Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports

This work is licensed under a Creative Commons Attribution Non-Commercial-No Derivatives 4.0 International License.Objective Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration’s adverse event reporting system. Methods Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification. Results Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR. Conclusion Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event