Construction of an Active Monitoring Model for Adverse Events of Pediatric Antimicrobial Drugs Based on the China Hospital Pharmacovigilance System
10.12259/j.issn.2095-610X.S20250911
- VernacularTitle:基于中国医院药物警戒系统构建儿童抗菌药物不良事件主动监测模型
- Author:
Xiu JIN
1
;
Rui WU
;
Yongmei LIANG
;
Yanyan ZHOU
;
Mandi XU
;
Yanping LI
Author Information
1. 安宁市第一人民医院药学部,云南 安宁 650300
- Keywords:
Adverse drug events;
Children;
Antibacterial drugs;
Delphi method;
China hospital pharmacovigilance system
- From:
Journal of Kunming Medical University
2025;46(9):98-106
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop an active monitoring model for adverse drug events(ADEs)related to antimicrobial use in children based on the China hospital pharmacovigilance system(CHPS).Methods Trigger items for the active monitoring model were initially drafted through a review of relevant literature,adverse reaction databases,and drug label warnings,and subsequently refined using the Delphi method.A retrospective analysis was performed on pediatric inpatients who received antibiotics at Anning First People's Hospital between January 1 and December 31,2024.The detection rate and positive predictive value(PPV)of the active monitoring model were calculated and compared with spontaneous ADE reports from the same period.Risk factors for ADEs were further analyzed using logistic regression.Results 25 trigger items were established for the active monitoring model.Among 1,784 cases,233 ADEs were identified,yielding a detection rate of 13.06%(233/1,784).The spontaneous reporting rate of adverse events during the same period was 1.85%(33/1,784).The difference between the two was statistically significant(P<0.001).There were 727 positive trigger events,with 299 cases of ADE detected,resulting in an overall PPV of 41.13%(299/727).Logistic regression revealed that antibiotic use exceeding 3 days(OR=1.454,95%CI:1.012-2.088)was significantly associated with ADE occurrence.Conclusion Compared with conventional spontaneous reporting,the active monitoring model can significantly improve the detection rate of ADEs of pediatric antimicrobial drugs and achieve active and real-time monitoring of drug adverse events.
