title: Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.
abstract: BACKGROUND
  Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.
  
  
  METHODS
  We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs.
  
  
  RESULTS
  In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.
  
  
  CONCLUSIONS
  The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.
authors.affiliation: Department of Public Health, EA 2694, University of Lille Nord de France, F-59000 Lille, France. gregoire.ficheur@univ-lille2.fr.
authors.forename: Grégoire
authors.initials: G
authors.lastname: Ficheur
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Chazard
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Beuscart
authors.forename: Béatrice
authors.initials: B
authors.lastname: Merlin
authors.forename: Michel
authors.initials: M
authors.lastname: Luyckx
authors.forename: Régis
authors.initials: R
authors.lastname: Beuscart
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards*; D000368:Aged; D016208:Databases, Factual / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D006947:Hyperkalemia / Q000139:chemically induced*; D008297:Male; D008492:Medical Informatics Computing / Q000592:standards*