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      Longitudinal medical records as a complement to routine drug safety signal analysis†

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          Abstract

          Purpose

          To explore whether and how longitudinal medical records could be used as a source of reference in the early phases of signal detection and analysis of novel adverse drug reactions (ADRs) in a global pharmacovigilance database.

          Methods

          Drug and ADR combinations from the routine signal detection process of VigiBase® in 2011 were matched to combinations in The Health Improvement Network (THIN). The number and type of drugs and ADRs from the data sets were investigated. For unlabelled combinations, graphical display of longitudinal event patterns (chronographs) in THIN was inspected to determine if the pattern supported the VigiBase combination.

          Results

          Of 458 combinations in the VigiBase data set, 190 matched to corresponding combinations in THIN (after excluding drugs with less than 100 prescriptions in THIN). Eighteen percent of the VigiBase and 9% of the matched THIN combinations referred to new drugs reported with serious reactions. Of the 112 unlabelled combinations matched to THIN, 52 chronographs were inconclusive mainly because of lack of data; 34 lacked any outstanding pattern around the time of prescription; 24 had an elevation of events in the pre‐prescription period, hence weakened the suspicion of a drug relationship; two had an elevated pattern of events exclusively in the post‐prescription period that, after review of individual patient histories, did not support an association.

          Conclusions

          Longitudinal medical records were useful in understanding the clinical context around a drug and suspected ADR combination and the probability of a causal relationship. A drawback was the paucity of data for newly marketed drugs with serious reactions. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.

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          Most cited references18

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          Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates.

          The degree of generalisability of patient databases to the general population is important for interpreting database research. This report describes the representativeness of The Health Improvement Network (THIN), a UK primary care database, of the UK population. Demographics, deprivation (Townsend), Quality and Outcomes Framework (QOF) condition prevalence and deaths from THIN were compared with national statistical and QOF 2006/2007 data. Demographics were similar although THIN contained fewer people aged under 25 years. Condition prevalence was comparable, e.g. 3.5% diabetes prevalence in THIN, 3.7% nationally. More THIN patients lived in the most affluent areas (23.5% in THIN, 20% nationally). Between 1990 and 2009, standardised mortality ratio ranged from 0.81 (95% CI: 0.39-1.49; 1990) to 0.93 (95% CI: 0.48-1.64; 1995). Adjusting for demographics/deprivation, the 2006 THIN death rate was 9.08/1000 population close to the national death rate of 9.4/1000 population. THIN is generalisable to the UK for demographics, major condition prevalence and death rates adjusted for demographics and deprivation.
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            Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

            The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.
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              A Bayesian neural network method for adverse drug reaction signal generation.

              The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35,000 new reports are added quarterly. The task of trying to find new drug-ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug-ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril-coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin-acne; digoxin-rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug-ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.
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                Author and article information

                Journal
                Pharmacoepidemiol Drug Saf
                Pharmacoepidemiol Drug Saf
                10.1002/(ISSN)1099-1557
                PDS
                Pharmacoepidemiology and Drug Safety
                John Wiley and Sons Inc. (Hoboken )
                1053-8569
                1099-1557
                May 2015
                27 January 2015
                : 24
                : 5 ( doiID: 10.1002/pds.v24.5 )
                : 486-494
                Affiliations
                [ 1 ] Uppsala Monitoring CentreWHO Collaborating Centre for International Drug Monitoring UppsalaSweden
                Author notes
                [*] [* ]Correspondence to: K. Star, Uppsala Monitoring Centre, WHO Collaborating Centre for, International Drug Monitoring, Box 1051, S‐751 40 Uppsala, Sweden. Email: Kristina.Star@ 123456who-umc.org
                Article
                PDS3739 PDS-14-0037.R2
                10.1002/pds.3739
                5024044
                25623045
                4eb134fa-8a1b-4f36-b1d9-aa9d18cd4025
                © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 22 January 2014
                : 12 October 2014
                : 17 November 2014
                Page count
                Pages: 9
                Categories
                Original Report
                Original Reports
                Custom metadata
                2.0
                pds3739
                May 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:15.09.2016

                Pharmacology & Pharmaceutical medicine
                electronic medical records,temporal pattern discovery,adverse drug reactions,post‐marketing surveillance,signal detection and analysis,individual case safety reports,pharmacoepidemiology

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