Abstract

Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.

Highlights

  • Background & SummaryThe timely and accurate identification of adverse drug reactions (ADRs) during the post-approval phase is an important goal of the public health system

  • Data mining for pharmacovigilance has predominantly relied on spontaneous reporting systems (SRS), such as the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS)[4,5,7], which pool reports of suspected ADRs collected from manufacturers, healthcare professionals, and consumers

  • We provide a first of its kind reference standard to support prospective performance evaluations, including lead time to detection, whose applicability is not restricted to the mining of a particular data source

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Summary

Background & Summary

The timely and accurate identification of adverse drug reactions (ADRs) during the post-approval phase is an important goal of the public health system. Recent efforts to develop reference standards, namely those of the OMOP22 and the EU-ADR23 projects, sparked substantial progress in the field, and have been applied to various data sources[5,11,12,22,24] Both reference sets were designed for the retrospective evaluation of ADR identification based on health records. We provide a first of its kind reference standard to support prospective performance evaluations, including lead time to detection, whose applicability is not restricted to the mining of a particular data source. The reference standard was systematically curated from all product label updates (e.g., warnings) communicated by the FDA in 2013 It includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events ranging from mild to rare and serious.

Methods
Data Records
Technical Validation
Usage Notes
Author Contributions
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