Objective To evaluate the effectiveness ofradiofrequency volumetric tissue reduction （RFVTR） in the treatment of sleep disordered breathing （SDB ）. Methods We searched The Cochrane Central Register of Controlled Trials （Issue 1, 2005）, MEDLINE （1966 to Apr. 2005）, EMBASE （1989 to Apr. 2005）, CINAHL （1982 to Dec. 2000）, VIP （1989 to Dec. 2004） , CJFD （1979 to 2005）, WANFANG DATA （1977 to 2004） , and CBMdisc （1978 to 2005）. The bibliographies of all papers retrieved in full text form and relevant narrative reviews were searched for additional publications. All randomized controlled clinical trials （RCT） or quasi-randomized controlled trials （quasi-RCT） or prospective cohort studies of RFVTR alone or in combination with other treatments compared with placebo or other treatments were included. Data were extracted independently from the trial reports by the two authors. Meta-analysis was performed using RevMan software. Results There were 11 studies including 540 patients met the inclusion criteria for this review, among which five were RCTs, six were prospective cohort studies, and all trials were of lower methodological quality. RFVTR showed benefit over placebo in apnea index （AI）, but this benefit was not seen in other polysorrmography （PSG） parameters, symptom and quality of life, psychomotor vigilance pain, swallowing difficulty and adverse events. Compared with continuous positive airway pressure （CPAP）, uvulopalatopharyngoplasty （UPPP） and laser assisted uvulopalatoplasty （LAUP） , RFVTR was more effective in psychomotor vigilance pain and swallowing difficulty, but this effect was not seen in PSG parameters, symptom and quality of life. Conclusions RFVTR is more effective than placebo in AI improvement and other treatments in decreasing postoperative pain and other adverse events ; but this benefit was not seen in improving quality of sleeping and life. More well-designed randomized trials need to be conducted to identify the effectiveness and the influence on effectiveness of severity and frequency of treatment.
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 investigate the relative factors of sleep disorders in patients after arthroscopic knee surgery.MethodsThe clinical data of 155 patients undergoing arthroscopic knee surgery in West China Hospital of Sichuan University from October 1st 2017 to April 1st 2018 were analyzed. The sleep quality index was assessed by the Pittsburgh Sleep Quality Index. According to the scores, the patients were divided into two groups: the sleep disorder group and the non-sleep disorder group. Mindfulness level were evaluated by Mindful Attention Awareness Scale (MAAS). The binary logistic regression was used to assess the relationship between MAAS and sleep disorders.ResultsAmong the enrolled cases, there were 135 males and 20 females, with an average age of (34.12±12.13) years; 64 patients had poor sleep quality. The results of univariate analysis showed that the payment method, single or bilateral lesions, and MAAS score were the factors affecting sleep (P<0.05). The results of multivariate analysis indicated that with medical insurance [odds ratio (OR)=0.118, 95% confidence interval (CI) (0.021, 0.671), P=0.016], postoperative pain [OR=3.379, 95%CI (1.164, 9.812), P=0.025], bilateral lesions [OR=3.842, 95%CI (1.040, 14.191), P=0.044], and MAAS score ≥68 [OR=0.046, 95%CI (0.018, 0.121), P<0.001] were independent predictive factors for sleep disorders (P<0.05). Conclusion Postoperative pain reduction and mindfulness training may improve the patients’ sleep quality and reduce the incidence of sleep disorders.
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.
ObjectiveTo explore and clarify the relationship between epileptic seizure and inducing factors. Avoid inducing factors and reduce epileptic seizure, so as to improve the quality of life in patients with epilepsy.MethodsClinical data of 604 patients diagnosed with epilepsy in Xijing Hospital of Air Force Military Medical University from January 2018 to January 2019 were collected. The clinical data of patients with epilepsy were followed up 6 months.ResultsAmong the 604 patients, 318 (52.6%) were seizure-free in the last 6 months, 286 (47.4%) had seizures. 169 (59.1%) had seizures with at least one inducing factor. Common inducing factors: 123 cases of sleep disorder (72.8%), 114 cases of emotion changes (67.5%), 87 cases of irregular medication (51.5%), 97 cases of diet related (57.4%), 33 cases of menstruation and pregnancy (19.5%), etc. Using the χ2 test, seizures with age, gender differences had no statistical significance (P > 0.05), but seizure type was statistically different between inducing factors. In generalized seizures, tonic-clonic seizures associated with sleep deprivation (χ2= 0.189), absence seizures and anger (χ2= 0.237), pressure (χ2= 0.203), irregular life (χ2= 0.214). In the focal seizures, focal motor seizures was correlated with coffee consumption (χ2=0.145), focal sensory seizures with cold (χ2=0.235), electronic equipment use (χ2 =0.153), satiety (χ2 =0.257). Complex partial seizures was correlated with anger (χ2 =0.229), stress (χ2 =0.187), and cold (χ2 =0.198). The secondarily generalized seizures was correlated with drug missing (χ2 =0.231), sleep deprivation (χ2 =0.158), stress (χ2 =0.161), cold (χ2 =0.263), satiety (χ2 =0.182). Among the inducing factors, sleep deprivation was correlated with anger (χ2 =0.167), fatigue (χ2 =0.283), and stress (χ2 =0.230).ConclusionsEpileptic seizure were usually induced by a variety of factors. Generalized seizures were associated with sleep disorders, emotional changes, stress, irregular life, etc. While focal seizures were associated with stress, emotional changes, sleep disorders, cold, satiety, etc. An analysis of the triggers found that sleep deprivation was associated with anger, fatigue, and stress. Therefore, to clarify the inducing factors of epileptic seizure, avoid the inducing factors as much as possible, reduce the harm caused by seizures, and improve the quality of life of patients.
ObjectiveTo explore the status of common comorbidities in adult epilepsy patients in western China, and to explore the related risk factors.MethodsThe Chinese version of Generalized Anxiety Disorder (GAD)-7, neurological disorders depression inventory for epilepsy (NDDI-E) scales, pittsburgh sleep quality index scale (PSQI) and epworth sleepiness scale (ESS) were used to evaluate the 199 epilepsy patients between April 2017 and March 2018 in the Epilepsy Center of Neurology Department of Sichuan People's Hospital. Logistic regression analysis was performed on the risk factors of epilepsy comorbidity.ResultsIn the 199 adult epilepsy patients, 28.1% had anxiety, 17.1% had depression, 33.2% had sleep disorder, and 2.5% had migraine. 140 patients received monotherapy, including 15 patients with carbamazepine (CBZ), 20 patients with lamotrigine (LTG), 26 patients with levetiracetam (LEV), 31 patients with topiramate (TPM), 25 patients with oxcarbazepine (OXC), and 23 patients with Valproate (VPA).Multivariate logistic regression analysis of epilepsy patients treated with monotherapy showed that seizure occurring more than once a month, LEV, TPM, sleep disorders were independent risk factors for anxiety in patients with epilepsy (P<0.05). Unemployment, seizure occurrence in the last three months, sleep disorders were independent risk factors for comorbid depression (P<0.05). Anxiety, depression, daytime sleepiness, CBZ, LTG were independent risk factors for comorbid sleep disorders (P<0.05).ConclusionsAnxiety, depression and sleep disorder are common comorbidities in adults with epilepsy in westChina. For patients with affective disorder and sleep disorder, early identification and intervention may be important to improve the quality of life and prognosis of patients. In addition, patients treated with LEV or TPM monotherapy had a higher risk of anxiety than other drugs. Patients with LTG and CBZ monotherapy are more likely to comorbid sleep disorders.
Patients with autoimmune encephalitis are mainly characterized by behavioral, mental and motor abnormalities, neurological dysfunction, memory deficits and seizures. Different antibody types of autoimmune encephalitis its pathogenesis, clinical characteristics are different, in recent years found immune related epilepsy is closely related to autoimmune encephalitis, based on autoimmune encephalitis type is more, we choose more common autoimmune encephalitis, expounds its characteristics, to help clinical diagnosis.
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.