Literature DB >> 12071782

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

Elliot G Brown1.   

Abstract

To support signal generation a terminology should facilitate recognition of medical conditions by using terms which represent unique concepts, providing appropriate, homogeneous grouping of related terms. It should allow intuitive or mathematical identification of adverse events reaching a threshold frequency or with disproportionate incidence, permit identification of important events which are commonly drug-related, and support recognition of new syndromes. It is probable that the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) or high level terms (HLTs) will be used to represent adverse events for the purposes of signal generation. A comparison with 315 WHO Adverse Reaction Terminology (WHO-ART) PTs showed that for about 72% of WHO-ART PTs, there were one or two corresponding MedDRA PTs. However, there were instances where there were many MedDRA PTs corresponding to single WHO-ART PTs. In many cases, MedDRA HLTs grouped large numbers of PTs and sometimes there could be problems when a single HLT comprises PTs which represent very different medical concepts, or conditions which differ greatly in their clinical importance. Further studies are needed to compare the way in which identical data sets coded with MedDRA and with other terminologies actually function in generating and exploring signals using the same methods of detection and evaluation.

Mesh:

Year:  2002        PMID: 12071782     DOI: 10.2165/00002018-200225060-00009

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


  4 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

3.  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

4.  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

  4 in total
  29 in total

Review 1.  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

2.  Possible mitochondrial dysfunction and its association with antiretroviral therapy use in children perinatally infected with HIV.

Authors:  Marilyn J Crain; Miriam C Chernoff; James M Oleske; Susan B Brogly; Kathleen M Malee; Peggy R Borum; William A Meyer; Wendy G Mitchell; John H Moye; Heather M Ford-Chatterton; Russell B Van Dyke; George R Seage Iii
Journal:  J Infect Dis       Date:  2010-07-15       Impact factor: 5.226

Review 3.  Application of data mining techniques in pharmacovigilance.

Authors:  Andrew M Wilson; Lehana Thabane; Anne Holbrook
Journal:  Br J Clin Pharmacol       Date:  2004-02       Impact factor: 4.335

Review 4.  Using MedDRA: implications for risk management.

Authors:  Elliot G Brown
Journal:  Drug Saf       Date:  2004       Impact factor: 5.606

5.  Identifying adverse events of vaccines using a Bayesian method of medically guided information sharing.

Authors:  Colin John Crooks; David Prieto-Merino; Stephen J W Evans
Journal:  Drug Saf       Date:  2012-01-01       Impact factor: 5.606

6.  Terminological challenges in safety surveillance.

Authors:  Andrew Bate; Elliot G Brown; Stephen A Goldman; Manfred Hauben
Journal:  Drug Saf       Date:  2012-01-01       Impact factor: 5.606

7.  A decade of data mining and still counting.

Authors:  Manfred Hauben; G Niklas Norén
Journal:  Drug Saf       Date:  2010-07-01       Impact factor: 5.606

8.  Biclustering of adverse drug events in the FDA's spontaneous reporting system.

Authors:  R Harpaz; H Perez; H S Chase; R Rabadan; G Hripcsak; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

Review 9.  Clarification of terminology in drug safety.

Authors:  Jeffrey K Aronson; Robin E Ferner
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

Review 10.  Perspectives on the use of data mining in pharmaco-vigilance.

Authors:  June Almenoff; Joseph M Tonning; A Lawrence Gould; Ana Szarfman; Manfred Hauben; Rita Ouellet-Hellstrom; Robert Ball; Ken Hornbuckle; Louisa Walsh; Chuen Yee; Susan T Sacks; Nancy Yuen; Vaishali Patadia; Michael Blum; Mike Johnston; Charles Gerrits; Harry Seifert; Karol Lacroix
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.