title: Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor?
abstract: OBJECTIVE
  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 new issues in drug safety arise.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. g.trifiro@erasmusmc.nl
authors.forename: Gianluca
authors.initials: G
authors.lastname: Trifirò
authors.forename: Antoine
authors.initials: A
authors.lastname: Pariente
authors.forename: Preciosa M
authors.initials: PM
authors.lastname: Coloma
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
authors.forename: Giovanni
authors.initials: G
authors.lastname: Polimeni
authors.forename: Ghada
authors.initials: G
authors.lastname: Miremont-Salamé
authors.forename: Maria Antonietta
authors.initials: MA
authors.lastname: Catania
authors.forename: Francesco
authors.initials: F
authors.lastname: Salvo
authors.forename: Anaelle
authors.initials: A
authors.lastname: David
authors.forename: Nicholas
authors.initials: N
authors.lastname: Moore
authors.forename: Achille Patrizio
authors.initials: AP
authors.lastname: Caputi
authors.forename: Miriam
authors.initials: M
authors.lastname: Sturkenboom
authors.forename: Mariam
authors.initials: M
authors.lastname: Molokhia
authors.forename: Julia
authors.initials: J
authors.lastname: Hippisley-Cox
authors.forename: Carlos Diaz
authors.initials: CD
authors.lastname: Acedo
authors.forename: Johan
authors.initials: J
authors.lastname: van der Lei
authors.forename: Annie
authors.initials: A
authors.lastname: Fourrier-Reglat
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D016903:Drug Monitoring / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records; D005060:Europe; D006801:Humans; D018570:Risk Assessment