Literature DB >> 19187799

Decision support methods for the detection of adverse events in post-marketing data.

M Hauben1, A Bate.   

Abstract

Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.

Mesh:

Year:  2009        PMID: 19187799     DOI: 10.1016/j.drudis.2008.12.012

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  48 in total

1.  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

Review 2.  Nasal septal perforation from bevacizumab: a discussion of outcomes, management, and pharmacovigilance.

Authors:  Judi Anne B Ramiscal; Aminah Jatoi
Journal:  Curr Oncol Rep       Date:  2012-08       Impact factor: 5.075

3.  Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System.

Authors:  Elisabetta Poluzzi; Emanuel Raschi; Domenico Motola; Ugo Moretti; Fabrizio De Ponti
Journal:  Drug Saf       Date:  2010-04-01       Impact factor: 5.606

4.  A Multiagent System for Integrated Detection of Pharmacovigilance Signals.

Authors:  Vassilis Koutkias; Marie-Christine Jaulent
Journal:  J Med Syst       Date:  2015-11-21       Impact factor: 4.460

5.  Detecting Signals of Disproportionate Reporting from Singapore's Spontaneous Adverse Event Reporting System: An Application of the Sequential Probability Ratio Test.

Authors:  Cheng Leng Chan; Sowmya Rudrappa; Pei San Ang; Shu Chuen Li; Stephen J W Evans
Journal:  Drug Saf       Date:  2017-08       Impact factor: 5.606

6.  Response to "Comment on: Botulinum Toxin Type A Overdoses: Analysis of the FDA Adverse Event Reporting System Database".

Authors:  Rashid Kazerooni; Edward P Armstrong
Journal:  Clin Drug Investig       Date:  2018-12       Impact factor: 2.859

7.  Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

Authors:  Rave Harpaz; Santiago Vilar; William Dumouchel; Hojjat Salmasian; Krystl Haerian; Nigam H Shah; Herbert S Chase; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2012-10-31       Impact factor: 4.497

8.  Evaluation of automated term groupings for detecting anaphylactic shock signals for drugs.

Authors:  Julien Souvignet; Gunnar Declerck; Béatrice Trombert; Jean Marie Rodrigues; Marie-Christine Jaulent; Cédric Bousquet
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

9.  Web-scale pharmacovigilance: listening to signals from the crowd.

Authors:  Ryen W White; Nicholas P Tatonetti; Nigam H Shah; Russ B Altman; Eric Horvitz
Journal:  J Am Med Inform Assoc       Date:  2013-03-06       Impact factor: 4.497

10.  Revisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilance.

Authors:  Manfred Hauben; Eric Y Hung
Journal:  Ther Adv Drug Saf       Date:  2016-05-23
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