Future Proofing Adverse Event Monitoring.

‘‘The world is full of signals that we don’t perceive’’ wrote Stephen Jay Gould in ‘‘The Panda’s Thumb’’ [1]. He was suggesting that physical limits of human perception (hearing, smell, touch, taste) lead us to miss signals all around us that other animals with more acute senses can use to their advantage. The fundamental challenge of signal detection [simultaneously heightening both perception (sensitivity) and discrimination (specificity)] is embodied in this quotation, and the paper by Li and colleagues [2] in this issue of Drug Safety aims to turn up the volume in an effort to improve signal detection while screening out excess noise that comes with it. The ‘‘sound’’ on which the authors seek to improve signal detection is not the mating call from a far-away member of the same species, but rather the occurrence of an adverse event resulting from medication use. Patients take medications with the expectation that the benefits will outweigh the risks, and the public expects a positive benefit-risk balance for marketed drugs, provided they are used appropriately. The evidence for both benefits and risks that serves as a foundation for this assessment is constantly being updated and each new piece of information might alter the balance. Adverse event monitoring contributes to these assessments, and the adverse event reports received by the Food and Drug Administration Adverse Event Reporting System (FAERS) represent an ongoing engagement of practitioners, patients, and manufacturers with the US Food and Drug Administration (FDA). The FDA receives more than a million adverse event reports per year, a number that has increased substantially over recent years [3]. This expanding resource for drug safety information is only beneficial if put to constructive use, and the number of reports received by the FAERS rules out certain approaches; reviewing individual case reports and discerning patterns across them becomes impractical at numbers much smaller than the FAERS. Various statistical signal detection methods (such as disproportionality analyses and others) are available to mine this wealth of data and identify potential signals, permitting more targeted investigation, and efficient allocation of limited resources [4]. If a signaling approach generates too many alarms, alarm fatigue might follow, with the consequence of inaction when truly needed. Filtering the signals to reduce false positives is an approach to refining a signaling method, and of greater value with increasing numbers of adverse event reports. However, Evans [4] points out that tinkering with the signaling methods or simply increasing the size of an existing data source will likely only produce marginal gains and that substantial improvement in signal detection requires new types of data, such as might be obtained by combining different types of existing data sources. Li and colleagues [2] extend the framework of adverse event signal detection across data sources by applying their methods in a combined way across different data sources, including a spontaneous reporting system, an inpatient electronic health record system, an outpatient electronic health record system, and a health insurance claims database. They compared the databases across four categories of adverse event (acute myocardial infarction, gastrointestinal bleed, acute renal failure, and acute liver injury), & John D. Seeger jdseeger@partners.org; http://www.drugepi.org