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KMID : 1011320120050010040
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Journal of Pharmacoepidemiology and Risk Management
2012 Volume.5 No. 1 p.40 ~ p.45
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Analysis of Adverse Events Reporting Patterns and Signal Detection for Pediatric Patients in the Korean Spontaneous Reporting Data
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Kim Hye-Min
Seong Jong-Mi
Yang Bo-Ram
Jin Xue-Mei
Choi Nam-Kyong
Lee Joong-Yub
Lee Joong-Yub
Park Byung-Joo
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Abstract
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Objective: To describe the characteristics of the adverse event (AE) reports and detect signals for pediatric patients in the Korean spontaneous AE reporting database.
Methods: Among the reports from June 24, 2009 to December 31, 2010 from the Korean Regional Pharmacovigilance Centers (RPVC), the reports of children aged 0-17 were analyzed. Drugs and AEs were converted into anatomical therapeutic chemical (ATC) codes and the preferred term (PT) of World Health Organization-Adverse Reaction Terminology (WHO-ART), respectively. Children’s age, gender, the PT of AEs, and ATC codes of drugs were analyzed. Signals were detected by comparing a specific drug and all other drugs in the reports of pediatric patients using data mining analysis. A signal was defined as the AE detected by more than one index among proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). Among signals, serious and unexpected adverse events were identified according to information on serious events in the database and drug label information from Korean Food and Drug Administration (KFDA).
Results: Among 48,261 AE reports, 2,424 (5.0%) reports were from pediatric patients. The frequently reported drugswereamoxicillin and enzyme inhibitor 492 (9.5%), cytarabine 120 (2.3%), paracetamol 115 (2.2%). Rash 526 (15.8%), diarrhoea 355 (10.6%), nausea 298 (8.9%) were frequently reported AEs.Among 7,509 drug-AE combinations, 11 unexpected and serious adverse events were detected as signals.
Conclusion: This is the first study to analyze AE reporting pattern and to detect signals for pediatric patients through KFDA database. Further research on causality assessment for the detected signals will be needed.
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KEYWORD
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Spontaneous AE reports, Pediatric patients, Signal detection, Data mining
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