title: Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system.
abstract: BACKGROUND
  Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings.
  
  
  OBJECTIVE
  To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.
  
  
  METHODS
  Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible.
  
  
  RESULTS
  Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate.
  
  
  CONCLUSIONS
  Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.
  
  
  CONCLUSIONS
  A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation.
authors.affiliation: Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
authors.forename: Preciosa M
authors.initials: PM
authors.lastname: Coloma
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
authors.forename: Gianluca
authors.initials: G
authors.lastname: Trifirò
authors.forename: Laura
authors.initials: L
authors.lastname: Furlong
authors.forename: Erik
authors.initials: E
authors.lastname: van Mulligen
authors.forename: Anna
authors.initials: A
authors.lastname: Bauer-Mehren
authors.forename: Paul
authors.initials: P
authors.lastname: Avillach
authors.forename: Jan
authors.initials: J
authors.lastname: Kors
authors.forename: Ferran
authors.initials: F
authors.lastname: Sanz
authors.forename: Jordi
authors.initials: J
authors.lastname: Mestres
authors.forename: José Luis
authors.initials: JL
authors.lastname: Oliveira
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
authors.forename: Ernst Ahlberg
authors.initials: EA
authors.lastname: Helgee
authors.forename: Mariam
authors.initials: M
authors.lastname: Molokhia
authors.forename: Justin
authors.initials: J
authors.lastname: Matthews
authors.forename: David
authors.initials: D
authors.lastname: Prieto-Merino
authors.forename: Rosa
authors.initials: R
authors.lastname: Gini
authors.forename: Ron
authors.initials: R
authors.lastname: Herings
authors.forename: Giampiero
authors.initials: G
authors.lastname: Mazzaglia
authors.forename: Gino
authors.initials: G
authors.lastname: Picelli
authors.forename: Lorenza
authors.initials: L
authors.lastname: Scotti
authors.forename: Lars
authors.initials: L
authors.lastname: Pedersen
authors.forename: Johan
authors.initials: J
authors.lastname: van der Lei
authors.forename: Miriam
authors.initials: M
authors.lastname: Sturkenboom
mesh_terms: D000208:Acute Disease; D016907:Adverse Drug Reaction Reporting Systems; D017963:Azithromycin / Q000009:adverse effects / Q000627:therapeutic use; D001623:Betamethasone / Q000009:adverse effects / Q000627:therapeutic use; D020117:Cisapride / Q000009:adverse effects / Q000627:therapeutic use; D004294:Domperidone / Q000009:adverse effects / Q000627:therapeutic use; D057286:Electronic Health Records; D015725:Fluconazole / Q000009:adverse effects / Q000627:therapeutic use; D006801:Humans; D019290:Megestrol Acetate / Q000009:adverse effects / Q000627:therapeutic use; D008787:Metoclopramide / Q000009:adverse effects / Q000627:therapeutic use; D009203:Myocardial Infarction / Q000139:chemically induced*
chemical_list: D004294:Domperidone; D017963:Azithromycin; D015725:Fluconazole; D001623:Betamethasone; D008787:Metoclopramide; D019290:Megestrol Acetate; D020117:Cisapride