Analysis of 2 642 reports of adverse reactions related to carbapenems and risk signal mining
Objective To understand the incidence and characteristics of adverse drug reactions(ADRs)of carbapenems,explore the relevant risk signals,and provide a reference for clinically safe drug use.Methods All spontaneous reports of carbapenem drug-related ADRs from January 2008 to October 2022 in the Adverse Drug Reaction Monitoring Center,PLA General Hospital's ADR database were retrieved,and information such as patients'general conditions,involved systems and organs damage,and the names of ADRs involved were retrospectively analysed.Using the reporting odd ratio method,the proportional reporting ratio method,the Medicines and Healthcare Products Regulatory Agency method,and information component method to obtain risk signals of carbapenem antimicrobial drug-related ADR.Results A total of 2 642 ADR reports of carbapenems were reported,of which 410 serious ADR reports(15.52％)were serious ADR reports,five cabapenem antimirobial drug species were mainly involved.In descending order of composition were imipenem cilastatin(51.28％),meropenem(32.13％),biapenem(8.10％),ertapenem(7.68％),and panipenem(0.79％).The male to female ratio of patients was 1.74:1,with the most age＞60 years(59.69％).A total of 14"drug-ADR name"combinations generated risk signals in all four data mining methods,with meropenem being the most signals,and imipenem cilastatin and ertapenem had a high number of reported ADR in nervous system.Conclusion The results of risk signal mining are basically consistent with the known carbapenem ADR information,during the use of carbapenem antimicrobial drugs in the clinic,it is recommended to monitor patients'liver and kidney functions as well as blood biochemical indexes,so as to strengthen the awareness of vigilance in the clinical use of carbapenem antimicrobial drugs,and timely recognize and deal with ADRs in a timely manner,and to avoid the occurrence of serious ADRs.
CarbapenemsAntibacterial drugsAdverse drug reactionsData miningRetrospective analysis
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