In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a drug was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by experts while the parameters were learnt from 593 completely filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre medical expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April–September 2014) and a prospective cohort of 1041 reports (January–December 2015). Additionally, a simplified assessment matrix was derived from the model, enabling its preliminary direct use by notifiers. Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although struggling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) TTA was 4 (2:8) days using the network and 8 (5:14) days using global introspection, meaning the network allowed a faster time to assessment, which has a procedural deadline of 30 days, improving daily activities in the centre. The matrix expressed similar validity, allowing an immediate feedback to the notifiers, which may result in better future engagement of patients and health professionals in the pharmacovigilance system.

Introduction

In pharmacovigilance, most of the reported cases are considered as suspected adverse drug reactions (ADR). Health professionals and consumers are asked to report episodes they believe are related with drug intake, but in most of the cases ADR are not particular for each drug and a drug rechallenge (i.e. the suspected drug was reintroduced into the patient’s therapy, or the patient has taken the same suspected drug before) rarely occurs. To solve this difficulty, health authorities have adopted structured and harmonized causality assessment methods, in order to classify the ADR reports with one of the causality degrees proposed by the Uppsala Monitoring Centre (WHO-UMC) causality assessment system [1]. Apart from ADR identification, where innovative methods have been proposed [2], causality assessment is an essential tool in the pharmacovigilance system, as it helps the riskbenefit evaluation of commercialized medicines, and is part of the signal detection (being a signal a “reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously” [۱]) performed by health authorities. The Portuguese Pharmacovigilance System has adopted the method of Global Introspection [3], since its creation. During this process, an expert (or a group of experts) expresses judgement about possible drug causation, considering all available data in the ADR report. The decision is based on the expert knowledge and experience, and uses no standardized tools. Although this is the method most widely used [4], it has some limitations related to its reproducibility and validity [5–۷]. Besides, this method is closely linked with the medical expert availability which not always allows meeting legal deadlines. Causality assessment can also be done through validated algorithms such as the Naranjo [8], Jones [9] or Karch-Lasagna [10] algorithms. Although these algorithms have better agreement rates than Global Introspection, they also have the disadvantage of not being flexible and, consequently, it is not possible to include more causal factors to be evaluated at the same time [4]. Moreover, in our experience, some real cases evaluated by more than one algorithm may give rise to different degrees of causality. Guidelines such as the ones used for causality assessment are several times hard to interpret and to apply, even by experienced practicioners. Furthermore, they often result in simple rules or association measures, making their application in decision support somewhat limited, especially in the context of guidelines that are to be computer-interpreted for decision support systems [11].