A Discovery and Verification Approach for Pharmacovigilance using Electronic Health Care Data

Abstract

AbstractIntroductionPharmacovigilance shifted its focus from spontaneous reporting systems to electronic health care (EHC) data. Usually, a single statistical method is used to detect signals, i.e., potential adverse drug reactions (ADRs).Objective and MethodWe present a novel approach to detect ADRs in EHC databases. It aggregates the results of multiple statistical signal detection methods applying Borda count ranking, a preference voting system, which results are used by an expert committee to select plausible signals. The obtained signals are afterwards investigated in tailored pharmacoepidemiological studies to provide support of plausibility or spuriousness of the signal.We showcase the approach using data from the German Pharmacoepidemiological Research Database on drug reactions of the direct oral anticoagulant rivaroxaban. Results of four statistical methods are aggregated into Borda count rankings: longitudinal Gamma Poisson shrinker, Bayesian confidence propagation neural network, random forests and LASSO. A verification study designed as nested active comparator case-control study was conducted. We included patients diagnosed with atrial fibrillation who initiated anticoagulant treatment with rivaroxaban or with phenprocoumon as active comparator between 2011 and 2017.ResultsThe case study highlights that our Borda ranking approach (https://borda.bips.eu) is fast, able to retrieve known ADRs and find other interesting signals. Hasty false conclusions are avoided by a verification study, which is, however, time-consuming.ConclusionPost-market signal detection in EHC data is useful to identify and validate safety signals, particularly a few years after first admission to the market, when spontaneous reports are less frequent and more EHC data are available.