Background: This study aimed to adopt the conventional signal detection methods to explore a new way of risk identification and to mine important drug risks from the perspective of big data based on Zhenjiang Adverse Event Reporting System (ZAERS).
Research design and methods: Data were extracted from ZAERS database between 2012 and 2022. The risks of all the reported drug event combinations were identified at the preferred term level and the standardized MedDRA query level using disproportionality analysis. Then, we conducted signal assessment according to the descriptions of drug labels.
Results: In total 41,473 ADE were reported and there were 12 risky signals. Signal assessment indicates the suspected causal associations in clindamycin-taste and smell disorders, valsartan-hepatic enzyme increased and valsartan-edema peripheral; the specific manifestations of allergic reactions triggered by clindamycin, cefotaxime, cefazodime, ShexiangZhuanggu plaster, ShexiangZhuifeng plaster, and Yanhuning need to be refined in drug labels. In addition, the drug labels of NiuHuangShangQing tablet/capsule, Fuyanxiao capsule, and BiYanLing tablet should be improved.
Conclusions: In this study, we attempted a new way to find potential drug risks using small spontaneous reporting data. Our findings also suggested the need for more precise identification of allergic risks and the improvement of traditional Chinese medicine labels.
Keywords: Pharmacovigilance; data mining; drug safety; signal detection; standardized MedDRA query.