Literature DB >> 15154830

Using MedDRA: implications for risk management.

Elliot G Brown1.   

Abstract

The introduction of MedDRA, the Medical Dictionary for Regulatory Activities, as a standardised terminology may have a major impact on the performance of risk management. Thus, MedDRA is likely to have an important effect on the analysis of clinical trial safety data. Review of the most commonly used terms in clinical trial tables from the labelling of ten products indicated that each adverse event could be represented by many MedDRA preferred terms; this might theoretically lead to failure to identify differences in adverse event incidence between treatment arms. Possible solutions are proposed. The use of MedDRA in spontaneous reporting systems is a regulatory requirement in some countries. Variability in modes of implementation and use of the terminology are discussed; these may impose additional limitations on any use of spontaneous data for comparative purposes. There are important differences in the ways that safety databases interface with MedDRA and uncertainty about the most appropriate way to manage version changes. The characteristics of MedDRA must be taken into account when establishing methods for signal detection and its use will affect the retrieval of similar cases as required for signal evaluation. The use of MedDRA in the periodic safety update report is discussed. The possible use of MedDRA in pharmacoepidemiology is highly relevant to risk management, and some issues are briefly outlined. With regard to communication of risk, if MedDRA is introduced into existing product labelling, care must be taken that the change itself does not cause misunderstanding; the most appropriate use of MedDRA in this regard remains to be determined. There is a need for careful evaluation of MedDRA in fulfilling its various functions in pharmacovigilance, followed by definitive regulatory guidance on its use.

Mesh:

Year:  2004        PMID: 15154830     DOI: 10.2165/00002018-200427080-00010

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  9 in total

Review 1.  The medical dictionary for regulatory activities (MedDRA).

Authors:  E G Brown; L Wood; S Wood
Journal:  Drug Saf       Date:  1999-02       Impact factor: 5.606

2.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

Authors:  S J Evans; P C Waller; S Davis
Journal:  Pharmacoepidemiol Drug Saf       Date:  2001 Oct-Nov       Impact factor: 2.890

Review 3.  Methods and pitfalls in searching drug safety databases utilising the Medical Dictionary for Regulatory Activities (MedDRA).

Authors:  Elliot G Brown
Journal:  Drug Saf       Date:  2003       Impact factor: 5.606

4.  A data mining approach for signal detection and analysis.

Authors:  Andrew Bate; Marie Lindquist; I Ralph Edwards; Roland Orre
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

5.  Effects of coding dictionary on signal generation: a consideration of use of MedDRA compared with WHO-ART.

Authors:  Elliot G Brown
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

6.  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.

Authors:  Ana Szarfman; Stella G Machado; Robert T O'Neill
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

7.  International Conference on Harmonisation; guidance on Addendum to E2C Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs; availability. Notice.

Authors: 
Journal:  Fed Regist       Date:  2004-02-05

8.  Tabulation and analysis of pharmacovigilance data using the medical dictionary for regulatory activities.

Authors:  E Brown Bmedsci Mb Chb Mrcgp Ffpm; S Douglas Bsc
Journal:  Pharmacoepidemiol Drug Saf       Date:  2000-11       Impact factor: 2.890

9.  Quality criteria for early signals of possible adverse drug reactions.

Authors:  I R Edwards; M Lindquist; B E Wiholm; E Napke
Journal:  Lancet       Date:  1990-07-21       Impact factor: 79.321

  9 in total
  16 in total

1.  Identifying drugs that cause acute thrombocytopenia: an analysis using 3 distinct methods.

Authors:  Jessica A Reese; Xiaoning Li; Manfred Hauben; Richard H Aster; Daniel W Bougie; Brian R Curtis; James N George; Sara K Vesely
Journal:  Blood       Date:  2010-06-08       Impact factor: 22.113

2.  Appraisal of the MedDRA conceptual structure for describing and grouping adverse drug reactions.

Authors:  Cédric Bousquet; Georges Lagier; Agnès Lillo-Le Louët; Christine Le Beller; Alain Venot; Marie-Christine Jaulent
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

3.  What counts in data mining?

Authors:  Manfred Hauben; Vaishali K Patadia; David Goldsmith
Journal:  Drug Saf       Date:  2006       Impact factor: 5.606

4.  Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.

Authors:  Xiaoyan Wang; George Hripcsak; Marianthi Markatou; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2009-03-04       Impact factor: 4.497

5.  The Use of the Medical Dictionary for Regulatory Activities in the Identification of Mitochondrial Dysfunction in HIV-Infected Children.

Authors:  Miriam Chernoff; Heather Ford-Chatterton; Marilyn J Crain
Journal:  Case Studies Bus Ind Gov Stat       Date:  2012-10

6.  Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection.

Authors:  Taxiarchis Botsis; Michael D Nguyen; Emily Jane Woo; Marianthi Markatou; Robert Ball
Journal:  J Am Med Inform Assoc       Date:  2011-06-27       Impact factor: 4.497

7.  The contribution of the vaccine adverse event text mining system to the classification of possible Guillain-Barré syndrome reports.

Authors:  T Botsis; E J Woo; R Ball
Journal:  Appl Clin Inform       Date:  2013-02-27       Impact factor: 2.342

8.  Cardiovascular, ocular and bone adverse reactions associated with thiazolidinediones: a disproportionality analysis of the US FDA adverse event reporting system database.

Authors:  Domenico Motola; Carlo Piccinni; Chiara Biagi; Emanuel Raschi; Anna Marra; Giulio Marchesini; Elisabetta Poluzzi
Journal:  Drug Saf       Date:  2012-04-01       Impact factor: 5.606

9.  Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria.

Authors:  Masayo Hayakawa; Takeshi Imai; Yoshimasa Kawazoe; Kouji Kozaki; Kazuhiko Ohe
Journal:  Drug Saf       Date:  2019-09       Impact factor: 5.606

10.  Application of information retrieval approaches to case classification in the vaccine adverse event reporting system.

Authors:  Taxiarchis Botsis; Emily Jane Woo; Robert Ball
Journal:  Drug Saf       Date:  2013-07       Impact factor: 5.606

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