title: Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records.
abstract: OBJECTIVE
  Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs.
  
  
  METHODS
  Using 12 years of EMR data from Vanderbilt University Medical Center (VUMC), we designed a study to correlate abnormal laboratory results with specific drug administrations by comparing the outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance measures used in spontaneous reporting systems (SRSs): proportional reporting ratio (PRR), reporting OR (ROR), Yule's Q (YULE), the χ(2) test (CHI), Bayesian confidence propagation neural networks (BCPNN), and a gamma Poisson shrinker (GPS).
  
  
  RESULTS
  We systematically evaluated the methods on two independently constructed reference standard datasets of drug-event pairs. The dataset of Yoon et al contained 470 drug-event pairs (10 drugs and 47 laboratory abnormalities). Using VUMC's EMR, we created another dataset of 378 drug-event pairs (nine drugs and 42 laboratory abnormalities). Evaluation on our reference standard showed that CHI, ROR, PRR, and YULE all had the same F score (62%). When the reference standard of Yoon et al was used, ROR had the best F score of 68%, with 77% precision and 61% recall.
  
  
  CONCLUSIONS
  Results suggest that EMR-derived laboratory measurements and medication orders can help to validate previously reported ADRs, and detect new ADRs.
authors.affiliation: Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
authors.forename: Eugenia Renne
authors.initials: ER
authors.lastname: McPeek Hinz
authors.forename: Michael Edwin
authors.initials: ME
authors.lastname: Matheny
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Jonathan Scott
authors.initials: JS
authors.lastname: Schildcrout
authors.forename: Randolph A
authors.initials: RA
authors.lastname: Miller
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D057286:Electronic Health Records; D006801:Humans; D060735:Pharmacovigilance; D011358:Product Surveillance, Postmarketing / Q000379:methods