title: Predictive value of alert triggers for identification of developing adverse drug events.
abstract: OBJECTIVE
  Adverse drug events (ADEs) are the most common type of iatrogenic injury in hospitalized patients. However; the ability of electronic triggers to identify patients at high risk for inpatient ADEs before they occur has not been well studied. The objective of this study was to assess the positive predictive value of event triggers to detect developing ADEs.
  
  
  METHODS
  We conducted a prospective observational study in patients at a university-based teaching hospital during a 5-month period. Patients were monitored using electronic triggers designed to detect patients at increased risk for 4 types of ADEs: hypoglycemia, hypokalemia, hyperkalemia, and thrombocytopenia. Each patient for whom a trigger fired was followed to determine whether a drug-induced markedly abnormal laboratory result occurred between 1 and 72 hours after the initial trigger firing.
  
  
  RESULTS
  Overall, the triggers fired 611 times on 456 patients. Of the 456 patients, 101 experienced 1 or more related ADEs between 1 and 72 hours after the initial trigger firing. The positive predictive value of the triggers and median time from trigger firing to ADE was 31% and 11.6 hours for hypoglycemia, 4.0% and 17 hours for hypokalemia, 31% and 25.4 hours for hyperkalemia, and 21% and 48.4 hours for thrombocytopenia.
  
  
  CONCLUSIONS
  Computerized triggers have sufficient predictive value to detect developing ADEs and can help clinicians avert ADEs. More research is required to determine whether real-time, primary-prevention alerts may reduce the incidence of ADEs.
authors: Division of General Medicine and Clinical Epidemiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599-7110, USA. crmoore@med.unc.edu
authors: Carlton
authors: C
authors: Moore
authors: Jiang
authors: J
authors: Li
authors: Chang-Chiao
authors: CC
authors: Hung
authors: John
authors: J
authors: Downs
authors: Jonathan R
authors: JR
authors: Nebeker
mesh_terms: D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D020000:Decision Support Systems, Clinical; D005260:Female; D006784:Hospitals, Teaching; D006801:Humans; D008297:Male; D008875:Middle Aged; D009657:North Carolina; D011237:Predictive Value of Tests; D011446:Prospective Studies