Triage and Evaluation of Potential Safety Signals Identified from Electronic Healthcare Record Databases

Background: There is huge potential for mining electronic healthcare records (EHR) databases to augment current systems in pharmacovigilance.[1-3] Like any signal detection system, there is a need to establish ‘rules’ how to trigger an alert, when to consider a signal likely enough to be true to warrant follow-up or even to require immediate health policy intervention.[4,5] Objectives: To describe the process of prioritisation of drug-adverse event associations derived from signal detection using EHR databases in the EU-ADR Project. Methods: Association measures between drug use and acute myocardial infarction (AMI) were generated by first applying various statistical methods on healthcare data from seven databases of the EUADR network.[6] Association estimates were ranked based on the best performing method (Longitudinal Gamma Poisson Shrinker). Matched case-control and self-controlled case series methods were additionallyconducted to deal with temporality and confounding effect, while the LEOPARD method was applied to specifically detect protopathic bias. Consistency of the association among drugs of the same class and the number of excess cases attributable to the drug exposure were further assessed to prioritize the list of potential signals. Finally, signal filtering and signal substantiation were done using different bioinformatics workflows to determine the novelty and plausibility of the identified signals. Results: Demographic, clinical and prescription/dispensing data in three European Countries were obtained from 21 171 291 individuals with 154 474 063 person-years of follow-up within the period 1995–2011. Overall, 163 potential signals forAMI were identified based on statistical association. Of these, 72 signals were flagged by LEOPARDas likely due to protopathic bias. Further signal refinement to reduce possible confounding decreased the number of signals to 39. Nine signals remained after applying the criteria for novelty and plausibility. Conclusion: We propose a prioritisation strategy for drug safety signal detection using EHR by taking into account, in addition to statistical association, also public health relevance, novelty, and plausibility. This strategy needs to be further tested using other EHR data sources and other adverse events.