Objective To analyze the causes of missed diagnosis of sleep apnea hypopnea syndrome ( SAHS) . Methods 42 missed diagnosed cases with SAHS from May 2009 to May 2011 were retrospectively analyzed and related literatures were reviewed. Results The SAHS patients often visited the doctors for complications of SAHS such as hypertension, diabetes mellitus, metabolic syndrome, etc. Clinical misdiagnosis rate was very high. Lack of specific symptoms during the day, complicated morbidities, and insufficient knowledge of SAHS led to the high misdiagnosis rate and the poor treatment effect of patients with SAHS. Conclusion Strengthening the educational propaganda of SAHS, detail medical history collection, and polysomnography monitoring ( PSG) as early as possible can help diagnose SAHS more accurately and reduce missed diagnosis.
Objective
To summarize and analyze the clinical and video-EEG (VEEG) characteristics of adult sleep-related epilepsy, so as to provide evidence for clinical diagnosis, differential diagnosis and treatment.
Methods
The clinical data, routine EEG and long-term VEEG of 187 adult patients with sleep-related epilepsy treated in Department of Neurology, Xiangya Hospital, Central South University from January 2017 to December 2017 were retrospectively analyzed by χ2 test.
Results
Clinical manifestations: The duration of sleep-related epilepsy in 187 adults was concentrated in 1～10 years (101 cases, 54.01%); the frequency of seizures was mainly from several to dozens of times a year (99 cases, 52.94%); 119 cases (63.64%) had two or more types of seizures. Among the patients, 121 cases (39.29%) had focal origin, 152 cases (49.35%) had bilateral tonic clonus and 110 cases (58.82%) were treated with two or more drugs. EEG results: ① The detection rate of epileptiform discharges in routine EEG was 22.78%, and that in long-term video EEG was 80.43%. There was significant difference between the two methods (P< 0.01); ② Eighteen epileptiform discharges were monitored by routine EEG during interparoxysmal period and 111 epileptiform discharges were monitored by video EEG; and ③ Fifty-six epileptic events were monitored and all occurred in the process of long-term VEEG monitoring, 50 of them occurred in sleep (89.29%) and 6 in awake (10.71%); 45 cases (80.36%) were diagnosed as epileptic seizures, 9 cases (16.07%) were diagnosed as non-epileptic seizures, and 2 cases (3.57%) could not be determined. ④ The detection rate of epileptic discharges during sleep was higher than that during awake period in long-term VEEG monitoring (P< 0.01). The detection rate of epileptiform discharges in NREM stage I–II was the highest in sleep stage.
Conclusion
Sleep-related epilepsy in adults has certain clinical features and EEG manifestations. Compared with conventional EEG, long-term video-EEG can improve the detection rate of epileptiform discharges, provide diagnostic basis for the qualitative analysis of sleep-related seizures, and reflect the relationship between epileptiform discharges and sleep, and provide basis for the clinical diagnosis and treatment of sleep-related epilepsy in adults.
Objectives
To study the characteristics and influencing factors of sleep disorder in patients with epilepsy.
Methods
One hundred and eighty-four patients with epilepsy who were admitted to the outpatient department and the epilepsy center in the Second Affiliated Hospital of Zhejiang University from October 2016 to October 2017 were enrolled. Their clinical data were collected in detail and their sleep related scales were evaluated. Sleep related assessment tools: Chinese version of the Pittsburgh sleep quality index scale (PSQI), the Epworth sleepiness scale (ESS), Berlin Questionnaire (BQ), Quality Of Life In People With Epilepsy-31 (QOLIE-31), Beck Anxiety Inventory (BAI) and Beck Depression Inventory(BDI).
Results
Among the 184 cases of patients with epilepsy, 100 cases were male (54.3%), 84 cases were female (45.7%), 35 cases (19.0%) had sleep disorders, 89 cases (48.4%) with poor quality of life, 23 cases (12.5%) with anxiety, 47 cases (25.5%) with depression, 59 cases (32.1%) had daytime sleepiness, and 30 cases (16.3%) with OSAS. there were statistically significant differences in age, history of hypertension, seizure frequency, quality of life , anxiety and depression in epilepsy patients with sleep disorder compared those without sleep disorder (P<0.05). The seizure frequency, quality of life, anxiety and depression were analyzed by logistic regression analysis, suggesting that seizure frequency (P=0.011) and depression (P<0.001) are independent risk factors of sleep disorders.
Conclusions
Epileptic patients with sleep disorder have higher frequency of seizures, poorer quality of life, and are more likely to be associated with anxiety and depression, and the frequency and depression are independent risk factors of sleep disorder in patients with epilepsy.
ObjectiveTo investigate the diagnostic value of oximetry in sleep apnea hypopnea syndrome (SAHS).
MethodsAdult patients suspected for SAHS were enrolled between May 2010 and May 2013. The patients underwent both polysomnography (PSG) and oximetry for further diagnosis. Apnea hyponea index (AHI) and oxygen desaturation index four (ODI4) were calculated on a single night. The relationship between AHI and ODI4 were analyzed.
ResultsA total of 628 adult patients were recruited.ODI4 was linearly correlated with AHI with a regression coefficient of almost 1. The cut-off values of ODI4 for indentifing SAHS and moderate to severe SAHS were 10 events per hour and 20 events per hour, with specificities of 99.9% and 99.3%, and AUCs of 0.931 and 0.934, respectively. Female, lower weight and less severe SAHS patients were easily misdiagnosed.
ConclusionsThere is a high agreement between AHI and ODI4. Oximetry is less likely misdiagnose SAHS.
ObjectiveTo systematically review the efficacy and safety of non-pharmacological interventions for sleep disturbance in dementia, and to provide evidence for clinical practice.MethodsDatabases including CNKI, WanFang Data, VIP, PubMed, EMbase and The Cochrane Library were searched to collect randomized controlled trials (RCTs) on non-pharmacological interventions for sleep disturbance in dementia from inception to May 2020. Two reviewers independently screened literature, extracted data, and assessed risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 software.ResultsA total of 9 RCTs were included, involving 720 patients. Light therapy was the most commonly used treatment, followed by special activity and sleep education program. The results of meta-analysis showed that compared with the control intervention, light therapy could improve sleep efficiency (MD=2.21, 95%CI 1.09 to 3.33, P=0.0001) and the night-time sleep (MD=14.27, 95%CI 5.01 to 23.53, P=0.003) of patients with dementia in the community and nursing institutions, special activity could increase the night-time sleep (MD=29.74, 95%CI 20.44 to 39.04, P<0.00001), and sleep education program could also improve sleep efficiency (MD=6.19, 95%CI 5.22 to 7.16, P<0.00001) and night-time sleep (MD=33.95, 95%CI 25.40 to 42.50, P<0.00001). In addition, it was superior to obtain 120 or 60 minutes of light exposure than 30 minutes to improve the quality of sleep (RR=?2.62, 95%CI ?3.56 to ?1.68, P<0.001) and reduce daytime sleep (RR=?4.75, 95%CI ?5.71 to ?3.42, P<0.001). However, there was significant difference in incidence of adverse reactions between groups of 120 minutes and 30 minutes of light exposure (RR=2.57, 95%CI 1.44 to 4.58, P=0.001).ConclusionsThe current evidence shows that non-pharmacological intervention can improve sleep efficiency and night-time sleep in patients with dementia. Due to limited quantity and quality of the included studies, more high quality studies are required to verify above conclusions.
Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.
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.
Objective
To analyze the risk factors of prethrombotic state of obstructive sleep apnea and hyponea syndrome (OSAHS), providing basis and reference for the prevention of prethrombotic state of OSAHS.
Methods
Two hundred and thirty-eight patients excluding the presence of possible effects of coagulation factors from June 2014 to July 2016 were diagnosed as OSAHS by polysomnography (PSG) and underwent coagulation, thrombosis, fibrinolysis, and inflammatory factors testing. Fifty-six patients met the standard of prethrombotic state (prethrombotic state group) and 59 patients randomly selected from the remaining 182 patients did not meet the standard (non-prethrombotic state group). The age, sex, body mass index (BMI), sleep apnea and hypopnea index (AHI), interleukin-6 (IL-6), complicating chronic obstructive pulmonary disease (COPD) and hypertension were compared between two groups.
Results
Non conditional Logistic regression analysis showed that the risk factors of prethrombotic state of OSAHS were age (OR=1.202, 95%CI: 1.107 to 1.305), IL-6 (OR=1.127, 95%CI: 1.014 to 1.252), AHI (OR=1.151, 95%CI: 1.055 to 1.256), and complicating COPD (OR=4.749, 95%CI: 1.046 to 21.555).
Conclusion
Age, AHI, IL-6, and complicating COPD may be the risk factors of prethrombotic state of OSAHS, among which complicating COPD may be the most important risk factor.
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.
