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J Am Med Inform Assoc 20:420-426 doi:10.1136/amiajnl-2012-001119
  • Research and applications

Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records

  1. Hua Xu2,6
  1. 1Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
  2. 2Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
  3. 3Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
  4. 4Division of General Internal Medicine, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
  5. 5Geriatric Research, Education and Clinical Care, Veterans Health Administration, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
  6. 6School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
  1. Correspondence to Dr Hua Xu, Department of Biomedical Informatics, School of Medicine, Vanderbilt University, 2209 Garland Ave, EBL 412, Nashville, TN 37232, USA; hua.xu{at}vanderbilt.edu
  • Received 21 May 2012
  • Accepted 25 October 2012
  • Published Online First 17 November 2012

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 χ2 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.

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