中国科技论文统计源期刊 中文核心期刊
美国《化学文摘》《国际药学文摘》
《乌利希期刊指南》
WHO《西太平洋地区医学索引》来源期刊
日本科学技术振兴机构数据库（JST）
第七届湖北十大名刊提名奖
美国《化学文摘》《国际药学文摘》
《乌利希期刊指南》
WHO《西太平洋地区医学索引》来源期刊
日本科学技术振兴机构数据库（JST）
第七届湖北十大名刊提名奖
, 杨林慧
, YANG Linhui
目的 了解国内外临床联合用药安全性与风险管理的研究现状热点,以期为进一步开展临床联合用药安全性与风险管理研究、促进临床合理用药提供参考。 方法 在Web of Science数据库和中国知网(CNKI)数据库以“联合用药”“安全性”“风险管理”等主题词组成检索式对相关文献进行检索,采用引文空间(CiteSpace)对筛选后的文献进行共词分析、聚类分析、突现分析以及合作网络分析,可视化呈现并比较分析国内外临床联合用药安全性与风险管理研究的热点、规律和分布情况。 结果 共纳入英文文献1174篇,中文文献1109篇。国外在联合用药潜在的药物-药物相互作用以及安全性评价领域的研究较为成熟,尤其是利用目前的药物-药物相互作用数据库和药物警戒数据库进行随机对照临床试验以及联合用药风险信号筛选两方面;国内研究则主要集中于联合用药方案的临床疗效与基于回顾性试验数据的联合用药不良反应情况分析等方面。 结论 近10年来联合用药的安全性研究已逐渐拓展至关注患者与联合用药的系统性上,当前关于我国联合用药安全性与风险管理的研究还存在诸多不足,难以真正满足临床需求及药品安全管理要旨。有效联合药品监管部门、医药企业、高校与科研院所,利用多方数据,在临床真实世界中探讨联合用药使用合理性、风险决策科学性,是医疗健康大数据发展背景下促进临床合理用药的重要发展方向。
Objective To understand the research frontiers and hotspots of safety and risk management of clinical drug combinations in China and abroad,in order to provide references for further research on safety and risk management of clinical drug combination and to promote the clinical rational drug use. Methods Taking "drug combination","safety" and "risk management" as keywords,relevant articles were searched in the databases of CNKI and Web of Science.CiteSpace was used to conduct co-word analysis,cluster analysis,emergence analysis,and cooperative network analysis on the selected articles,and to visually present and compare the hot spots,rules,and distribution of clinical combination therapy safety and risk management research at home and abroad. Results A total of 1174 English articles and 1109 Chinese articles were included.Foreign research in the field of potential drug-drug interaction and safety evaluation of combination therapy is relatively mature,especially in conducting randomized controlled clinical trials and screening risk signals of combination therapy using the current drug-drug interaction database and pharmacovigilance database.In China,the research mainly focused on the clinical efficacy of the drug combination regimens and the analysis of adverse reactions related to the drug combination based on retrospective experimental data. Conclusions In the past 10 years,research on the safety of drug combination has gradually expanded to be focused on patients and the systematic nature of the drug combinations.There are still many shortcomings in the current research on the safety and risk management of drug combination in China,and it is difficult to really meet the clinical needs and the gist of drug safety management.The effective combination of drug regulatory authorities,pharmaceutical enterprises,universities,and research institutes and the use of multi-party data to explore the rationality of drug combination and scientific risk decision-making in the real clinical world are a significant development direction to promote clinical rational drug use under the background of medical and health big data development.
开放科学(资源服务)标识码(OSID)
药品安全不仅是医疗安全监管体系中的重要一环,也是全球卫生服务领域中政府与学界广泛关注的焦点。2017年,世界卫生组织(WHO)在第2届全球患者安全部级峰会上呼吁创新理念与方法,在未来5年内将严重、可避免的药物伤害减少50%[1]。为实现患者安全用药的愿景,2022年WHO将“用药安全”定为第三届“患者安全日”的主题[2]。随着我国经济高质量发展和社会平稳进步,国家对人民群众身体健康的重视程度日益提升。联合用药(drug combination)是指为了达到治疗目的而同时或先后应用2种或2种以上药物[3]。其在发挥药物的协同治疗作用以提高疗效、增加患者用药依从性、延迟或减少耐药性的发生等方面具有一定的优势[4]。针对合并多种疾病的老年患者,临床联合用药十分普遍,中西药联合用药也广泛应用[5,6,7]。联合用药可能产生有害的药物-药物相互作用(drug-drug interaction,DDI),导致药物疗效减弱或药物的毒副作用加重,甚至产生严重不良反应。因药物停用和药物不良事件导致的住院病例中有超过1/5是由DDI引起的[8]。同时服用5种药物DDI导致药物不良事件风险增加50%,同时服用8种药物风险则增加100%[9]。2015—2020年全球药源性疾病致死率排名第四,仅次于心脑血管、肿瘤、卒中的病死率,其中超过70%为临床不合理联合用药造成[10]。上市前临床研究多针对于单药有效性和安全性且试验条件严苛,联合用药的安全信息极度匮乏。本研究拟通过全面检索国内外临床联合用药安全性与风险管理的相关文献,辅以CiteSpace可视化软件对研究现状进行分析形成科学知识图谱,捕捉文献核心关键词,分析在医疗健康大数据发展的背景下,临床联合用药安全性以及风险管理的文献记录结果,展示研究前沿和发展趋势,以期更好地服务于临床需求,促进合理用药。
文献检索以紧跟临床联合用药安全性与风险管理研究前沿与研究热点,且尽可能较为全面地纳入该领域相关文献为原则展开,纳入近10年来国内外相关文献。英文以Web of Science(WOS)核心合集为检索数据库,检索式为:TS=(“drugs combination”or“concomitant drugs”)and(“drug-drug interaction”or“adverse drug event”)and(“safety surveillance”or“signal detection”or“clinical risk management”),时间跨度为2012年1月1日—2022年3月1日(检索时间),文献类型选择论文(Article)。中文以中国知网(CNKI)数据库为检索源,检索式设定为:主题=(“联合用药”或“合并用药”)和(“安全性”或“风险管理”),时间跨度设定为2012年1月1日—2022年3月1日,文献类型选择学术期刊。文献排除标准为:①新闻、会议等其他类型的文献;②与联合用药明显不相关的文献;③重复文献。筛选后共获得有效英文文献1174篇,中文文献1109篇。
将相关文献导入CiteSpace5.8.R3版软件,对文献格式进行转化后建立分析项目:时间区间选择2012—2022年,时间切片为1年,将Node Types分别设置为关键词(Keyword)、作者(Author)、机构(Institution),绘制关键词共现及聚类知识图谱、关键词突现知识图谱、全部作者-机构合作网络图谱,同时辅以文献分析对比研究国内外近10年来联合用药安全性与风险管理研究的热点与发展趋势的异同。
2.1.1 关键词共现与聚类分析 共现图谱中节点圆环代表关键词频次,其大小代表关键词出现的频次以及时间跨度;各点之间的连线则反映该领域关键词之间的合作关系及密切程度[11]。同一关键词在一段时间高频出现,可被认为是该领域学者共同关注的热点和研究趋势。结果见
表1
2012—2022年国外临床联合用药安全性评价与决策风险管理文献关键词(
Tab.1
Keywords of foreign literatures on safety evaluation and decision-making risk management of clinical drug combination from 2012 to 2022(
聚类采取LLR算法,对文献标题、摘要、引用文献等整体进行信息提取,形成具有不同研究特点的区域和聚类标识[12]。模块化值(Modularity)为0.527 3,表示网络的聚类结果良好。
2.1.2 关键词突现分析 关键词突现强度越大,越能代表在一段时间内,该关键词为领域的活跃术语或新兴知识。从
2.1.3 作者-机构合作网络分析 科研合作网络能够全面客观了解不同学术机构、不同作者之间针对同一领域研究热点的合作关系,网络中的节点大小反映论文数量。从
图4 外文文献学术机构合作网络图
Fig.4 Cooperation network diagram of foreign literature academic institutions
表2 2012—2022年国外临床联合用药安全性评价与决策风险管理文献作者-机构发文量
Tab.2 Number of papers published by authors-institutions on safety evaluation and decision-making risk management of clinical drug combination abroad from 2012 to 2022
2.2.1 关键词共现与聚类分析 结果见
表3
2012—2022年国内临床联合用药安全性评价与决策风险管理文献关键词(
Tab.3
Key words of literatures on safety evaluation and decision-making risk management of clinical drug combination in China from 2012 to 2022 (
2.2.2 关键词突现分析 从
2.2.3 作者-机构合作网络分析 结果见
图9 中文文献学术机构合作网络图
Fig.9 Cooperation network diagram of Chinese literature academic institutions
表4 2012—2022年国内临床联合用药安全性评价与决策风险管理文献作者-机构发文量
Tab.4 Number of papers published by authors and institutions on safety evaluation and decision-making risk management of clinical drug combination in China from 2012 to 2022
用药方案中每添加一种药物,由DDI导致的药物不良事件风险也随之增加,在临床实践中大大加剧医生合理处方的难度[13]。上市前药物随机临床试验大多只研究单个药物的安全性和有效性,忽视联合用药的风险[14]。据文献报道,在未预期的药物不良事件中由DDI引起的比例约为30%[15]。为此,国外不少学者将研究方向转向上市后药物再评价,从自由文本数据、商用的DDI数据库和大量药物警戒数据筛选联合用药的风险信号。SEGURA-BEDMAR等[16]基于自然语言处理(natural language processing,NLP)技术从生物医学文献中提取DDI信息。通常商用的DDI数据库包含大量药物化学结构以及药物特性数据,大量研究据此来预测联合用药可能引起的药物不良事件。VILAR等[17]使用谷本系数衡量不同药物分子指纹的结构相似性,提示相似结构的药物可能会引起相似的不良反应/事件;此外还有研究基于2种药物共享某类靶向蛋白时往往会产生相互作用的原理,分析药物靶向蛋白网络来预测不良反应/事件[18]。使用药物警戒数据作上市后安全性研究主要分为3个方面:①基于比例失衡法设计完善的技术体系检测联合用药不良事件信号[19];②基于关联规则或频繁模式探索和评估联合用药与发生不良事件之间的关联性[20];③基于概率图和药物-不良事件因果关系来评估联合用药风险性[21]。
综合CiteSpace知识图谱结果与现有研究可发现,临床联合用药安全性评价与风险管理正是当前药品安全领域的重要话题。国外学者在联合用药潜在的药物-药物相互作用以及安全性评价领域深耕良久,尤其是利用目前较为成熟的药物-药物相互作用数据库和日臻完善的药物警戒数据库进行随机对照临床试验以及联合用药风险信号筛选两方面。但这些数据库数据来源渠道多元、类型多样、表现形式不统一、存在缺失值,以及试验偏重于在设定条件下的药物-药物相互作用研究,未能从真实世界临床病例数据资源角度,开展多中心临床联合用药风险评价。此外,外文文献多仅以药物为核心概念开展研究,与国内研究常同时探讨联合用药安全性与相应管理措施有所不同。
随着与联合用药相关的药物不良事件报道越来越多,提高联合用药安全性的研究成为近年来国内药事管理领域研究的热点之一。耿洪娇等[22]运用Tabu搜索算法对真实世界治疗脑出血患者的联合用药情况进行群组模块分析,筛选归纳针对不同病情患者的目标药物联合中西药物用药方案供临床医师参考。马洁等[23]利用数据挖掘的Apriori算法,通过患者身体基础数据分析预测不同高危人群中发生药物不良反应与高危因素间的关联关系,结果显示目标药物发生不良反应风险与联合用药具有强关联度。金鑫瑶等[24]基于以往多中心、大样本医院集中监测研究数据对目标药物使用情况和患者信息进行分析,发现80.67%患者在使用目标药物时联合使用其他注射剂,提出联合用药需慎重。也有研究者基于省/市药品不良反应中心的不良反应报告数据库[25]和医院病例数据[26]的研究发现联合用药与单独用药的不良反应发生率具有显著性差异,且随联合用药品种的增多,药物不良反应发生率也随之增加。近年来,我国学者针对联合用药的临床有效性与安全性进行大量研究,提出了多种经随机对照实验证明行之有效的临床联合用药方案。不良反应作为药品安全性的重要表征,常在其中用于评价联合用药方案实验组与对照组的安全性[27]。此外,我国许多医院也通过建设处方前置审核系统保障临床合理用药,系统的“两审两拦截”工作模式可为处方审核提供必要的信息和保障,提高了联合用药合理用药水平和处方质量[28,29,30,31]。
综上所述,我国学者通过数据挖掘或统计分析方法对真实世界用药数据进行多角度探析,逐步显现了联合用药安全性问题的严重性与复杂性。处方审核系统的应用探索也已经取得了较为良好的成效。但当前关于我国联合用药安全性与风险管理的研究仍有许多不足之处:①针对联合用药安全性的探索不够深入,远不足以满足临床的真实需求;②处方审核系统和临床联合用药方案的探索难以综合临床与管理两方面要素,系统的高成本也使得许多基层医院望而却步;③存在重安全性评价而轻风险管理的现象,且联合用药安全性问题常常仅作为用药安全性分析的一部分,针对联合用药安全性问题所提出的临床建议和管理措施难以形成体系;④联合用药决策仍然多依靠于医生和药师的临床经验,缺少充分的真实世界证据支撑,难以提出具有可操作性和科学性的临床联合用药决策风险管理模式。
联合用药安全是药物警戒工作的重点关注内容,也是难点、堵点,当前其研究热点已逐渐从关注具体药物、病种以及临床药动、药效试验转移拓展至关注患者本身与联合用药的系统性上,其安全性评价也亟待从新药临床试验延续至上市后评价的药品全生命周期,为临床联合用药提供更多更全面的安全性证据,以降低患者用药风险。此外,囿于联合用药内在机制复杂,药物-药物相互作用纷繁,当前医疗机构常用的管理工具如处方审核系统、医院信息系统、药品不良反应报告系统等远远不能满足临床实践需求,联合用药安全性管理需多方数据来引导决策。因此,在临床真实世界中探讨联合用药使用合理性、风险决策科学性,利用真实世界数据对联合用药安全性进行评估、形成真实世界证据,落实风险评估与风险管理机制,有效联合药品监管部门、医药企业、高校与科研院所,合力保障临床合理用药,将会是实现“以人民健康为中心”愿景的有效途径。
本文在研究方法上存在着一定的局限性。首先,本文以Web of Science核心合集以及中国知网(CNKI)数据库为检索源,检索所得文献可以涵盖绝大多数相关文献,但由于未纳入其他数据库,可能存在部分有关文献遗漏的问题;其次,文献检索所使用检索词以“安全性”和“风险管理”为核心,针对临床联合用药其他相关检索词扩充不足,也可能导致部分相关文献未纳入,进而导致文中研究热点与前沿的呈现存在一定的偏移。
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There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.
[本文引用:1]
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Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.
DOI:10.1136/amiajnl-2013-001612
PMID:24158091
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Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug--ADR associations, but have yet to reach their full potential for drug-drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug-drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug-drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug-drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug-drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.
DOI:10.1002/sim.3247
PMID:18344185
[本文引用:1]
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| [21] |
Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration's adverse event reporting system.Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification.Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR.Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event.Copyright © 2017 Elsevier B.V. All rights reserved.
DOI:S0933-3657(16)30543-7
PMID:28363289
[本文引用:1]
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