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 medicine 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 an expert, aiming at implementing the current guidelines for causality assessment, while the parameters were learnt from 593 completely-filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre 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 to September 2014) and a prospective cohort of 1041 reports (January to December 2015). Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although strugling 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.
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Acknowledgements
This work has been developed under the scope of project NanoSTIMA [NORTE-01-0145-FEDER-000016], which was financed by the North Portugal Regional Operational Programme [NORTE 2020], under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund [ERDF].
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Rodrigues, P.P., Ferreira-Santos, D., Silva, A., Polónia, J., Ribeiro-Vaz, I. (2017). Implementing Guidelines for Causality Assessment of Adverse Drug Reaction Reports: A Bayesian Network Approach. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_6
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