ABSTRACT
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.
Abbreviations
ADE | = | Adverse Drug Event |
ADR | = | Adverse Drug Reaction |
ARB | = | Angiotensin Receptor Blocker |
BCPNN | = | Bayesian Confidence Propagation Neural Network |
CI | = | Confidence Interval |
DEC | = | Drug Event Combination |
FAERS | = | Food and Drug Administration Adverse Event Reporting System |
GVP | = | Good Pharmacovigilance Practice |
HLT | = | High Level Term |
IC | = | Information Component |
IQR | = | Interquartile Range |
JADER | = | Japanese Adverse Drug Event Report |
MedDRA | = | Medical Dictionary for Regulatory Activities |
MHRA | = | Medicines and Healthcare Products Regulatory Agency |
NMPA | = | National Medical Products Administration |
PT | = | Preferred Term |
PRR | = | Proportional Reporting Ratio |
ROR | = | Reporting Odds Ratio |
SD | = | Standard Deviation |
SMQ | = | Standardized MedDRA Query |
SOC | = | System Organ Class |
SRS | = | Spontaneous Reporting System |
TCM | = | Traditional Chinese Medicine |
ZAERS | = | Zhenjiang Adverse Event Reporting System |
Declaration of interests
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Author contributions
Y Liu and N Wang provided data collection assistance. Y Liu and X Xu wrote the first draft of the manuscript and conducted the statistical analysis. Y Liu, X Xu and P Liu were involved in the conception of the study. J Yang, Y Zhang, M He, W Liao and N Wang analyzed the data. All authors were involved in the study design, interpretation of results, and critical review of the manuscript. All authors read and approved the final version.
Availability of data and materials
The database generated and analyzed during the current study doesn’t have an official download link, but it can be exported through the internal website of relevant partners (such as hospitals, etc.). If there is a need for access, anyone can contact the first author or corresponding author to obtain the data.
Ethics approval
As the present study used secondary healthcare data, it was exempted from review by the ethics committee, and informed consent from patients was not required.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2023.2288143.