利用新技术提高结核病患者治疗依从性的研究进展

提高结核病患者治疗依从性是减少结核病传播、控制结核病流行有效公共卫生措施之一。世界卫生组织（WHO）推荐以医务人员为主的直接面视下治疗（DOT）,但在落实中存在医务人员严重缺乏、交通不便等困难。目前,利用新技术（如智能手机、移动网络、卫星通信等）提供医疗服务和信息,打破了传统医疗行为与模式受时间和空间的限制,被广泛应用于结核患者治疗依从性的管理中。本文综述了国内外相关新技术在结核病患者治疗依从性中应用的干预形式、干预效果、医务人员和患者的使用体会,以期为我国推广相关新技术的使用、改善结核病患者的治疗依从性提供依据。     

无锡市社区严重精神障碍患者服药依从性及其影响因素研究

背景　遵医嘱服药是患者病情稳定和全面康复的基本保障,服药率是社区严重精神障碍患者管理治疗工作质量的核心指标。了解本地区居家患者的药物治疗现状并提出改进措施,对促进社区精神卫生服务质量的有效提升具有重要意义。目的　了解无锡市社区严重精神障碍患者服药依从性及其影响因素,探讨进一步提高患者服药率的有效措施。方法　采用多阶段整群抽样法,对无锡市12个街道/乡镇的1 285例严重精神障碍患者进行问卷调查。调查工具包括服药依从性问卷（自行设计）、自知力与治疗态度问卷及精神障碍患者病耻感评估量表。共1 220例患者完成全部调查,有效应答率为94.94%。采用多因素Logistic回归分析患者服药依从性的影响因素。结果　无锡市社区严重精神障碍患者的服药依从率为75.33%（919/1 220）,其中精神分裂症、双相情感障碍、分裂情感性障碍、偏执性精神障碍、精神发育迟滞伴发精神障碍、癫痫所致精神障碍服药依从率分别为78.56%（667/849）、75.49%（154/204）、69.57%（16/23）、47.06%（8/17）、35.71%（25/70）、85.96%（49/57）。不同文化程度、婚姻状况、家庭经济、监护人关系、监护人对疾病认知、疾病诊断、病程、住院次数、是否参加社区健康讲座、家庭与精神专科医院距离、自知力与治疗态度评分、病耻感水平患者服药依从率比较,差异均有统计学意义（P<0.05）。多因素Logistic回归分析结果显示,文化程度、监护人对疾病认知、疾病类型、病程、住院次数、是否参加健康讲座、家庭离精神专科医院距离、自知力与态度评分、病耻感水平9个因素为社区严重精神障碍患者服药依从性的影响因素（P<0.05）。结论　无锡市社区严重精神障碍患者服药依从性有待提高,增设精神卫生服务网点、重点关注精神发育迟滞及新发病患者、加强健康宣教、消除病耻感是提高服药依从性的有效措施。     

基于数据挖掘和网络药理学的肥胖用药规律及潜在作用分析

[目的]基于数据挖掘分析中药治疗肥胖的组方用药规律,并通过网络药理学方法分析高频药物治疗肥胖的潜在作用机制。[方法]以中国知识资源总库(CNKI)、中国生物医学文献数据库(CBMdisc)、中文科技期刊数据库(VIP)、万方数据资源系统为资料来源。使用Excel 2016,统计学软件R3.5.2,对数据进行频数分析、关联规则分析、聚类分析及主成分分析,使用BATMAN-TCM系统对高频药物所含化合物进行检索,获取这些化合物对应靶点,与Genecard数据库中检索obesity相关靶点相映射并取交集,通过STRING 11.0构建PPI网络,对其进行网络拓扑分析,筛选出高频药物治疗肥胖病的核心靶点,通过DAVID 6.8数据库进行KEGG通路分析获取高频药物治疗肥胖病的重要通路。[结果]筛选出符合排除与纳入标准的文献共143篇,其中收录中药组方148首、药物172味。通过分析处方中药物的使用频次、并进行关联规则分析、聚类分析以及主成分分析,得出药物使用频率≥10%的药物共21种,药物关联规则按支持度、置信度、提升度排序各6个药物组合,以及2个聚类药物群和主成分分析中4个主要维度的代表用药。通过网络药理学方法发现茯苓治疗肥胖病的核心靶点10个以及和肥胖病关系密切的通路20条。[结论]中药治疗肥胖主要从"气虚""痰湿""血瘀"3个方面进行调治。以健脾利湿化痰为主要治则,故用药多以具有益气健脾,燥湿化痰功效的中药为主,辅以具有化瘀消脂作用之泽泻、山楂、丹参、荷叶等。初步验证和预测了高频药物茯苓治疗肥胖的核心靶点和重要通路,为深入研究其作用机制提供参考。     

不同ASCVD危险分层患者LDL-C达标及影响因素分析

目的 分析不同动脉粥样硬化性心血管疾病（ASCVD）危险分层患者低密度脂蛋白胆固醇（LDL-C）达标情况及影响因素。方法 本研究纳入2018年6月～2019年3月至大连解放军第967医院就诊的既往给予他汀类药物治疗的患者为研究对象,通过询问并记录的方式收集、整理患者的一般情况和临床资料。根据《中国成人血脂异常防治指南（2016修订版）》将入选患者分为极高危（n=242）、高危（n=207）、低中危（n=139）三组。将极高危患者LDL-C达标定义为坚持或间断服用他汀类药物且LDL-C＜1.8 mmol/L,高危组及低中危组达标分别定义为LDL-C＜2.6 mmol/L和＜3.4 mmol/L,无论是否使用他汀类药物。采用单因素分析影响他汀服药依从性的相关变量,并建立Logistic回归模型进行多因素分析以探讨影响他汀服药依从性的独立影响因素。结果 ①LDL-C达标率在极高危患者为42.1%,高危患者为59.4%,低中危患者为81.3%;不同危险分层患者LDL-C达标率比较,差异有统计学意义（P=0.000）。②极高危和高危患者LDL-C不达标原因包括联合使用降脂药物（他汀类药物联合依折麦布）比例低（10.0%和2.4%）、使用低剂量他汀类药物（37.9%和31.0%）及不按医嘱服药（26.4%和56.0%）;低中危患者LDL-C不达标原因主要为使用低剂量他汀类药物（23.1%）和不按医嘱服药（76.9%）。③单因素分析显示,年龄≤65岁、使用药物种类≤3种和对药物认知良好的患者服药依从性显著增高（均P＜0.05）;独居状态、低收入及易被非医疗机构保健信息影响的患者服药依从性明显偏低（均＜0.05）。④多因素Logistic回归分析显示,使用药物种类少、对药物认知程度良好可以增加他汀类药物服药依从性;低收入、独居状态和非医疗机构保健信息影响可降低他汀类药物服药依从性。结论 改善ASCVD危险患者LDL-C达标率需强化医生对降脂药物的合理使用和提高患者服药依从性。     

基于关联规则的中药组分配伍治疗脑缺血再灌注损伤规律研究

目的探讨治疗脑缺血再灌注损伤(cerebral ischemia reperfusion injury,CIRI)的高频用药,并揭示用药与证型、组分与组分的关系,从组分层面解析中药治疗CIRI的配伍规律。方法收集中国知网(1998-08-25至2018-08-25)20年期刊文献数据库中治疗CIRI的中药及相关组分的研究,采用关联规则Apriori计算方法,对治疗CIRI的中药复方及相关组分的研究结果进行挖掘分析。结果高频次药物包括补阳还五汤、川芎嗪注射液、黄芪提取物等,印证及推测出构成丹参、三七等单味中药的活性成分及血栓通注射液和脑心通胶囊等中药复方的药效物质基础,并发现了芍药苷-阿魏酸、黄芪甲苷-藁本内酯-阿魏酸等新的组分配伍形式。结论基于关联规则,为CIRI中药组分配伍治疗挖掘高频用药、用药与证型、组分与组分的关联关系,可为组分配伍研发提供依据与参考。     

Electronic prescribing and other forms of technology to reduce inappropriate medication use and polypharmacy in older people: a review of current evidence

This review provided an overview of the current evidence in relation to the use of e-prescribing and other forms of technology, such as CDSS, to reduce inappropriate prescribing in older people. The evidence indicates that various types of e-prescribing and CDSS interventions have the potential to reduce inappropriate prescribing and polypharmacy in older people, but the magnitude of their effect varies according to study design and setting. There was significant heterogeneity in the studies reported in terms of study designs, intervention design, patient settings, and outcome measures with patient outcomes seldom reported. Widespread diffusion of these interventions has not occurred in any of the health care settings examined. Overall, health care providers report being satisfied with e-prescribing systems and see the systems as having a positive impact on the safety of their prescribing practices, yet the problem of overriding or ignoring alerts persists. The problem of large numbers of inaccurate and insignificant alerts and this issue, along with the other barriers that have been identified, warrant further investigation.     

Motor outcomes during hospitalization in Parkinson's disease patients: A prospective study

BackgroundRetrospective studies suggest that many Parkinson's disease patients have a worsening of their motor status during hospitalization. We aimed to quantify this prospectively, and study possible contributing factors.MethodsOver one year we included all consecutive Parkinson's disease patients, newly admitted to a Dutch teaching hospital. We analyzed complications, interventions, and medication distribution. At inclusion and at discharge we assessed the motor status with the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III).Results48% of 46 admitted patients had complications, mainly confusion/delirium (24%) and infections (15%). At discharge 28% of the patients had a worse motor function with a mean increase of more than 5 points on the UPDRS-III. Medication errors occurred in 39%. This is the most important risk factor (p < 0.000) for motor function deterioration, followed by infections during hospitalization, and not being in control of own Parkinson's disease medication. 24% of patients were allowed to take control of their own Parkinson's disease medication, none of these patients did deteriorate.ConclusionsThis prospective study shows that a substantial part of hospitalized PD patients has a significant worse motor function at discharge mainly due to medication errors and infections. Quality of care could be improved by addressing preventable errors and allow patients to take control of their own Parkinson's disease medication.     

Clozapine as a potential treatment for refractory impulsive,compulsive, and punding behaviors in Parkinson’s disease

There have been emerging cases of medication refractory obsessions, impulsivity, compulsivity, and/or punding in Parkinson’s disease. These cases have proven difficult to treat, even for the experienced clinician. We report several medication refractory cases with a positive response to treatment with clozapine.     

The Korean Medication Algorithm for Major Depressive Disorder (KMA-MDD): Report of the Korean Society of Depressive and Bipolar Disorders

Objective. There are many differences in biological characteristics, clinical situations, and medical insurance systems with ethnic groups or countries. The Korean Society of Depressive and Bipolar Disorders decided to develop a Korean treatment algorithm for major depressive disorder. Methods. The Korean Medication Algorithm Project for Major Depressive Disorder (KMAP-MDD) was designed with the following principles: (1) to be an ideal algorithm, (2) to be a Korean algorithm, (3) to be a medication algorithm, (4) to be an evidence-based and formal consensus algorithm. After collecting and reviewing many literature citations and reports by evidence-based rule, we constructed a survey questionnaire for formal consensus of Korean experts. By employing panels of experts to review the evidence and survey results thoroughly, we used evidence-based algorithm development as a component of a formal consensus development process. Results. We developed two algorithms for the KMA-MDD: one for major depressive disorder without psychotic feature and the other for major depressive disorder with psychotic features. Clinical guidelines for the implementation of KMA-MDD were also developed. The KMA-MDD provides specific treatment strategies for each stage. Conclusions. The KMA-MDD is the first Korean algorithm for treatment of major depressive disorder. It is based on evidence which supports the efficacy of each treatment, and it has obtained the consensus of Korean experts. We hope that the KMA-MDD will be good practical tool for clinicians who treat major depressive disorder in Korea.