Signal mining of adverse reactions associated with macrolide antibiotics in pediatric patients based on the FAERS database
10.12206/j.issn.2097-2024.202404031
- VernacularTitle:基于FAERS数据库对儿童应用大环内酯类抗菌药物的不良事件分析
- Author:
Zhenpo ZHANG
1
;
Jiaxin HE
2
;
Jingping ZHENG
1
;
Yuting WANG
1
;
Lin MA
3
;
Ling SU
1
Author Information
1. College of Pharmacy, Jinan University, Guangzhou 511436, China.
2. Guangdong Food and Drug Vocational College, Guangzhou 510520, China.
3. Faculty of Pharmacy, Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510120, China.
- Publication Type:Pharmacyadministration
- Keywords:
azithromycin;
clarithromycin;
erythromycin;
FAERS;
adverse events;
data mining
- From:
Journal of Pharmaceutical Practice and Service
2026;44(3):160-166
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the adverse event signals of children using macrolide drugs （azithromycin, clarithromycin, and erythromycin）, and provide reference for rational medicine use in clinical practice. Methods Data from children under 12 years old were extracted from the US FAERS database spanning from the first quarter of 2004 to the second quarter of 2023. The adverse drug reaction （ADR） signal mining for three macrolide antibiotics was conducted using the Reporting Odds Ratio （ROR） and Bayesian Confidence Propagation Neural Network （BCPNN） methods. Special emphasis was placed on analyzing and contrasting the differences in adverse events among the three drugs. Results A total of 1 615 reports for children under 12 years old were retrieved from the FAERS database, including 1 024 reports of azithromycin, 460 reports of clarithromycin, and 131 reports of erythromycin. Among azithromycin and erythromycin, there were more reports from boys than girls, while for clarithromycin, there were more reports from girls than boys. Oral administration was the most common route of administration for all three drugs. Regarding the outcome of adverse events reported, azithromycin and clarithromycin were primarily associated with other serious adverse events, whereas erythromycin was mainly associated with hospitalization and other serious adverse events. The number of adverse events reported decreased with increasing age, with a higher number of reports in the 0-3 age group. Using the ROR and BCPNN methods for signal detection, 86 signals were identified for azithromycin, 91 for clarithromycin, and 34 for erythromycin. These signals involved 22 System Organ Classes （SOCs）, with azithromycin mainly concentrated in skin and subcutaneous tissue disorders （n=21）, clarithromycin in gastrointestinal disorders （n=15）, and erythromycin in gastrointestinal disorders （n=8）. Twenty-four signals of moderate to high risk were detected, with 13 for azithromycin, 9 for clarithromycin, and 2 for erythromycin. Conclusion The adverse events induced by the three drugs with different risks in different systems. When clinically treating Mycoplasma pneumoniae pneumonia in children, the risk profiles of drugs in different systems should be considered, and personalized dosing should be implemented.
