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.
Objective To develop and validate a prediction model to assess the risk of depression in patients with chronic kidney disease (CKD) based on National Health and Nutrition Examination Survey (NHANES) database. Methods Data on patients with CKD were selected from the NHANES between 2005 and 2018. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio for model development and validation, respectively. Multivariable logistic regression was used in the training set to identify independent risk factors associated with depression in CKD patients, with stepwise selection applied to determine the final predictors. Model performance was assessed using receiver operating characteristic curve (ROC), calibration plots, and decision curve analysis (DCA). Internal validation was performed through bootstrap resampling, and a predictive model was ultimately established. Results A total of 4413 CKD patients were included, including 2112 males (47.86%) and 2301 females (52.14%). Among them, 3089 patients were assigned to the training set and 1324 to the validation set. In the training set, 332 patients (10.75%) presented with depressive symptoms, while 143 patients (10.80%) in the validation set had depressive symptoms. Multivariate logistic regression analysis showed that other hispanic, current smoking, and sleep disorders were risk factors (P＜0.05). Male, middle or high-income, high school grad/ged or above, married or widowed were protective factors (P＜0.05). Finally, 7 variables were included to construct a prediction model, including gender, poverty income ratio, education level, marital status, smoking status, body mass index, and sleep disorders. The ROC curve showed that the AUC=0.773 [95% confidence interval (0.747, 0.799)] in the training set, the internal validation was evaluated by 1000 Bootstrap resampling methods, and the corrected C-index=0.763. The validation set AUC=0.778 [95% confidence interval (0.740, 0.815)], showed good discrimination ability. The calibration curve showed that the model’s predicted probability was highly consistent with the actual occurrence. Decision curve analysis showed that the model provided a significant net benefit for clinical decision-making at a threshold probability of 20%～50%. Conclusions The prediction model constructed in this study can effectively predict the risk of depression in patients with CKD and can provide guidance for early screening and personalized intervention for high-risk groups. However, the external validation and localization of the model still needed further research.
【摘要】 目的 了解老年惡性腫瘤患者的抑郁狀況及引起抑郁的因素，并針對抑郁的主要因素制定多層次、全方位、科學的護理干預措施，改善老年惡性腫瘤患者的抑郁心理，提高其生活質量。 方法 對2009年12月-2010年4月183例老年惡性腫瘤患者分別應用一般資料調查表和 Brink的老年抑郁量表（GDS）進行調查。從文化程度，疾病認識程度，對社會、家庭支持的滿意度，付費方式等方面進行了比較和分析。 結果 老年惡性腫瘤患者的抑郁患病率為80.87%。明顯高于一般老年人及老年慢性病患者；而不同文化程度、對疾病認知程度、患者對社會、家庭支持的滿意度與抑郁情緒的發生有一定的關系（Plt;0.05）。 結論 護理人員需加強對患者的疾病知識的健康教育，努力提高患者的社會支持滿意度，以減輕患者的抑郁情緒。【Abstract】 Objective To explore the state and the etiology of depression in patients with geratic malignant tumor, and to develop the global and scientific nursing management for patient with geratic malignant tumor, and improve the patients′ depression and life quality. Methods A total of 183 patients with geratic malignant tumor from December 2009 to April 2010 were investigated by questionnaire survey with Brink′s geratic-depression-scale (GDS). The education level, disease′s awareness level, satisfaction level for family and scocial supports, and the payment mode were analyzed. Results Depressive prevalence in malignant tumor patients (80.87%) was much higher than that in the normal elder people (10%-15%) and in the patients with chronic disease (31.0%). Different education level, disease′s awareness level, satisfaction level for family and social supports were related to the depressive prevalence (Plt;0.05). Conclusion Nursing faculty should enhance the health education to the patients with geratic malignant tumor, increase the satisfaction for social support and decrease their depression.
目的 了解成都市臥床老年人的抑郁發生情況及影響因素。 方法 對2009年12月－2011年2月臥床時間＞1個月的325例臥床老年人采用老年抑郁量表、焦慮自評量表、生活滿意度指數A進行調查，并對影響的抑郁的相關因素進行統計分析。 結果 成都市臥床老年人抑郁的發生率為57.5%。不同病情、生活自理能力、焦慮情況、社會交往情況、生活的滿意度和家庭功能的老年人，其抑郁評分差異有統計學意義（P＜0.05）。多重線性回歸分析發現影響老年人抑郁的主要因素有病情、臥床分級、焦慮、社會活動、滿意度、文化程度，其中對生活滿意、有社會活動、文化程度高是保護因素，而焦慮、病情較重、臥床等級高是抑郁的危險因素。 結論 臥床老年人的抑郁發生率較高，應加強對臥床老年人，特別是病情重、焦慮、大部分或者全天臥床、低文化的臥床老人抑郁發生的關注，鼓勵老年人增加社會活動、提高老年人對生活的滿意度和增進他們的心理健康。
ObjectiveTo investigate the fatigue of asthma patients, and to analyze its influencing factors, and provide a reference for clinical intervention.MethodsThe convenience sampling method was adopted to select asthma patients who were in clinic of the First Affiliated Hospital of Guangxi Medical University from November 2018 to March 2019. The patients’ lung function were measured. And questionnaires were conducted, including general data questionnaire, Chinese version of Checklist Individual Strength-Fatigue, Asthma Control Test, Chinese version of Self-rating Depression Scale. Relevant data were collected for multiple stepwise linear regression analysis.ResultsFinally, 120 patients were enrolled. The results of multiple stepwise linear regression analysis showed that age, education level, place of residence, time period of frequent asthma symptoms, degree of small airway obstruction, Asthma Control Test score and degree of depression were the influencing factors of fatigue in asthma patients (P≤0.05). Multivariate linear stepwise regression analysis showed that degree of small airway obstruction, degree of depression and time period of frequent asthma symptoms were the main influencing factors of fatigue in asthma patients, which could explain 51.8% of the variance of fatigue (ΔR2=0.518).ConclusionsThe incidence of fatigue in asthma patients is at a relatively high level. Medical staff should pay attention to the symptoms of fatigue in asthma patients. For asthma patients, it is recommended to strengthen standardized diagnosis and treatment, reduce the onset of symptoms at night and eliminate small airway obstruction. Psychological intervention methods are needed to improve patients’ depression, reduce fatigue symptoms, and improve quality of life.
Based on literatures on Meta-analysis and randomized controlled trial, drug use and some geriatrics syndromes such as cognitive impairment and depression, in elderly diabetic patients were reviewed. Insulin plus oral hypoglycemic drugs was more rational therapy for insulin resistance and islet dysfunction in type 2 diabetes mellitus. We should pay more attention to cognitive impairment and depression in elderly type 2 diabetic patients.
Objective To evaluate the changes in depressive symptoms and emotional responses in obstructive sleep apnea (OSA) patients after six months of continuous positive airway pressure (CPAP) therapy. Methods From June 2021 to December 2023, adult patients diagnosed with OSA at our hospital who were recommended for CPAP therapy as a first-line treatment were recruited. Demographic data (age, body mass index, gender), oxygen desaturation index, maximum duration of apnea and maximum duration of apnea were recorded. The patients were divided into a CPAP group and a non-CPAP group according to whether they were compliant to CPAP treatment. All patients completed questionnaires (including CES-D, DERS, ERS, and ESS) at 0, 1, 2, 4, and 6 months. Differences in general data and questionnaire results were compared between the two groups. Results The patients in the CPAP group showed significantly lower levels of depression and daytime sleepiness at 1, 2, 4, and 6 months compared with the non-CPAP group, with statistically significant differences (all P<0.05). Additionally, the CPAP group exhibited significantly lower scores in emotional responses and difficulties in emotion regulation across the same time points, with statistically significant differences (all P<0.05). In the non-CPAP group, increases in the apnea hypopnea index (AHI) and worsening emotional responses were key factors contributing to the exacerbation of depressive symptoms in OSA patients, with statistically significant differences (P<0.05). Conclusions CPAP therapy significantly improves depressive symptoms, emotional responses, and emotional regulation in OSA patients. Increases in the AHI and worsening emotional responses are primary factors leading to the worsening of depressive symptoms in OSA patients.
ObjectiveTo investigate the status of quality of life and influencing factors among newly diagnosed epilepsy patients with co-morbid anxiety and depression. MethodsA total of 180 newly diagnosed epilepsy patients from June 2022 to December 2022 in a district of Shanghai were selected as the study subjects. The Quality of Life in Epilepsy-31 (QOLIE-31), Hamilton Depression Rating Scale (HAMD-24), Hamilton Anxiety Rating Scale (HAMA), and Epilepsy Self-Management Scale (ESMS) were used to assess patients' quality of life, depression levels, anxiety levels, and self-management abilities, respectively. Patients were divided into the co-morbid depression group (HAMA≥14 and HAMD>17) and the control group (HAMA<14 and HAMD≤17), and their general characteristics and scale scores were compared. Spearman correlation, Pearson correlation, and multiple linear regression analysis were used to identify influencing factors of quality of life in epilepsy patients with co-morbid depression. ResultsCompared to the control group, the anxiety comorbid with depression group of older adults had a higher proportion, higher unemployment rate, lower personal and family annual income in the past year, higher frequency of epileptic seizures, and lower medication adherence (P<0.05). The correlational analysis revealed a negative correlation between the quality of life abilities of epilepsy patients with comorbid anxiety and depression and the severity of anxiety and depression. (r=?0.589, ?0.620, P<0.05). The results of multiple linear regression analysis showed that the frequency of seizures in the past year (β=?1.379, P<0.05), severity of anxiety (β=?0.279, P<0.05), and severity of depression (β=?0.361, P<0.05) have an impact on the ability to quality of life in epilepsy patients with co-morbid anxiety and depression. These factors account for 44.1% of the total variability in quality of life (R2=0.4411, P<0.05). ConclusionThe frequency of seizures in the past year, as well as the severity of anxiety and depression, are important factors that influence the ability to quality of life in epilepsy patients with comorbid anxiety and depression. For these patients, it is crucial to take into account these factors and provide appropriate support and interventions.
Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of “small world” and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.