the risk of bias of the included studies. Meta-analysis was then performed by using Stata 15.0 software. Results A total of 45 cross-sectional studies were included, with a total sample size of 769 918 participants, of whom 587 457 reported sleep deprivation. The results of meta-analysis showed that the sleep deprivation rate of Chinese children and adolescents was 61% (95%CI 55% to 68%). Subgroup analysis indicated that the sleep deprivation rates were 62% for female children and 59% for male children. The rate was 84% in junior high school, 80% in high school and 64% in primary school. The rates in south China, southwest China, northwest China, north China, east China and central China were 68%, 62%, 61%, 57%, 57% and 54%, respectively. The rate of sleep deficiency based on "health requirements for daily study time of primary and junior school students" was the highest at 74% (95% CI 70% to 79%). The cumulative meta-analysis by time showed that the sleep deprivation rate had gradually stabilized and approached 60% since 2011. Conclusion Current evidence shows that the sleep deprivation rate of Chinese children and adolescents is high. Due to the limited quality and quantity of included studies, more high-quality studies are needed to verify the above conclusion.
Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.