Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs

Abstract

Objective. The aim of this paper is to demonstrate the usefulness of the Bayesian Confidence Propagation Neural Network (BCPNN) in the detection of drug-specific and drug-group effects in the database of adverse drug reactions of the World Health Organization Programme for International Drug Monitoring.

Methods. Examples of drug–adverse reaction combinations highlighted by the BCPNN as quantitative associations were selected. The anatomical therapeutic chemical (ATC) group to which the drug belonged was then identified, and the information component (IC) was calculated for this ATC group and the adverse drug reaction (ADR). The IC of the ATC group with the ADR was then compared with the IC of the drug–ADR by plotting the change in IC and its 95% confidence limit over time for both.

Results. The chosen examples show that the BCPNN data-mining approach can identify drug-specific as well as group effects. In the known examples that served as test cases, beta-blocking agents other than practolol are not associated with sclerosing peritonitis, but all angiotensin-converting enzyme inhibitors are associated with coughing, as are antihistamines with heart-rhythm disorders and antipsychotics with myocarditis. The recently identified association between antipsychotics and myocarditis remains even after consideration of concomitant medication.

Conclusion. The BCPNN can be used to improve the ability of a signal detection system to highlight group and drug-specific effects.