Applied Data Mining of the FDA Adverse Event Reporting System, FAERS, and the Japanese Adverse Drug Event Report Database, JADER: Signal Detection of Adverse Events by New Quinolones

Abstract

Objective: Signal detection by analyzing adverse event spontaneous report databases is used to monitor drug safety.  One of the major spontaneous report databases is the FDA Adverse Event Reporting System (FAERS).  Recently, the Japanese Adverse Drug Event Report database (JADER) was released.  To compare FAERS and JADER, we calculated the signals of adverse events by new quinolones (NQs).
Methods: We extracted reports of adverse events by NQs from FAERS and JADER, and analyzed them using the ROR data mining algorithm.  Thirteen kinds of NQs were extracted, and the terms of adverse events extracted were defined by MedDRA.
Results: There were 35,990,645 reports in FAERS and 1,643,404 reports in JADER.  Significant RORs were found for hypersensitivity (FAERS: 1.78, JADER: 1.47), arrhythmia (1.07, 0.68), hypoglycemia (1.80, 2.03), hyperglycemia (0.72, 0.78), rhabdomyolysis (1.01, 0.78), tendon disorders (15.18, 6.59), psychiatric symptoms (1.12, 0.45) and convulsion (0.99, 1.31).  We identified 4 types of adverse events by comparing FAERS and JADER: 1) Signal detection in both, 2) No signal detection in either, 3) Signal detection only in FAERS, 4) Signal detection only in JADER.
Conclusion: Analyzing spontaneous report databases has several limitations, but is still a valuable tool for identifying potential associations between drugs and adverse events.  Spontaneous report databases may also be useful for detecting differences in adverse events between different races, countries and regions.