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KMID : 1011320120050020061
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Journal of Pharmacoepidemiology and Risk Management
2012 Volume.5 No. 2 p.61 ~ p.67
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An Evaluation of a Data Mining Signal for Fentanyl Transdermal Patch in Korean Spontaneous Adverse Event Reporting System
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Ko Young-Jin
Kim Ji-Young
Seong Jong-Mi
Choi Nam-Kyong
Lee Joong-Yub
Park Byung-Joo
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Abstract
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Objective: To detect signals associated with fentanyl transdermal therapeutic system (TTS; patch) using spontaneous adverse events (AEs) reports.
Methods: We used the database on spontaneously reported AEs between June 2009 and December 2010 from Regional Pharmacovigilance Centers (RPVC). This data were reported by health professionals. Data mining was used to detect signals after using fentanyl patches. We generated drug-AE pairs of reports completely informative in drug and AE and calculated proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The AE which was detected by more than one index among PRR, ROR and IC was defined as a signal. We identified unexpected AEs based on the detected signals that were listed on the label or not. And we also detected serious adverse events (SAEs).
Results: The spontaneous AEs reported by RPVCs contained 48,260 cases from June 2009 and December 2010, and the number of cases attributed to fentanyl patch was 272. Among the total 153,556 drug-AE pairs, 689 drug-AE pairs were associated with fentanyl patch. Most frequently reported AEs were nausea (26.6%), dizziness (16.3%), and vomiting (11.9%). Forty AEs were detected as signals of fentanyl patch. Among them, 3 AEs including convulsion, dehydration, and paralytic ileus were not listed on the label. Fourteen cases of SAE were reported.
Conclusion: We detected 3 unexpected signals and 14 cases of SAE associated with fentanyl patch by data mining. Further investigation to evaluate the causality between the detected signals and fentanyl patch will be needed.
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KEYWORD
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Fentanyl, Transdermal patch, Spontaneous adverse events, Data mining
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