Early postmarketing drug safety surveillance: data mining points to consider

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.

To explore issues in the practical evaluation and deployment of DMAs by comparing findings from an empirical Bayesian DMA with those from a traditional drug safety surveillance program.

Published findings from early postmarketing safety surveillance of thalidomide were compared with findings from an empirical Bayesian DMA. Differential results were used to explore practical issues in the evaluation and deployment of DMAs.

Most adverse events highlighted by each method were compatible with the product labeling or natural history/complications of reported treatment indications. Traditional surveillance highlighted 4 potentially serious and unexpected adverse events (Stevens-Johnson syndrome, toxic epidermal necrolysis, seizures, skin ulcers) warranting labeling amendments or close monitoring. None of these adverse event terms generated a signal using the DMA.

The DMA would not have enhanced early postmarketing surveillance in this particular setting. While the results cannot be used to draw inferences about the global performance of DMAs, they illustrate the following: (1) DMA performance may be highly situation dependent; (2) over-reliance on these methods may have deleterious consequences, especially with so-called "designated medical events"; and (3) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous factors that may influence comparative performance and incremental utility of DMAs.