Implemented Data Mining and Signal Management Systems On

170 Current Drug Safety, 2012, 7, 170-175 Implemented Data Mining and Signal Management Systems on Spontaneous Reporting Systems’ Databases and their Availability to the Scientific Community - A Systematic Review Luís Miguel de Almeida Vieira Lima*,1, Nuno Gonçalo Sales Craveiro Nunes2,3,8, Pedro Gonçalo Pires da Silva Dias4,5,6,7 and Francisco Jorge Batel Marques2,8 1University of Coimbra, Coimbra, Portugal 2AIBILI – Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal 3Health Sciences Department, University of Aveiro, Aveiro, Portugal 4Beira Interior University, Covilhã, Portugal 5Polytechnic Institute of Leiria, Leiria, Portugal 6Center of Investigation in Human Motricity, Leiria, Portugal 7Research Center in Sports, Health Sciences and Human Development, Vila Real, Portugal 8School of Pharmacy, University of Coimbra, Coimbra, Portugal Abstract: Adverse drug reactions’ spontaneous reporting systems are an important element in worldwide pharmacovigilance, gathering potentially useful information for post-marketing drug safety surveillance. Data mining and signal management systems, providing the capability of reading and interpreting these systems’ raw data (data that has not been subjected to processing or any other manipulation), improve its analysis process. In order for this analysis to be possible, both data mining and signal management systems and raw data should be available to researchers and the scientific community. The purpose of this work was to provide an overview of the spontaneous reporting systems databases reported in literature as having implemented a data mining and signal management system and the implementation itself, evidencing their availability to researchers. A systematic review was carried out, concluding that they are freely provided to researchers within institutions responsible for maintaining the spontaneous reporting systems, but not to most researchers within the scientific community. Keywords: Data mining, information system, pharmacovigilance, post-marketing safety surveillance, scientific community, signal detection software, spontaneous reporting system. INTRODUCTION agencies, market authorization holders (MAH) and the scientific community [2-11] have struggled to develop and Spontaneous Reporting System (SRS) databases, enhance data mining and signal management systems containing suspected adverse drug reactions (ADR) reports, (DMSs), systematizing the process of analysing both are an important element in the worldwide previously existing and new spontaneous ADR reports. pharmacovigilance [1]. Despite intrinsic limitations [1-11], DMSs grew into an essential tool for an efficient detection of the amount of potentially useful information that they potential signals [11]. contain makes the process of searching for relevant cases among the stored information an important challenge for The objective of the present work is to compile drug safety researchers. These cases are often named as information that identifies and describes the SRS databases signals, defined by the World Health Organization as reported in the literature as having implemented a DMS and “reported information on a possible causal relationship the implementation characteristics themself, giving evidence between an adverse event and a drug, of which the of their availability to researchers within the scientific relationship is unknown or incompletely documented community. previously” [2], being its searching process known as signalling or signal detection. MATERIALS AND METHODS Due to the increasingly large number of spontaneous A Medline search from January 1995 to December 2010 reports stored in SRS databases, celerity and efficiency in the was carried out using the search strategy defined in Table 1. process of signalling for relevant cases are being compromised. To overcome the problem, drug regulatory All 210 titles were examined by the first author and reviewed independently by the second author. Relevant abstracts were extracted in order to identify potentially *Address correspondence to this author at the University of Coimbra, relevant studies. Full papers of potentially relevant studies Coimbra, Portugal; Tel: 00351912570205; were obtained and assessed according to the inclusion and E-mail: [email protected] exclusion criteria. Studies were selected based on their 1574-8863/12 $58.00+.00 © 2012 Bentham Science Publishers Adverse Drug Reactions Spontaneous Reporting Systems Current Drug Safety, 2012, Vol. 7, No. 2 171 Table 1. MedLine Search Strategy Information Source (Search Date) Step Number Search Terms Results #1 Pharmacovigilance 2030 #2 Post marketing surveillance 1108 #3 Adverse drug reaction 340832 #4 Adverse event 24183 #5 #1 OR #2 OR #3 OR #4 363873 #6 Signal detection 33770 #7 Data mining 7575 Pubmed (25/07/2011) #8 Automated signaling 816 #9 #6 OR #7 OR #8 42019 #10 Software 130058 #11 Information System 250967 #12 #10 OR #11 347629 #13 #5 AND #9 AND #12 212 #14 Limits: Publication Date from 1995/01/01 210 referral of DMSs implemented on SRS databases [1-14]. • Database Only articles published in English were considered to the • Data origin present review. Bibliographies of included studies were searched for further relevant studies [15-17]. In complement, • Report submission (collecting data process) institutions were contacted and their websites were accessed, • Implemented DMS characterization: as a way to extend our information sources [18-28]. The selection process is illustrated in Fig. (1). o System identification – used DMS software Data were extracted by the first author using a o Signal detection methods - methods used to identify standardized extraction table and checked independently by drug-event associations that should be signalized for the second author. All data were review by other authors. further investigation Disagreement was resolved by discussion. The extraction o Data presentation - way the results of the previous table took into account the following variables: phase are displayed in order to facilitate its analysis for risk assessment by researchers or technicians. 210 papers screened 112 papers excluded based on title 98 papers identified for abstracts review 57 papers excluded based on abstract review 41 papers identified for full paper review 27 papers excluded following perusal of full paper 14 papers met inclusion criteria Fig. (1). Identification of published evidence for review. 172 Current Drug Safety, 2012, Vol. 7, No. 2 Lima et al. o Public availability Food and Drug Administration’s Adverse Event Reporting System receives data from health care • Data availability to the scientific community. professionals, consumers, MAH and other residual sources, Finally, a description was performed for each SRS such as literature reports. From 1969 to 2002, 60% of the database identified in the review and each DMS identified reports received were from health care professionals, 29% has having been implemented on the SRS databases. from consumers and the remaining from MAH and others [15]. Report submission can be distinguished according to its RESULTS nature: voluntary, performed essentially by health care professionals and consumers, which occurs through This review identified seven SRS databases. Six of them MedWatch, a web-based reporting tool that allows users to are property of regulatory or drug safety monitoring fill and send an Online Voluntary Reporting Form; and institutions and one is property of GlaxoSmithKline, a mandatory, performed by MAH that receive direct reports multinational pharmaceutical company. from health care professionals and consumers and are Table 2 presents a summary of their characteristics. obliged to further report them to the Food and Drug Administration using a specific formulary sent by mail or by an electronic process [19]. This system’s raw reported data UNITES STATES FOOD AND DRUG ADMINISTRAT- are freely available and paid reports of specific drug-event ION ADVERSE EVENTS REPORTING SYSTEM association analysis are available on demand [14]. The Food and Drug Administration’s Adverse Event Reporting System is a large database of voluntarily WORLD HEALTH ORGANIZATION INDIVIDUAL submitted adverse events, representing the most important CASE SAFETY REPORTS DATABASE SYSTEM resource to the study and identification of adverse reactions (VIGIBASE) to drugs and biological products in the United States [15]. It VigiBase is a large and global database of Individual Case is in practice since October 1997, when it replaced Safety Reports, maintained and developed by the Uppsala Spontaneous Reporting System, an electronic database that Monitoring Centre, in Sweden, that conjugates reports from the contained reports since 1969 [15]. Currently contains more national centres that integrate the World Health Organization than four million reports, considering data from previous International Drug Monitoring Programme. Currently, there are Spontaneous Reporting System [18]. more than 80 countries submitting their national reports to VigiBase. The top-5 contributors are the United States, the Table 2. Identified SRS Databases Databases Data Origin Report Submission Data Availability United States (consumers, health Paper-based forms Food and Drug Administration’s Raw data available care professionals MEDWatch (electronic reporting Adverse Event Reporting System Reports available on demand and MAH) tools) World Health Organization E2B format reports Individual Case Safety Reports VigiFlow (web-based reporting World Health

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