• adverse drug reaction;
  • drug–drug interactions;
  • signal detection


• Concomitant use of different drugs may yield excessive risk for adverse drug reactions and it is a challenging task to do surveillance on the safety profile of the interaction between different drugs.

• Currently, several methods are used by pharmacoepidemiologists and statisticians to detect possible drug–drug interactions in spontaneous reporting systems.

• However, with the increasing number of reports in the system, there is a growing need for a computerized system that could facilitate the process of data arrangement and detection of drug interaction.


• We had already developed a computerized system to detect adverse drug reaction signals due to single drugs.

• After the development of this system, interaction between different drugs could also be detected automatically and intelligently.

AIMS In spontaneous reporting systems (SRS), there is a growing need for the automated detection of adverse drug reactions (ADRs) resulting from drug–drug interactions. In addition, special attention is also needed for systems facilitating automated data preprocessing. In our study, we set up a computerized system to signal possible drug–drug interactions by which data acquisition and signal detection could be carried out automatically and the process of data preprocessing could also be facilitated.

METHODS This system was developed with Microsoft Visual Basic 6.0 and Microsoft Access was used as the database. Crude ADR reports submitted to Shanghai SRS from January 2007 to December 2008 were included in this study. The logistic regression method, the Ω shrinkage measure method, an additive model and a multiplicative model were used for automatic detection of drug–drug interactions where two drugs were used concomitantly.

RESULTS A total of 33 897 crude ADR reports were acquired from the SRS automatically. The 10 drug combinations most frequently reported were found and the 10 most suspicious drug–drug ADR combinations for each method were detected automatically after the performance of the system.

CONCLUSIONS Since the detection of drug–drug interaction depends upon the skills and memory of the professionals involved, is time consuming and the number of reports is increasing, this system might be a promising tool for the automated detection of possible drug–drug interactions in SRS.