Quantitative evaluation of initial symptoms as predictors to detect adverse drug reactions using Bayes' theory: expansion and evaluation of drug-adverse drug reaction-initial symptom combinations using adverse event reporting system database.

In prescription dispensing in Japan, to avoid adverse drug reactions (ADR) pharmacists provide patients with information concerning the initial symptoms (IS) of any ADR that might be caused by the drugs they have been prescribed. However, the usefulness of such information for preventing ADR has not been quantitatively evaluated. We previously performed a trial calculation of the usefulness of rash as a predictor of drug-induced liver disorders by applying Bayes' theorem and showed that the predictive utility of IS can be quantitatively evaluated using likelihood ratios.