Abstract
Multiple data sources are preferred in adverse drug event (ADEs) surveillance owing to inadequacies of single source. However, analytic methods to monitor potential ADEs after prolonged drug exposure are still lacking. In this study we propose a method aiming to screen potential ADEs by combining FDA Adverse Event Reporting System (FAERS) and Electronic Medical Record (EMR). The proposed method uses natural language processing (NLP) techniques to extract treatment outcome information captured in unstructured text and adopts case-crossover design in EMR. Performances were evaluated using two ADE knowledge bases: Adverse Drug Reaction Classification System (ADReCS) and SIDER. We tested our method in ADE signal detection of conventional disease-modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis patients. Findings showed that recall greatly increased when combining FAERS with EMR compared with FAERS alone and EMR alone, especially for flexible mapping strategy. Precision (FAERS + EMR) in detecting ADEs improved using ADReCS as gold standard compared with SIDER. In addition, signals detected from EMR have considerably overlapped with signals detected from FAERS or ADE knowledge bases, implying the importance of EMR for pharmacovigilance. ADE signals detected from EMR and/or FAERS but not in existing knowledge bases provide hypothesis for future study.
Highlights
Adverse drug event (ADE), the untoward occurrence after exposure to a drug (Pirmohamed et al, 1998), has been an important public health concern
ADE signals detected from Electronic Medical Record (EMR) and/or FDA Adverse Event Reporting System (FAERS) that are not validated by gold standards could very well likely be false positives and could be potential novel ADEs, providing hypothesis for future study
We proposed a method for detecting potential ADEs associated with drugs by combining two data sources: FAERS and EMR, and evaluated their performances using two ADE knowledge bases: Adverse Drug Reaction Classification System (ADReCS) and SIDER
Summary
Adverse drug event (ADE), the untoward occurrence after exposure to a drug (Pirmohamed et al, 1998), has been an important public health concern. To analyze the clinical narratives, natural language processing (NLP) methods have been employed to extract potential drug-ADE pairs. Signal detection methods such as χ2 test or odds ratio have been utilized to rank the pairs (Wang et al, 2009, 2010; Harpaz et al, 2013). We propose a method aiming to screen potential ADEs from both FAERS and EMR. This method incorporates NLP techniques to process EMR, where case-crossover study is employed with consideration of clinical context, including drug indication and temporal information. We test our proposed method for screening potential ADEs for conventional disease-modifying antirheumatic drugs (DMARDs), the cornerstone treatment for Rheumatoid arthritis (RA) patients
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