目的 了解住院患者病死率以及死亡病例的年龄、性别及主要死因的分布状况，为疾病预防治疗提供科学依据。 方法 对重庆医科大学附属第二医院2007年1月－2011年12月3 030例住院死亡患者进行统计分析，根据国际疾病分类第10版（ICD-10）进行疾病分类，所有资料经SPSS 13.0软件进行统计分析。 结果 该院近5年共有出院患者173 411例，死亡3 030例，病死率1.75%。各年龄段男性患者病死率均高于女性。各年度、不同性别前3位死因均依次为：肿瘤、循环系统疾病、呼吸系统疾病。 结论 慢性非传染性疾病是住院患者的主要死亡原因。提高医疗技术水平、普及群众的预防保健知识、大力开展健康体检是降低病死率的主要措施。
ObjectivesTo explore the characteristics of Chinese methodological studies on patient compliance in clinical trials so as to provide reference for clinical trial of patient compliance in future.MethodsCNKI, VIP, CBM and WanFang Data databases were electronically searched to collect methodological studies on patient compliance in clinical trials published in Chinese language from January 2000 to December 2018. Two reviewers independently screened literature, extracted data and then, qualitative analysis of document characteristics was then performed.ResultsA total of 84 articles were included, in which 68 were studies on Western medicine and 16 were studies on traditional Chinese medicine (TCM). The results showed that: the quantity of studies on patient compliance increased along with time. However, those in TCM field did not increase. All included studies summarized their strategies to improve patient compliance, however key information for quality evaluation were missing.ConclusionsThere is still a lack of research on how to improve patients’ compliance in clinical trials to ensure the reliability of the results. Existing studies have not systematically and comprehensively explored the influencing factors of patients’ compliance. Clinical trials researchers in China have not focused sufficiently to patients’ compliance and lack the proper methodology to frame studies.
Objective To investigate biological markers that differentiate states during various seizure periods of childhood absence epilepsy (CAE) by examining the spatiotemporal dynamics of magnetoencephalographic (MEG) signals from Default Mode Network (DMN) nodes, revealing the neurophysiological mechanisms underlying changes in consciousness during CAE seizures. MethodsThirty-six drug-native children diagnosed with CAE were recruited. The interictal data, ictal data of CAE children were collected using a CTF-225 channel MEG system. Conduct temporal homogeneity partitioning for all seizure period data, co-registering 14 distinct seizure states. Identify 12 brain regions associated with the default mode network (DMN) as regions of interest (ROI); employ minimum norm estimation in conjunction with the Welch method to compute the power spectral density (PSD) of the ROI; conduct differential analysis on the relative PSD values; and use a random forest model to identify significant PSD features that differentiate between states of epilepsy. ResultsPower changes in DMN-related brain regions across various frequency bands show significant synchrony. During a seizure, the power in the δ band rapidly increases at the onset and quickly decreases at the end. Meanwhile, the power in the θ-γ2 bands decreases at the beginning and gradually recovers after the seizure. During the O+2 phase following seizure onset, the power in the β band peaks briefly before rapidly declining. The medial frontal cortex has lower power in the δ frequency band during seizures compared to other DMN brain regions, but higher power in the α frequency band. The random forest model's feature importance analysis reveals that the precuneus, lateral temporal lobe and medial temporal lobe play important roles in identifying seizure states. Power changes in the precuneus in the α and δ frequency bands improve the model's classification accuracy. ConclusionsThis study investigated the dynamic spatiotemporal characteristics of the DMN during CAE seizures, revealing the typical manifestations of power changes in specific brain regions and frequency bands at the onset, maintenance, and termination of seizures. It was discovered that power of the precuneus can act as an important feature to distinguish between different stages of CAE seizures. These findings shed new light on the pathophysiological mechanisms underlying changes in consciousness states in CAE.