A Model for Decision Support in Signal Triage

• Published in: Drug safety 2008-01, Vol.31 (9), p.727-735
• Main Authors: Levitan, Bennett, Yee, Chuen L., Russo, Leo, Bayney, Richard, Thomas, Adrian P., Klincewicz, Stephen L.
• Format: Article
• Language: English
• Subjects: Adverse and side effects; Adverse Drug Reaction Reporting Systems - statistics & numerical data; Biological and medical sciences; Clinical trial. Drug monitoring; Data Interpretation, Statistical; Decision Support Techniques; Decision-making; Drug Safety and Pharmacovigilance; Drug toxicity and drugs side effects treatment; Drug-Related Side Effects and Adverse Reactions; Drugs; General pharmacology; Humans; Leading Article; Management; Medical sciences; Medicine; Medicine & Public Health; Miscellaneous (drug allergy, mutagens, teratogens...); Models, Statistical; Pharmacology. Drug treatments; Pharmacology/Toxicology
• ISSN: 0114-5916; 1179-1942
• DOI: 10.2165/00002018-200831090-00001

Spontaneous reporting of suspected adverse drug reactions (ADRs) has long been a cornerstone of pharmacovigilance. With the increasingly large volume of ADRs, regulatory agencies, scientific/academic organizations and marketing authorization holders have applied statistical tools to assist in signal detection by identifying disproportionate reporting relationships in spontaneous reporting databases. These tools have generated large numbers of signals defined as drug-ADR reporting associations that meet specified statistical criteria. The challenge is to identify which signals are most likely to be medically important and therefore warrant priority for further investigation. Decisions related to signal triage are often complex and are based on a combination of clinical, epidemiological, pharmacological and regulatory criteria. There are no specific regulations, guidelines or standards that provide an objective basis for these decisions. This paper describes preliminary work to identify and quantify the specific factors that contribute to a decision to prioritize a specific drug-ADR combination for further in-depth review. We applied a tool from the discipline of decision analysis to systematically assess the important attributes of spontaneously reported ADRs. A model was created that integrates these assessments and produces rankings for the signals generated from quantitative signalling methods. Although more research is necessary to evaluate the performance of this model fully, preliminary results suggest that the use of formal decision analysis approaches to support signal triage can provide potential benefit and will help meet an important need.