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


OBJECTIVE
To 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).


METHODS
We 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.


RESULTS
A 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.


CONCLUSIONS
The 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.
Bailey
Steven
S
Global Safety Surveillance and Epidemiology, Wyeth, Collegeville, Pennsylvania, USA. baileysr@wyeth.com
Singh
Ajay
A
Azadian
Robert
R
Huber
Peter
P
Blum
Michael
M
D016907:Adverse Drug Reaction Reporting Systems; D057225:Data Mining; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D010865:Pilot Projects; D011358:Product Surveillance, Postmarketing; D011446:Prospective Studies; D014481:United States; D014486:United States Food and Drug Administration