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      Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area

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          Abstract

          Purpose

          Disproportionality screening analysis is acknowledged as a tool for performing signal detection in databases of adverse drug reactions (ADRs), e.g., in the European Union (EU) Drug Authority setting. The purpose of this study was to explore the possibility of decreasing false-positive signals of disproportionate reporting (SDR) by calculating the proportional reporting ratio (PRR)-by-therapeutic area (TA), while still maintaining the ability to detect relevant SDRs.

          Methods

          In the EudraVigilance (EV) Database, output from PRR calculated with a restricted TA comparator background was compared in detail to output from conventional authority-setting PRR calculations for four drugs: bicalutamide, abiraterone, metformin, and vildagliptin, within the TAs of prostate gland disease and type 2 diabetes mellitus.

          Results

          ADR reports per investigated drug ranged from 2,400 to 50,000. The PRR-TA’s ability to detect true-positive SDRs (as acknowledged in approved labeling) was increased compared to the conventional PRR, and performed 8–31 % better than a recently proposed stricter EU-SDR definition. The PRR-TA removed false SDRs confounded by disease or disease spill-over by up to 63 %, while retaining/increasing the number of unclassified SDRs relevant for manual validation, and thereby improving the ratio between confounded SDRs (i.e., noise) and unclassified SDRs for all investigated drugs (possible signals).

          Conclusions

          The performance of the PRR was improved by background restriction with the PRR-TA method; the number of false-positive SDRs decreased, and the ability to detect true-positive SDRs increased, improving the signal-to-noise ratio. Further development and validation of the method is needed within other TAs and databases, and for disproportionality analysis methods.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00228-014-1658-1) contains supplementary material, which is available to authorized users.

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          Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database.

          Mileni Machado, Matthew T. O'Neill, Ana Szarfman (2001)
          Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals [lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2]. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.
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            A Bayesian neural network method for adverse drug reaction signal generation.

            Mark A. Bate, Diana M. Lindquist, I Anna S Olsson … (1998)
            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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              Validation of statistical signal detection procedures in eudravigilance post-authorization data: a retrospective evaluation of the potential for earlier signalling.

              Ana Hidalgo Conde, Francois Maignen, Jim Slattery … (2010)
              Screening large databases of spontaneous case reports of possible adverse drug reactions (ADRs) is an established method of identifying hitherto unknown adverse effects of medicinal products; however, there is a lack of consensus concerning the value of formal statistical screening procedures in guiding such a process. This study was performed to clarify the nature of any added benefits and additional effort required when established pharmacovigilance techniques are supplemented with statistical screening. To evaluate whether statistical signal detection in spontaneous reporting data can lead to earlier detection of drug safety problems and to assess the additional regulatory work entailed. Using the EudraVigilance post-authorization module (EVPM), a screening procedure based on the proportional reporting ratio (PRR) was applied retrospectively to examine if regulatory investigations concerning ADRs in a predefined set of products could have been initiated earlier than occurred in practice. During the same time period, between September 2003 and March 2007, the number of PRR-based signals of disproportionate reporting (SDR) that arose in the same set of products was calculated and evaluated to determine the number requiring investigation. The outcome is expressed as the ratio of the number of SDRs requiring investigation compared with the number of signals pre-empted by the statistical screening approach. In those cases where the signal was discovered earlier, the delay was calculated between identification by the PRR method and by the method that originally identified the signal. In 191 chemically different products, 532 adverse reactions were added to the summary of product characteristics during the study period. Of these, 405 were designated as important medical events (IMEs) based on a comprehensive predefined list. Of the IMEs, 217 (53.6%) were identified earlier by the statistical screening technique, 79 (19.6%) were detected after the date at which they were raised by standard pharmacovigilance methods and 109 (26.9%) were not signalled during the study period. 1561 SDRs requiring further evaluation were detected during the study period, giving a ratio of 7.2 assessments for each signal pre-empted. The mean delay between the discovery of signals using the statistical methods in the EVPM and established methods in the 217 cases detected earlier was 2.45 years. A review resulted in clear explanation for why the statistical method had not pre-empted detection in all but 77 of 188 cases. The form of statistical signal detection tested in this study can provide significant early warning in a large proportion of drug safety problems; however, it cannot detect all safety issues more quickly than other pharmacovigilance processes and hence it should be used in addition to, rather than as an alternative to, established methods.
              Article 0 comments Cited 30 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Birgitta Grundmark: birgitta.grundmark@mpa.se
                Journal
                Journal ID (nlm-ta): Eur J Clin Pharmacol
                Journal ID (iso-abbrev): Eur. J. Clin. Pharmacol
                Title: European Journal of Clinical Pharmacology
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 0031-6970
                ISSN (Electronic): 1432-1041
                Publication date (Electronic): 7 March 2014
                Publication date PMC-release: 7 March 2014
                Publication date (Print): 2014
                Volume: 70
                Pages: 627-635
                Affiliations
                [ ]Department of Surgical Sciences, Uppsala University, 75185 Uppsala, Sweden
                [ ]Department of Pharmacovigilance, Medical Products Agency, Uppsala, Sweden
                [ ]Regional Cancer Centre of the Uppsala-Orebro region, Uppsala University Hospital, Uppsala, Sweden
                [ ]Division of Cancer Studies, King’s College London, Medical School, London, UK
                [ ]Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden
                [ ]Scientific Support/Epidemiology, Medical Products Agency, Uppsala, Sweden
                Article
                Publisher ID: 1658
                DOI: 10.1007/s00228-014-1658-1
                PMC ID: 3978377
                PubMed ID: 24599513
                SO-VID: a01c14a1-e1bf-47bc-82ea-97e8e343a1f5
                Copyright © © The Author(s) 2014
                License:

                Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                Date received : 21 August 2013
                Date accepted : 10 February 2014
                Categories
                Subject: Pharmacoepidemiology and Prescription
                Custom metadata
                issue-copyright-statement © Springer-Verlag Berlin Heidelberg 2014

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: prr,adverse drug reactions,adr,signal detection,pharmacovigilance,disproportionality analysis
                Data availability:
                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: prr, adverse drug reactions, adr, signal detection, pharmacovigilance, disproportionality analysis

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