دانلود رایگان مقاله انگلیسی ارزیابی علیت از گزارش های مربوط به واکنش های نامطلوب دارو با استفاده از شبکه بیزی - الزویر 2018

ABSTRACT

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.

Concluding remarks

The derived Bayesian network model has been used in the Northern Pharmacovigilance Centre, in Portugal, for more than three years now, for causality assessment of ADR reports. Upon reception of an ADR report, at the pharmacovigilance centre, whilst the expert is still and always consulted for final assessment, the centre pharmacists can, in parallel, use the network to inform the notifier about the preliminary assessment, speeding up the process of the centre. One important aspect of the creation of our model was to endow the pharmacovigilance team with an interactive model which could provide, along with the best prediction of the causality degree assigned by the expert, a visual interpretation of the interactions and dependences of different factors in the causality assessment process, more than the applied in other assessment algorithms (e.g. decision trees) or other less-interpretable models (e.g. neural nets). The additional goal (which is out of scope of this paper) was to improve the parallel assessment done by the team, preparing the final report for the national and European institutions of pharmacovigilance.