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Detection of adverse drug reaction signals using an electronic health records database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) algorithm

Authors
Yoon, D; Park, MY; Choi, NK; Park, BJ; Kim, JH; Park, RW
Citation
Clinical pharmacology and therapeutics, 91(3):467-474, 2012
Journal Title
Clinical pharmacology and therapeutics
ISSN
0009-92361532-6535
Abstract
Electronic health records (EHRs) are expected to be a good source of data for pharmacovigilance. However, current quantitative methods are not applicable to EHR data. We propose a novel quantitative postmarketing surveillance algorithm, the Comparison of Laboratory Extreme Abnormality Ratio (CLEAR), for detecting adverse drug reaction (ADR) signals from EHR data. The methodology involves calculating the odds ratio of laboratory abnormalities between a specific drug-exposed group and a matched unexposed group. Using a 10-year EHR data set, we applied the algorithm to test 470 randomly selected drug-event pairs. It was found possible to analyze a single drug-event pair in just 109 ± 159 seconds. In total, 120 of the 150 detected signals corresponded with previously reported ADRs (positive predictive value (PPV) = 0.837 ± 0.113, negative predictive value (NPV) = 0.659 ± 0.180). By quickly and efficiently identifying ADR signals from EHR data, the CLEAR algorithm can significantly contribute to the utilization of EHR data for pharmacovigilance.
MeSH terms
*Adverse Drug Reaction Reporting Systems*Algorithms*Databases, FactualDrug Toxicity/chemically induced/*epidemiology*Electronic Health RecordsHumansLaboratoriesOdds RatioPharmaceutical Preparations/*adverse effectsPharmacovigilancePredictive Value of TestsProduct Surveillance, Postmarketing/methods
DOI
10.1038/clpt.2011.248
PMID
22237257
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Medical Informatics
AJOU Authors
박, 래웅
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