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KMID : 1011320180100010016
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Journal of Pharmacoepidemiology and Risk Management
2018 Volume.10 No. 1 p.16 ~ p.23
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Signal Detection of Adverse Drug Reaction of Duloxetine Using the Korea Adverse Event Reporting System Database
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Kim Yong-Woo
Ko Young-Jin
Kim Seon-Ji
Park Byung-Joo
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Abstract
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Objective: To detect signals of adverse drug reactions (ADRs) after duloxetine intake using the Korea Adverse Events Reporting System (KAERS) database.
Methods: We used the KAERS database between 1989 and 2015. In KAERS database drugs and adverse events (AEs) are coded by the anatomical therapeutic chemical classification system and the preferred terms among the WHO adverse reaction terminology, respectively. We generated drug-AE pairs using reports completely informative on drug and AE. We detected signals of duloxetine by applying data mining procedure. We calculated proportional reporting ratio, reporting odds ratio, and information component. We defined a signal as any AE that satisfied the criteria of all the three indices. We compared detected signals and the labels of duloxetine among Korea, USA, UK, France, Germany, and Japan.
Results: The spontaneous AEs reported to the KIDS contained 860,224 cases during the study period. Among the total 3,012,517 drug-AE pairs, 3,890 pairs were associated with duloxetine. Forty-five AEs were detected as signals of duloxetine. Some of the signals, including medication error, hypotonia, breast cancer, ovarian cyst, and pelvic inflammation, were not labeled in the six countries.
Conclusion: Early detection of signals through post-marketing pharmacovigilance is crucial for patient safety, as it is difficult to identify all adverse drug reactions before marketing. We detected 5 unexpected signals associated with duloxetine by signal detection. Careful monitoring and further pharmacoepidemiologic research to evaluate the causality between the detected signals and duloxetine will be needed.
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KEYWORD
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Duloxetine, Korea Institute of Drug Safety & Risk Management, Korea Adverse Events Reporting System database, Data mining, Pharmacovigilance, Patient safety
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