Application of an Empiric Bayesian Data Mining Algorithm to Reports of Pancreatitis Associated with Atypical Antipsychotics

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Abstract
Study Objective. To compare the results from one frequently cited data mining algorithm with those from a study which was published in a peer-reviewed journal, that examined the association of pancreatitis with selected atypical antipsychotics observed by traditional rule-based methods of signal detection. Design. Retrospective pharmacovigilance study. Intervention. The widely studied data mining algorithm known as the Multi-item Gamma Poisson Shrinker (MGPS) was applied to adverse-event reports from the United States Food and Drug Administration's Adverse Event Reporting System database through the first quarter of 2003 for clozapine, olanzapine, and risperidone to determine if a significant signal of pancreatitis would have been generated by this method in advance of their review or the addition of these events to the respective product labels. Measurements and Main Results. Data mining was performed by using nine preferred terms relevant to drug-induced pancreatitis from the Medical Dictionary for Regulatory Activities (MedDRA). Results from a previous study on the antipsychotics were reviewed and analyzed. Physicians' Desk References (PDRs) starting from 1994 were manually reviewed to determine the first year that pancreatitis was listed as an adverse event in the product label for each antipsychotic. This information was used as a surrogate marker of the timing of initial signal detection by traditional criteria. Pancreatitis was listed as an adverse event in a PDR for all three atypical antipsychotics. Despite the presence of up to 88 reports/drug-event combination in the Food and Drug Administration's Adverse Event Reporting System database, the MGPS failed to generate a signal of disproportional reporting of pancreatitis associated with the three antipsychotics despite the signaling of these drug-event combinations by traditional rule-based methods, as reflected in product labeling and/or the literature. These discordant findings illustrate key principles in the application of data mining algorithms to drug safety surveillance. Conclusion. The optimal place of data mining algorithms in the pharmacovigilance tool kit remains to be determined, requires consideration of numerous factors that may affect their performance, and is highly situation dependent.