Potential Utility of Data-Mining Algorithms for Early Detection of Potentially Fatal/Disabling Adverse Drug Reactions: A Retrospective Evaluation


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Abstract

The objective of this study was to apply 2 data-mining algorithms to a drug safety database to determine if these methods would have flagged potentially fatal/disabling adverse drug reactions that triggered black box warnings/drug withdrawals in advance of initial identification via “traditional” methods. Relevant drug-event combinations were identified from a journal publication. Data-mining algorithms using commonly cited disproportionality thresholds were then applied to the US Food and Drug Administration database. Seventy drug-event combinations were considered sufficiently specific for retrospective data mining. In a minority of instances, potential signals of disproportionate reporting were provided clearly in advance of initial identification via traditional pharmacovigilance methods. Data-mining algorithms have the potential to improve pharmacovigilance screening; however, for the majority of drug-event combinations, there was no substantial benefit of either over traditional methods. They should be considered as potential supplements to, and not substitutes for, traditional pharmacovigilance strategies. More research and experience will be needed to optimize deployment of data-mining algorithms in pharmacovigilance.

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