摘要:目的: 观察免费治疗社区精神分裂症患者的疗效。 方法 :纳入贫困家庭精神分裂症患者140例,随机分为免费服药组和对照组,每组70例。随访1年,采用精神分裂症阳性与阴性症状量表(PANSS)\社会功能缺陷量表(SDSS)等评估。 结果 :对实验组与对照组的基线、6个月后及1年后随访的PANSS总分、各因子分、SDSS总分分别进行比较,结果显示基线、6月后均无统计学差异;1年后SDSS总分、PANSS总分、阳性因子分、一般病理因子、思维障碍、偏执因子分差异有显著性;免费治疗组1年后各指标与入组前相比分值降低(P＜001)。 结论 :精神分裂症患者免费服药后精神症状缓解明显,同时其社会功能缺陷也得到改善。Abstract: Objective: To observe the effect of the free treatment on schizophrenics from community. Methods : Totally 140 subjects from poor family were divided into the free treated group and the control group at random. They were followed up for 1 year. The treatment effects were evaluated by PANSS and SDSS. Results : There were no significant difference in all examinations at baseline and after 6 months; at the following end point, significant difference existed in the score of SDSS, the total scores of the PANSS, the positive factor, the general pathology factor, the thinking factor and the paranoid ideation factor between two groups. There was decrease in the scores for all examinations in the free treated group. Conclusion : The symptoms of schizophrenics by free treatment relieve significantly, and the social function improves.
The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
Objective To systematically review the health state utility values in patients with schizophrenia, and to provide references for subsequent studies on the health economics of schizophrenia. Methods The PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, and VIP databases were searched from inception to December 1st, 2021 to collect studies on health state utility values in patients with schizophrenia. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed by Stata 15.0 software. Results A total of 19 studies were included. Patients’ utility values were 0.68 (95%CI 0.59 to 0.77) for direct measures, and 0.77 (95%CI 0.75 to 0.80) and 0.66 (95%CI 0.61 to 0.70) for indirect measures with the EQ-5D-5L and EQ-5D-3L as the primary scales. Utility values varied with measures, tariffs, regions, and populations. Conclusion Studies on health state utility value in schizophrenia are diversified in measurement methods, showing high inter-study heterogeneity. Therefore, it is necessary to promote the study on utility value measurement in schizophrenia in China.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.
ObjectiveTo explore family care and its influence on the life quality of schizophrenia patients. MethodsBetween September 2011 and March 2012, 101 schizophrenia patients were investigated with Questionnaire of Family Care and Quality of Life Inventory and were divided into two groups in order to compare their life quality. According to the scores of Questionnaire of Family Care, 56 subjects were in support group and 45 subjects were in control group. ResultsAmong the 101 patients, 55.45% had good family care and 44.55% had not. In the support group, there was no significant correlation between family care and life quality in the first month and the third month (r=0.023, P=0.894; r=-0.072, P=0.587), while there was a significant correlation between family care and life quality in the sixth month (r=-0.322, P=0.032). In the control group, there was no significant differences in the score of family care and life quality in the first, third and sixth month (r=0.021, P=0.893; r=0.114, P=0.482; r=1.863, P=0.226). ConclusionLong-term family care is significantly correlated with the life quality of schizophrenia patients. If schizophrenic patients get more poor family care, they will have lower life quality. It's important to create a good and comfortable environment for the patients.
A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.