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PURPOSE To compare the results of drug safety data mining with three different algorithms, when adverse events are identified using MedDRA Preferred Terms (PT) vs. High Level Terms (HLT) vs. Standardised MedDRA Queries (SMQ). METHODS For a representative set of 26 drugs, data from the FDA Adverse Event Reporting System (AERS) database from 2001 through(More)
BACKGROUND Computer-assisted data mining algorithms (DMAs) are being studied to screen spontaneous reporting databases for signals of novel adverse events. The performance characteristics and optimum deployment of these techniques remain to be established. OBJECTIVE To explore issues in the practical evaluation and deployment of DMAs by comparing findings(More)
BACKGROUND Pharmacovigilance data-mining algorithms (DMAs) are known to generate significant numbers of false-positive signals of disproportionate reporting (SDRs), using various standards to define the terms 'true positive' and 'false positive'. OBJECTIVE To construct a highly inclusive reference event database of reported adverse events for a limited(More)
A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human(More)
Drug-induced immune thrombocytopenia (DITP) is often suspected in patients with acute thrombocytopenia unexplained by other causes, but documenting that a drug is the cause of thrombocytopenia can be challenging. To provide a resource for diagnosis of DITP and for drug safety surveillance, we analyzed 3 distinct methods for identifying drugs that may cause(More)
BACKGROUND Masking is a statistical issue by which signals are hidden by the presence of other medicines in the database. In the absence algorithm, the impact of the masking effect has not been fully investigated. OBJECTIVE Our study is aimed at assessing the extent and the impact of the masking effect on two large spontaneous reporting databases. STUDY(More)
PURPOSE Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with(More)
Pharmacovigilance serves to detect previously unrecognised adverse events associated with the use of medicines. The simplest method for detecting signals of such events is crude inspection of lists of spontaneously reported drug-event combinations. Quantitative and automated numerator-based methods such as Bayesian data mining can supplement or supplant(More)
A paradoxical drug reaction constitutes an outcome that is opposite from the outcome that would be expected from the drug's known actions. There are three types: 1. A paradoxical response in a condition for which the drug is being explicitly prescribed. 2. Paradoxical precipitation of a condition for which the drug is indicated, when the drug is being used(More)
BACKGROUND A phenomenon of 'masking' or 'cloaking' in pharmacovigilance data mining has been described, which can potentially cause signals of disproportionate reporting (SDRs) to be missed, particularly in pharmaceutical company databases. Masking has been predicted theoretically, observed anecdotally or studied to a limited extent in both pharmaceutical(More)