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Comparative Study
. 2013 May 1;20(3):420-6.
doi: 10.1136/amiajnl-2012-001119. Epub 2012 Nov 17.

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

Affiliations
Comparative Study

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

Mei Liu et al. J Am Med Inform Assoc. .

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.

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Figures

Figure 1
Figure 1
Study design overview. Correlation of abnormal laboratory results with specific drug administration through comparison of the outcomes of patients who were exposed with the outcomes of those who were unexposed to the study drug. Patients must have at least two laboratory measurements during one hospitalization, the first of which must be normal. EMR, electronic medical record. This figure is only reproduced in colour in the online version.

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