Data mining on electronic health record databases for signal detection in pharmacovigilance: Which events to monitor?
Purpose: Data mining on electronic health records (EHRs) has emerged as a promising complementary method for post-marketing drug safety surveillance. The EU-ADR project, funded by the European Commission, is developing techniques that allow mining of EHRs for adverse drug events across different countries in Europe. Since mining on all possible events was considered to unduly increase the number of spurious signals, we wanted to create a ranked list of high-priority events. Methods: Scientific literature, medical textbooks, and websites of regulatory agencies were reviewed to create a preliminary list of events that are deemed important in pharmacovigilance. Two teams of pharmacovigilance experts independently rated each event on five criteria: 'trigger for drug withdrawal', 'trigger for black box warning', 'leading to emergency department visit or hospital admission', 'probability of event to be drug-related', and 'likelihood of death'. In case of disagreement, a consensus score was obtained. Ordinal scales between 0 and 3 were used for rating the criteria, and an overall score was computed to rank the events. Results: An initial list comprising 23 adverse events was identified. After rating all the events and calculation of overall scores, a ranked list was established. The top-ranking events were: cutaneous bullous eruptions, acute renal failure, anaphylactic shock, acute myocardial infarction, and rhabdomyolysis. Conclusions: A ranked list of 23 adverse drug events judged as important in pharmacovigilance was created to permit focused data mining. The list will need to be updated periodically as knowledge on drug safety evolves and newissues in drug safety arise. Copyright
|Keywords||Adverse event, Data mining, Database, Drug safety, Signal detection|
|Persistent URL||dx.doi.org/10.1002/pds.1836, hdl.handle.net/1765/24118|
|Journal||Pharmacoepidemiology and Drug Safety: an international journal|
|Grant||This work was funded by the European Commission 7th Framework Programme; grant id fp7/215847 - Exploring and understanding adverse drug reactions by integrative mining of clinical records and biomedical knowledge (EU-ADR)|
Trifirò, G, Pariente, A, Coloma, P.M, Kors, J.A, Polimeni, G, Miremont-Salamé, G, … Fourrier-Reglat, A. (2009). Data mining on electronic health record databases for signal detection in pharmacovigilance: Which events to monitor?. Pharmacoepidemiology and Drug Safety: an international journal, 18(12), 1176–1184. doi:10.1002/pds.1836