Study of high-risk agents for drug-induced kidney injury:monitoring data of 10-year spontaneous reports in Wuhan Municipality
OBJECTIVE To explore the clinical characteristics and occurrence patterns of drug-induced kidney injury(DIKI)in Wuhan Municipality and identify the risk signals of kidney injury caused by high-frequency agents to provide references for rational use of drugs in clinical practices.METHODS For this retrospective study,all DIKI-related reports were collected from Wuhan ADR spontaneous reporting system database during 2012 to 2022.Demographic profiles,distribution/type of drugs and clinical manifestations were recorded with descriptive statistics.Risk signal mining was further conducted with reporting odds ratio(ROR)and proportional reporting ratio(PRR)through the databases of Medicines and Healthcare Products Regulatory Agency(MHRA)and Bayesian Confidence Propagation Neural Network(BCPNN).RESULTS A total of 1 504 DIKI cases were obtained with an annual increase in the number of cases.The degree of DIKI was mostly"general"and"serious"accounted for 38.43％.The subjects predominated in 60-79 age group with a male-to-female ratio of 1.58∶1.A large majority of DIKI reports occurred at Days 2-7 post-dosing.A total of 19 categories and 417 types of drugs were involved.The most frequently reported drugs were antibiotics(27.73％),hematological agents(12.50％)and urinary system drugs(8.31％).Top three drugs were scopolamine,mannitol and vancomycin.Further data mining revealed eight drugs with strong positive risk signals for kidney injury.CONCLUSION DIKI is associated with a large variety of drugs.Antibiotics are the most frequent.Due to diverse clini-cal manifestations of DIKI,high-risk senior males should be closely monitored.Drugs should be dosed strictly according to the indications and doses of package insert.And kidney functions shall be watched for minimizing the occurrence of DIKI in high-risk populations.
drug-induced kidney injuryadverse drug eventspontaneous reporting systempharmacovigilancedata mining
NETL
NSTL
万方数据
