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      Valproic Acid and Fatalities in Children: A Review of Individual Case Safety Reports in VigiBase

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          Abstract

          Introduction

          Valproic acid is an effective first line drug for the treatment of epilepsy. Hepatotoxicity is a rare and potentially fatal adverse reaction for this medicine.

          Objective

          Firstly to characterise valproic acid reports on children with fatal outcome and secondly to determine reporting over time of hepatotoxicity with fatal outcome.

          Methods

          Individual case safety reports (ICSRs) for children ≤17 years with valproic acid and fatal outcome were retrieved from the WHO Global ICSR database, VigiBase, in June 2013. Reports were classified into hepatotoxic reactions or other reactions. Shrinkage observed-to-expected ratios were used to explore the relative reporting trend over time and for patient age. The frequency of polytherapy, i.e. reports with more than one antiepileptic medicine, was investigated.

          Results

          There have been 268 ICSRs with valproic acid and fatal outcome in children, reported from 25 countries since 1977. A total of 156 fatalities were reported with hepatotoxicity, which has been continuously and disproportionally reported over time. There were 31 fatalities with pancreatitis. Other frequently reported events were coma/encephalopathy, seizures, respiratory disorders and coagulopathy. Hepatotoxicity was disproportionally and most commonly reported in children aged 6 years and under (104/156 reports) but affected children of all ages. Polytherapy was significantly more frequently reported for valproic acid with fatal outcome (58%) compared with non-fatal outcome (34%).

          Conclusion

          Hepatotoxicity remains a considerable problem. The risk appears to be greatest in young children (6 years and below) but can occur at any age. Polytherapy is commonly reported and seems to be a risk factor for hepatotoxicity, pancreatitis and other serious adverse drug reactions with valproic acid.

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          Most cited references32

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          Under-reporting of adverse drug reactions : a systematic review.

          The purpose of this review was to estimate the extent of under-reporting of adverse drug reactions (ADRs) to spontaneous reporting systems and to investigate whether there are differences between different types of ADRs. A systematic literature search was carried out to identify studies providing a numerical estimate of under-reporting. Studies were included regardless of the methodology used or the setting, e.g. hospital versus general practice. Estimates of under-reporting were either extracted directly from the published study or calculated from the study data. These were expressed as the percentage of ADRs detected from intensive data collection that were not reported to the relevant local, regional or national spontaneous reporting systems. The median under-reporting rate was calculated across all studies and within subcategories of studies using different methods or settings. In total, 37 studies using a wide variety of surveillance methods were identified from 12 countries. These generated 43 numerical estimates of under-reporting. The median under-reporting rate across the 37 studies was 94% (interquartile range 82-98%). There was no significant difference in the median under-reporting rates calculated for general practice and hospital-based studies. Five of the ten general practice studies provided evidence of a higher median under-reporting rate for all ADRs compared with more serious or severe ADRs (95% and 80%, respectively). In comparison, for five of the eight hospital-based studies the median under-reporting rate for more serious or severe ADRs remained high (95%). The median under-reporting rate was lower for 19 studies investigating specific serious/severe ADR-drug combinations but was still high at 85%. This systematic review provides evidence of significant and widespread under-reporting of ADRs to spontaneous reporting systems including serious or severe ADRs. Further work is required to assess the impact of under-reporting on public health decisions and the effects of initiatives to improve reporting such as internet reporting, pharmacist/nurse reporting and direct patient reporting as well as improved education and training of healthcare professionals.
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            Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery

            Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.
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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

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                10 October 2014
                : 9
                : 10
                : e108970
                Affiliations
                [1 ]Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden
                [2 ]Academic Division of Child Health, University of Nottingham, Derbyshire Children's Hospital, Nottingham, United Kingdom
                Louisiana State University Health Sciences center, United States of America
                Author notes

                Competing Interests: Kristina Star and Imti Choonara have declared that no competing interests exist relevant for this study. I. Ralph Edwards has provided expert testimony relating to valproic acid and fetal damage.

                Conceived and designed the experiments: IC KS. Performed the experiments: KS. Analyzed the data: KS IC IRE. Contributed reagents/materials/analysis tools: KS. Wrote the paper: KS IC IRE.

                Article
                PONE-D-14-09404
                10.1371/journal.pone.0108970
                4193865
                25302991
                e4e6d3b7-108e-4192-a37f-46054d7a94da
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 February 2014
                : 2 September 2014
                Page count
                Pages: 8
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Biology and Life Sciences
                Toxicology
                Medicine and Health Sciences
                Clinical Medicine
                Epidemiology
                Pharmacoepidemiology
                Gastroenterology and Hepatology
                Liver Diseases
                Neurology
                Epilepsy
                Neuropharmacology
                Pediatrics
                Pharmacology
                Adverse Reactions
                Clinical Pharmacology

                Uncategorized
                Uncategorized

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