A computerized system for detecting signals due to drug–drug interactions in spontaneous reporting systems

Abstract

WHAT IS ALREADY KNOWN ABOUT THE SUBJECT • 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. WHAT THIS STUDY ADDS • 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.

Association between Selective Serotonin Reuptake Inhibitor Therapy and Suicidality: Analysis of U.S. Food and Drug Administration Adverse Event Reporting System Data

“…Non-cases consisted of patients associated with all other reports. 17,[25][26][27][28] In the present study, the association between SSRIs and suicidal or self-harm events was calculated as an ROR. The RORs were calculated from two-by-two contingency tables of counts that indicated the presence or absence of a particular drug and a particular adverse event in the case reports, and were expressed as point estimates with 95% confidence intervals (CIs).…”

“…28) The ROR can be adjusted using logistic regression analysis, 25,26) which offers the possibility of controlling for covariates, and can be used to analyze the effects of the interaction terms in detail. [25][26][27] We applied the logistic regression model in the calculation of adjusted RORs for age-stratified patient data and refined the safety signal with a dedicated correction to detect possible confounding factors present in the database. The RORs were adjusted for gender, reporting year, and stratified age group by applying logistic regression analysis.…”

Analysis of the Interaction between Clopidogrel, Aspirin, and Proton Pump Inhibitors Using the FDA Adverse Event Reporting System Database

“…23,24) Logistic regression offers the advantageous possibility of controlling for covariates and it can be used to analyze the use of interaction terms in more detail. [23][24][25] However, other research groups have criticized the use of logistic regression to assess drug interactions. 35) Although research on the performance, accuracy, and reliability of different data mining algorithms is in progress, there is no recognized gold standard methodology.…”

“…35) Although research on the performance, accuracy, and reliability of different data mining algorithms is in progress, there is no recognized gold standard methodology. 25,36) In the Table 3 (for hemorrhagic events) and Table 6 (for embolic/thrombotic events), the crude ROR offered a rough indication of the signal strength for the influence of PPIs on combined administration of clopidogrel and aspirin. As shown in Table 3, the reporting of the hemorrhagic events decreased when PPIs were used along with concomitant clopidogrel and aspirin.…”

“…23,24) The effects of drug combinations can be studied using a statistical interaction term in a logistic model to calculate the RORs. [22][23][24][25] To our knowledge, our study was the first to evaluate the effects of the interactions between PPIs, clopidogrel, and aspirin using the FAERS database.…”