Evidence map›Paper›PMID 20082540›Full record

ArticleDrug safety2010

Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles.

Steven Bailey, Ajay Singh, Robert Azadian, Peter Huber, Michael Blum

Abstract read
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In one paragraph

Article in Drug safety, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
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  10. Review
  11. Postmarketing surveillance for "modified-risk" tobacco products.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2012
    Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Steven BaileyGlobal Safety Surveillance and Epidemiology, Wyeth, Collegeville, Pennsylvania, USA. baileysr@wyeth.com
Ajay Singh
Robert Azadian
Peter Huber
Michael Blum

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of data mining has increased among regulators and pharmaceutical companies. The incremental value of data mining as an adjunct to traditional pharmacovigilance methods has yet to be demonstrated. Specifically, the utility in identifying new safety signals and the resources required to do so have not been elucidated.

objectivesTo analyse the number and types of disproportionately reported product-event combinations (DRPECs), as well as the final disposition of each, in order to understand the potential utility and resource implications of routinely conducting data mining in the US FDA Adverse Event Reporting System (AERS).

methodsWe generated DRPECs from AERS for six of Wyeth's products, prospectively tracked their dispositions and evaluated the appropriate DRPECs in the company's safety database. We chose EB05 (the lower bound of the 90% confidence interval around the Empirical Bayes Geometric Mean) > or =2 as the appropriate metric, employing stratification based on age, sex and year of report.

resultsA total of 861 DRPECs were identified - the average number of DRPECs was 144 per product. The proportion of unique preferred terms (PTs) in AERS for each drug with an EB05 > or =2 was similar across the six products (5.1-8.5%). Overall, 64.0% (551) of the DRPECs were closed after the initial screening (44.8% labelled, 14.3% indication related, 4.9% non-interpretable). An additional 9.9% (85) had been reviewed within the prior year and were not further reviewed. The remaining 26.1% (225) required full case review. After review of all pertinent reports and additional data, it was determined which of the DRPECs necessitated a formal review by the company's ongoing Safety Review Team (SRT) process. In total, 3.6% (31/861) of the DRPECs, yielding 16 medical concepts, were reviewed by the SRT, leading to seven labelling changes. These labelling changes involved 1.9% of all DRPECs generated. Four of the six compounds reviewed as part of this pilot had an identified labelling change. The workload required for this pilot, which was driven primarily by those DRPECs requiring review, was extensive, averaging 184 hours per product.

conclusionThe number of DRPECs identified for each drug approximately correlated with the number of unique PTs in the database. Over one-half of DRPECs were either labelled as per the company's reference safety information (RSI) or were under review after identification by traditional pharmacovigilance activities, suggesting that for marketed products these methods do identify adverse events detected by traditional pharmacovigilance methods. Approximately three-quarters of the 861 DRPECs identified were closed without case review after triage. Of the approximately one-quarter of DRPECs that required formal case review, seven resulted in an addition to the RSI for the relevant products. While this pilot does not allow us to comment on the utility of routine data mining for all products, it is significant that several new safety concepts were identified through this prospective exercise.

Indexed as

Adverse Drug Reaction Reporting SystemsData MiningDrug-Related Side Effects and Adverse ReactionsDatabases, FactualHumansPilot ProjectsProduct Surveillance, PostmarketingProspective StudiesUnited StatesUnited States Food and Drug Administration

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.