A Model for Decision Support in Signal Triage

Abstract

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.

Drug-Induced Acute Myocardial Infarction: Identifying ‘Prime Suspects’ from Electronic Healthcare Records-Based Surveillance System

“…Various strategies for signal prioritisation have been proposed in many publications, although most of these refer to signals derived from SRS. [12], [13], [14], [15], [16] These strategies focus consistently on signals with serious adverse effect, strong supporting evidence, and greatest public health impact.…”

Improved Statistical Signal Detection in Pharmacovigilance by Combining Multiple Strength-of-Evidence Aspects in vigiRank

“…These include explicit assessment of causality by the reporter or national center, records of dechallenge interventions where the adverse event had resolved upon end of treatment, and records of rechallenge interventions where the adverse event had reappeared upon re-exposure to the drug. The latter was identified by experts as an important aspect of strength of evidence for pharmacovigilance triages [12]. However, an important difference is that their ascertainment of positive rechallenges is based on manual review, whereas we rely on already encoded information in the database, which may not be perfectly trustworthy.…”

“…This necessitates the use of triages to guide clinical assessment [9–11]. Generally, such triages can consider three main criteria: strength of evidence, novelty, and medical impact [12]. Since the 1990s, computerized evaluation of strength of evidence has relied largely on so-called disproportionality analysis [13–15].…”

“…This is contrasted by clinical assessment of potential safety signals, which attempts to manually account for all relevant aspects [11, 12]. In particular, the quality and content of individual reports is of fundamental importance [23].…”

Evaluation of patient reporting of adverse drug reactions to the UK ‘Yellow Card Scheme’: literature review, descriptive and qualitative analyses, and questionnaire surveys

“…Triage methods may be applied. 42,43 For example, the MHRA currently uses a system of filtering ADRs within its signal detection database so as to prioritise important ADRs for further clinical evaluation. Hence, ADR reports that have a fatal outcome, involve children or pregnant women, or include 'alert' terms (a list of reaction terms specified in-house by the MHRA) are regarded as high priority.…”