Introduction: Until now, methods of pharmacovigilance as disproportionality analysis were not capable of proving the otherwise well-established increased bleeding risk related to antidepressants (ADs). As bleeding events with ADs often occur in combination with antithrombotics, they might not be considered causative of, but merely “linked” with, the bleeding event. Therefore, we hypothesized that the causality assessment of bleeding events in association with ADs and the competitive impact of antithrombotics are factors contributing to the non-findings of previous pharmacovigilance studies.Methods: We performed a case/non-case study based on data from VigiBaseTM and calculated reporting odds ratios (RORs) for 25 ADs. We used individual case safety reports (ICSRs) that were differently categorized in the database regarding their type of association between drug and event. Furthermore, we investigated the competitive impact of antithrombotics by comparing RORs with and without ICSRs related to antithrombotics.Results: Analysis of ICSRs that were categorized as causally associated resulted in the detection of only two signals (citalopram and escitalopram; upper gastrointestinal bleeding). Analysis of ICSRs irrespective of the type of association resulted in the detection of signals in 8 out of 25 ADs (regarding bleeding, in general, gastrointestinal bleeding and upper gastrointestinal bleeding). Consideration of ICSRs associated with antithrombotics as competitive substances did not have a major impact on signal detection in our analysis.Conclusion: Categorization of the type of association between drug and event affects the results of quantitative signal detection. Causality assessment seems to play a major role in signal detection, probably particularly concerning rare, unknown, or clinically insignificant adverse drug reactions. ADs appear to significantly increase the bleeding risk, even independent of antithrombotic comedication.
Corrigendum: Risk of Bleeding Associated With Antidepressants: Impact of Causality Assessment and Competition Bias on Signal Detection
