Study on the increased probability of detecting adverse drug reactions based on bayes' theorem: evaluation of the usefulness of information on the onset timing of adverse drug reactions

In order to avoid adverse drug reactions (ADRs), pharmacists are reconstructing ADR-related information based on various types of data gathered from patients, and then providing this information to patients. Among the data provided to patients is the time-to-onset of ADRs after starting the medication (i.e., ADR onset timing information). However, a quantitative evaluation of the effect of onset timing information offered by pharmacists on the probability of ADRs occurring in patients receiving this information has not been reported to date. In this study, we extracted 40 ADR-drug combinations from the data in the Japanese Adverse Drug Event Report database. By applying Bayes' theorem to these combinations, we quantitatively evaluated the usefulness of onset timing information as an ADR detection predictor. As a result, when information on days after taking medication was added, 54 ADR-drug combinations showed a likelihood ratio (LR) in excess of 2. In particular, when considering the ADR-drug combination of anaphylactic shock with levofloxacin or loxoprofen, the number of days elapsed between start of medication and the onset of the ADR was 0, which corresponded to increased likelihood ratios (LRs) of 138.7301 or 58.4516, respectively. When information from 1-7 d after starting medication was added to the combination of liver disorder and acetaminophen, the LR was 11.1775. The results of this study indicate the clinical usefulness of offering information on ADR onset timing.