欧盟、美国和日本的药物警戒信号管理体系比较研究

目的:比较欧盟、美国和日本的药物警戒信号管理体系,为建立和完善我国药物警戒信号管理体系提供参考.方法:采用文献研究分析法,系统对比欧盟、美国和日本的相关监管机构在药物警戒信号定义、来源、检测方法和管理流程等方面的异同,并对我国药物警戒管理工作提出建议.结果与结论:欧盟、美国和日本的监管机构对于信号的定义并不统一,欧盟药...     

Signal detection of methylphenidate by comparing a spontaneous reporting database with a claims database

Data mining is critical for signal detection in pharmacovigilance systems. In this study, we compared signals between spontaneous reporting data and health insurance claims data for a socially issued drug, methylphenidate. We implemented data-mining tools for signal detection in both databases: Reporting Odds Ratios (ROR), Proportional Reporting Ratios (PRR), Chi-squared test, and Information Component (IC), in addition to a Relative Risk (RR) tool in the claims database. The claims database generated 15, 15, 36, 1, and 1 adverse drug reactions (ADRs) by ROR, PRR, chi-square, IC, and RR, respectively. The World Health Organization (WHO) spontaneous database generated 91, 91, 137, and 96 ADRs by ROR, PRR, chi-square, and IC, respectively. We found seven potential matching associations from the claims and WHO databases, but only one of them was present in the Korean spontaneous reporting database. In Korea, spontaneous reporting is still underreported and there is a small amount of data for Koreans. Signal comparison between the claims and WHO databases can provide additional regulatory insight.     

Application of data mining techniques in pharmacovigilance

AIMS: To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance. METHODS: A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse drug reactions or pharmacovigilance in medical databases. RESULTS: ADEs are common and result in significant mortality, and despite existing systems drugs have been withdrawn due to ADEs many years after licensing. Knowledge discovery in databases (KDD) is a technique which may be used to detect potential ADEs more efficiently. KDD involves the selection of data variables and databases, data preprocessing, data mining and data interpretation and utilization. Data mining encompasses a number of statistical techniques including cluster analysis, link analysis, deviation detection and disproportionality assessment which can be utilized to determine the presence of and to assess the strength of ADE signals. Currently the only data mining methods to be used in pharmacovigilance are those of disproportionality, such as the Proportional Reporting Ratio and Information Component, which have been used to analyse the UK Yellow Card Scheme spontaneous reporting database and the WHO Uppsala Monitoring Centre database. The association of pericarditis with practolol but not with other beta-blockers, the association of captopril and other angiotensin-converting enzymes with cough, and the association of terfenadine with heart rate and rhythm disorders could be identified by mining the WHO database. CONCLUSION: In view of the importance of ADEs and the development of massive data storage systems and powerful computer systems, the use of data mining techniques in knowledge discovery in medical databases is likely to be of increasing importance in the process of pharmacovigilance as they are likely to be able to detect signals earlier than using current methods.     

Reports of hyperkalemia after publication of RALES--a pharmacovigilance study

PURPOSE: A population-based study and anecdotal reports have indicated that the publication of the Randomized Aldactone Evaluation Study (RALES) was associated with not merely a broader use of spironolactone in the treatment of heart failure, but also with a coinciding sharp increase in hyperkalemia-associated morbidity/mortality in patients also being treated with ACE-inhibitors. Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in hopes of obtaining early warnings/additional insights into post-licensure safety data. We applied two DMAs (i.e. multi-item gamma Poisson shrinker [MGPS] and proportional reporting ratios [PRRs]) to spontaneous reporting system (SRS) data to determine if these DMAs could have provided an earlier indication of a possible hyperkalemia safety issue. METHODS: MGPS and PRRs were retrospectively applied to US FDA-AERS, an SRS database. Year-by-year analysis and analysis of increasing cumulative time intervals were performed on cases in which both spironolactone and hyperkalemia and possibly related cardiac events had been reported. RESULTS: Neither of the DMAs initially provided a compelling signal of disproportionate reporting (SDR) for hyperkalemia after publication of RALES. However, using events consistent with clinical sequelae of hyperkalemia (e.g,. sudden death), SDRs were identified with PRRs. CONCLUSIONS: The quality and usefulness of data mining analysis is highly situation dependent and may vary with the knowledge and experience of the drug safety reviewer. Our analysis suggests that contemporary DMAs may have significant limitations in detecting increased frequency of labeled events in real-life prospective pharmacovigilance. There is a paucity of research in this area and we recommend further research for new approaches to detecting increased frequency of labeled events.     

药品不良反应数据挖掘技术在药物警戒的应用

随着信息技术的发展,医药电子数据海量增长,药品不良事件报告大幅增加,给药物警戒研究带来了巨大的挑战。而数据挖掘技术可以自动从真实世界数据中撷取药品不良反应风险信号。因此,对海量不良事件报告数据进行高效数据挖掘是实现药品不良反应自动检测的必要措施。本研究通过介绍当前主要的大型药品不良事件报告数据库和相关数据挖掘方法,对药品不良反应数据挖掘技术在药物警戒中的应用及其局限性进行综述,为药物警戒相关机构和科研人员提供参考。     

Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions.

The primary aim of spontaneous reporting systems (SRSs) is the timely detection of unknown adverse drug reactions (ADRs), or signal detection. Generally this is carried out by a systematic manual review of every report sent to an SRS. Statistical analysis of the data sets of an SRS, or quantitative signal detection, can provide additional information concerning a possible relationship between a drug and an ADR. We describe the role of quantitative signal detection and the way it is applied at the Netherlands Pharmacovigilance Centre Lareb. Results of the statistical analysis are implemented in the traditional case-by-case analysis. In addition, for data-mining purposes, a list of associations of ADRs and suspected drugs that are disproportionally present in the database is periodically generated. Finally, quantitative signal generation can be used to study more complex relationships, such as drug-drug interactions and syndromes. The results of quantitative signal detection should be considered as an additional source of information, complementary to the traditional analysis. Techniques for the detection of drug interactions and syndromes offer a new challenge for pharmacovigilance in the near future.     

Detection of spironolactone-associated hyperkalaemia following the Randomized Aldactone Evaluation Study (RALES).

INTRODUCTION: A population-based analysis has suggested that the publication of the RALES (Randomized Aldactone Evaluation Study) in late 1999 was associated with both the wider use of spironolactone to treat heart failure and a corresponding increase in hyperkalaemia-associated morbidity and mortality in patients also being treated with ACE inhibitors. OBJECTIVES: To gain further insight into the reporting of spironolactone-associated hyperkalaemia in an independent dataset by analysing the spontaneous reporting experience in relation to the publication of RALES, and to determine whether the implementation of a commonly used data mining algorithm (DMA) might have directed the attention of safety reviewers to the spironolactone/hyperkalaemia association in advance of epidemiological findings. METHODS: We calculated the reporting rate of spironolactone-associated hyperkalaemia per 1,000 reports per year from 1970 through to the end of 2005 by identifying relevant cases in the US FDA Adverse Event Reporting System. We did this for reports of spironolactone-associated hyperkalaemia (where spironolactone was listed as a suspect drug) and according to whether the reports listed an ACE inhibitor as a co-suspect or concomitant medication. A further statistical analysis of the overall reporting of spironolactone (suspect drug)-associated hyperkalaemia was also performed. We also performed 3-dimensional (3-D; drug-drug-event) disproportionality analyses using a DMA known as the multi-item gamma-Poisson shrinker, which allows the calculation and display of a 3-D disproportionality metric known as the 'interaction signal score' (INTSS). This metric is a measure of the strength of a higher order reporting relationship of a triplet (i.e. drug-drug-event) association above and beyond what would be expected from the largest disproportionalities associated with the individual 2-way associations. RESULTS: Visual inspection of a graph of the reporting frequency of spironolactone (suspect drug)-associated hyperkalaemia per 1,000 reports was highly suggestive of a change point. The t-test on the arcsine-transformed data showed a significant difference in reporting of spironolactone-hyperkalaemia combination through 1999 compared with 2000 onwards (p < 0.001). When examining the reporting time trends according to the presence or absence of an ACE inhibitor, the change point seemed to be mostly attributable to an increase in the number of spironolactone (suspect drug)-associated hyperkalaemia reports with ACE inhibitors listed as a co-suspect drug. No obvious change points in INTSSs for spironolactone-ACE inhibitor-hyperkalaemia reports were observed. DISCUSSION: Although we could not pinpoint the relative contribution of many possible artifacts in the reporting process, as well as increased drug exposure, increased adverse event incidence and/or a change in patient monitoring practices, to our findings, we observed a notable change in reporting frequency of spironolactone-associated hyperkalaemia in temporal proximity to the publication of RALES. Evidence of this was provided by a trend analysis depicted in a simple graph that was supported by statistical analysis. The observed trend was in large part due to increased reporting of spironolactone-associated hyperkalaemia with reported co-medication with ACE inhibitors. CONCLUSION: These findings are consistent with those originally reported in an epidemiological analysis. In this retrospective exercise, a simple graph was more illuminating than more complex data mining analyses.     

Perspectives on the use of data mining in pharmaco-vigilance.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided.Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.     

Innovations for the future of pharmacovigilance.

Post-marketing pharmacovigilance involves the review and management of safety information from many sources. Among these sources, spontaneous adverse event reporting systems are among the most challenging and resource-intensive to manage. Traditionally, efforts to monitor spontaneous adverse event reporting systems have focused on review of individual case reports. The science of pharmacovigilance could be enhanced with the availability of systems-based tools that facilitate analysis of aggregate data for purposes of signal detection, signal evaluation and knowledge management. GlaxoSmithKline (GSK) recently implemented Online Signal Management (OSM) as a data-driven framework for managing the pharmacovigilance of marketed products. This pioneering work builds upon the strong history GSK has of innovation in this area. OSM is a software application co-developed by GSK and Lincoln Technologies that integrates traditional pharmacovigilance methods with modern quantitative statistical methods and data visualisation tools. OSM enables the rapid identification of trends from the individual adverse event reports received by GSK. OSM also provides knowledge-management tools to ensure the successful tracking of emerging safety issues. GSK has developed standard procedures and 'best practices' around the use of OSM to ensure the systematic evaluation of complex safety datasets. In summary, the implementation of OSM provides new tools and efficient processes to advance the science of pharmacovigilance.     

Safety related drug-labelling changes: findings from two data mining algorithms.

INTRODUCTION: With increasing volumes of postmarketing safety surveillance data, data mining algorithms (DMAs) have been developed to search large spontaneous reporting system (SRS) databases for disproportional statistical dependencies between drugs and events. A crucial question is the proper deployment of such techniques within the universe of methods historically used for signal detection. One question of interest is comparative performance of algorithms based on simple forms of disproportionality analysis versus those incorporating Bayesian modelling. A potential benefit of Bayesian methods is a reduced volume of signals, including false-positive signals. OBJECTIVE: To compare performance of two well described DMAs (proportional reporting ratios [PRRs] and an empirical Bayesian algorithm known as multi-item gamma Poisson shrinker [MGPS]) using commonly recommended thresholds on a diverse data set of adverse events that triggered drug labelling changes. METHODS: PRRs and MGPS were retrospectively applied to a diverse sample of drug-event combinations (DECs) identified on a government Internet site for a 7-month period. Metrics for this comparative analysis included the number and proportion of these DECs that generated signals of disproportionate reporting with PRRs, MGPS, both or neither method, differential timing of signal generation between the two methods, and clinical nature of events that generated signals with only one, both or neither method. RESULTS: There were 136 relevant DECs that triggered safety-related labelling changes for 39 drugs during a 7-month period. PRRs generated a signal of disproportionate reporting with almost twice as many DECs as MGPS (77 vs 40). No DECs were flagged by MGPS only. PRRs highlighted DECs in advance of MGPS (1-15 years) and a label change (1-30 years). For 59 DECs, there was no signal with either DMA. DECs generating signals of disproportionate reporting with only PRRs were both medically serious and non-serious. DISCUSSION/CONCLUSION: In most instances in which a DEC generated a signal of disproportionate reporting with both DMAs (almost twice as many with PRRs), the signal was generated using PRRs in advance of MGPS. No medically important events were signalled only by MGPS. It is likely that the incremental utility of DMAs are highly situation-dependent. It is clear, however, that the volume of signals generated by itself is an inadequate criterion for comparison and that clinical nature of signalled events and differential timing of signals needs to be considered. Accepting commonly recommended threshold criteria for DMAs examined in this study as universal benchmarks for signal detection is not justified.     

不同信号检测方法在我国药品不良反应自发呈报系统中的应用

目前,国家药品不良反应监测中心的自发呈报系统收集到的药品不良反应数据量已经具备筛选信号的规模。通过对广东、上海、江苏3个省级中心药品不良反应数据库采用的不同信号检测方法进行比较和分析,旨在为我国药品不良反应信号检测及预警系统的建设提供参考。     

Detecting drug–drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs

OBJECTIVE: Drug-drug interactions are relatively rarely reported to spontaneous reporting systems (SRSs) for adverse drug reactions. For this reason, the traditional approach for analysing SRS has major limitations for the detection of drug-drug interactions. We developed a method that may enable signalling of these possible interactions, which are often not explicitly reported, utilising reports of adverse drug reactions in data sets of SRS. As an example, the influence of concomitant use of diuretics and non-steroidal anti-inflammatory drugs (NSAIDs) on symptoms indicating a decreased efficacy of diuretics was examined using reports received by the Netherlands Pharmacovigilance Foundation Lareb. METHODS: Reports received between 1 January 1990 and 1 January 1999 of patients older than 50 years were included in the study. Cases were defined as reports with symptoms indicating a decreased efficacy of diuretics, non-cases as all other reports. Exposure categories were the use of NSAIDs or diuretics versus the use of neither of these drugs. The influence of the combined use of both drugs was examined using logistic regression analysis. RESULTS: The odds ratio of the statistical interaction term of the combined use of both drugs was increased [adjusted odds ratio 2.0, 95% confidence interval (CI) 1.1-3.7], which may indicate an enhanced effect of concomitant drug use. CONCLUSION: The findings illustrate that spontaneous reporting systems have a potential for signal detection and the analysis of possible drug-drug interactions. The method described may enable a more active approach in the detection of drug-drug interactions after marketing.     

基于FAERS数据库的芳香化酶抑制剂不良事件信号挖掘研究

目的 对芳香化酶抑制剂(aromatase inhibitors,AIs)的不良事件(adverse drug event,ADE)信号进行挖掘分析,为临床安全用药提供参考。方法 从美国FDA不良事件报告系统提取2015年第1季度—2021年第2季度共26个季度的与AIs相关的ADE报告,数据规范化后,利用报告比值比法和比例报告比值法对ADE报告进行数据筛选与分析。结果 共获得以AIs为首要怀疑药物的ADE报告共16 501份,筛选后得到ADE信号1 150个(依西美坦209个,来曲唑377个,阿那曲唑564个),累及23个系统,主要涉及各种肌肉骨骼及结缔组织疾病和全身性疾病及给药部位各种反应等。报告数前50位的ADE中未在说明书中出现的信号有47个(依西美坦15个,来曲唑11个,阿那曲唑21个),新信号主要集中在血液、心血管和呼吸系统。结论 本研究挖掘出的常见ADE信号和其累及系统与说明书一致,但3种AIs的ADE具有差异性且发现新信号,可为临床合理用药提供一定的参考。     

Signalling possible drug–drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole

AIMS: In spontaneous adverse drug reaction reporting systems, there is a growing need for methods facilitating the automated detection of signals concerning possible adverse drug reactions. In addition, special attention is needed for the detection of adverse drug reactions resulting from possible drug-drug interactions. We describe a method for detecting possible drug-drug interactions using logistic regression analysis to calculate ADR reporting odds ratios. METHODS: To illustrate this method, we analysed the adverse drug reaction 'delayed withdrawal bleeding' resulting from a possible interaction between itraconazole and oral contraceptives in reports received by the Netherlands Pharmacovigilance Foundation LAREB between 1991 and 1998. RESULTS: In total 5,503 reports were included in the study. The odds ratio, adjusted for year of reporting, age and source of the reports, for a delayed withdrawal bleeding in women who used both drugs concomitantly compared with women who used neither oral contraceptives, nor itraconazole, was 85 (95% CI: 32-230). CONCLUSIONS: Since spontaneous reporting systems can only generate signals concerning possible relationships, this association needs to be analysed by other methods in more detail in order to determine the real strength of the relationship. This approach might be a promising tool for the development of procedures for automated detection of possible drug-drug interactions in spontaneous reporting systems.     

Spontaneous adverse drug reaction reporting by community pharmacists: preparedness and barriers

BackgroundAdverse drug reactions (ADRs) are undesired, unintended responses to drugs, and are significantly underreported. Pharmacists are drug experts recognized as custodians of drug safety, who are expected to be prepared for and knowledgeable about ADR reporting.ObjectivesTo identify Egyptian community pharmacists’ preparedness for and perceived barriers to spontaneous ADR reporting.MethodsThis cross-sectional study recruited a sample of community pharmacists across Egypt, who were invited to complete a self-administrated questionnaire during April 2020.ResultsA total of 923 pharmacists across Egypt responded to the questionnaire. Most pharmacists were knowledgeable about the definition of ADRs (93.9 %) and indicated they felt reporting ADRs benefits the patients (82.2%). Despite recognizing their public health value, only a small percentage of participants conveyed familiarity with the reporting process for both paper (19.2%) and electronic (30.4%) forms, indeed 56.6% of participants did not remember what the ADR report form looked like. Moreover, 75.4% of respondents said they felt that community pharmacies are not the right place for reporting, with 49% suggesting that reporting was the responsibility of physicians. However, only 32.1% reported having insufficient time being a barrier to ADR reporting.ConclusionsCommunity pharmacists in Egypt are not well prepared for spontaneous ADR reporting due to a lack of knowledge about the formal process and not acknowledging their responsibility, although time was not a major barrier. Therefore, this highlights a clear opportunity for improvement likely involving targeted education.     

地市级药品安全监测数据综合管理平台构建研究

目的：通过构建药品安全监测数据综合管理平台,实现对药品安全监测数据的管理与分析利用。方法：运用信息化手段,以国家药品不良反应监测系统为依托,将数据采集检索、报告质量管理、数据统计分析、实时风险预警、风险信号挖掘五大功能融为一体,实现一站式服务。以“机器换人”为导向,将人工难以实现的计算任务转为机器计算功能,并引入可视化模型用于数据分析和信号挖掘,体现基于数据的价值创造。结果：利用数据综合管理平台,可有效管理和分析全市药品安全监测信息,极大提高了监测效率及风险预警能力。结论：数据综合管理平台体现了药品安全的智慧监测,具有较好的应用前景。     

口服抗糖尿病药物不良反应信号及其相关因素分析

目的研究口服抗糖尿病药物不良反应特征,并提供相应临床合理用药的依据。方法通过上海市不良反应监测中心收集2006至2010年有关口服抗糖尿病药物不良反应的数据,使用描述性统计,logistic回归模型及不相称性,对不良反应发生的特征、风险因子和相关因素进行分析。结果共获得812例口服抗糖尿病药物的不良反应报告。结果显示老年人和女性占不良反应发生的多数;不良反应级别以一般为主;2009至2010年间作为药品说明书中未记载的新的不良反应有上升的趋势;双胍类及磺酰脲类（SU）不良反应最为突出;logistic回归模型显示女性、日服用药频次多、单一用药是胃肠道不良反应的危险因素。体质量是皮肤损害的危险因素,联合用药、患者年龄大是SU致低血糖的危险因素。不相称性研究同时也得到一些口服抗糖尿病药物致不良反应的信号。结论口服抗糖尿病药物不良反应累及系统器官广泛,像性别、年龄、体质量、用药频次、是否联合用药都会在一定程度上影响不良反应的发生。故口服抗糖尿病药物在临床使用时应加强不良反应监测,重视和控制其风险因子,以期更合理安全地用药。     

Postmarketing surveillance of potentially fatal reactions to oncology drugs: potential utility of two signal-detection algorithms

PURPOSE: Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with cancer drugs might have been identified earlier than with traditional methods. METHODS: Screening algorithms used for analysis were the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs). Data mining was performed on data from the FDA AERS database. When a signal was identified, it was compared with that in the year in which the event was added to package insert and/or the year a "case series" was published. A recent publication summarizing the time of dissemination of information on potentially fatal AEs to cancer drugs provided the data set for analysis. RESULTS: The peer-reviewed published analysis contained 21 drugs and 26 drug-event combinations (DECs) that were considered sufficiently specific for data mining. Twenty-four of the DECs generated a signal of disproportionate reporting with PRRs (6 at 1 year and 16 from 2 years to 18 years prior to either a published "case series" or a package insert change) and 20 with MGPS (3 at 1 year and 11 from 2 years to 16 years prior to either a published "case series" or a package insert change). Two DECs did not signal with either DMA. CONCLUSION: At least one commonly cited DMA generated a signal of disproportionate reporting for 24 of 26 DECs for selected cancer drugs. For 16 DECs, one could conclude that a signal was generated well in advance (> or =2 years) of standard techniques in use with at least one DMA. DMAs might be useful in supplementing traditional surveillance strategies with oncology drugs and other drugs with similar features. (i.e., drugs that may be approved on an accelerated basis, are known to have serious toxicity, are administered to patients with substantial and complicated comorbid illness, are not available to the general medical community, and may have a high frequency of "off-label" use).     

Public Perspectives of Using Social Media Data to Improve Adverse Drug Reaction Reporting: A Mixed-Methods Study

Introduction

Information on suspected adverse drug reactions (ADRs) voluntarily submitted by patients can be a valuable source of information for improving drug safety; however, public awareness of reporting mechanisms remains low. Whilst methods to automatically detect ADR mentions from social media posts using text mining techniques have been proposed to improve reporting rates, it is unclear how acceptable these would be to social media users.

Objective

The objective of this study was to explore public opinion about using automated methods to detect and report mentions of ADRs on social media to enhance pharmacovigilance efforts.

Methods

Users of the online health discussion forum HealthUnlocked participated in an online survey (N = 1359) about experiences with ADRs, knowledge of pharmacovigilance methods, and opinions about using automated data mining methods to detect and report ADRs. To further explore responses, five qualitative focus groups were conducted with 20 social media users with long-term health conditions.

Results

Participant responses indicated a low awareness of pharmacovigilance methods and ADR reporting. They showed a strong willingness to share health-related social media data about ADRs with researchers and regulators, but were cautious about automated text mining methods of detecting and reporting ADRs.

Conclusions

Social media users value public-facing pharmacovigilance schemes, even if they do not understand the current framework of pharmacovigilance within the UK. Ongoing engagement with users is essential to understand views, share knowledge and respect users’ privacy expectations to optimise future ADR reporting from online health communities.