Mining pharmacovigilance signals for asthma adverse events in underage population based on the FAERS database

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Author: CHEN Yan ¹ LI Bing ¹ LIU Xia ² LIU Hongyan ¹ SHAN Qing ¹  GUO Jinmin ¹

Affiliation: 1. Department of Clinical Pharmacy, the 960th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army, Jinan 250031, China 2. Department of Clinical Pharmacy, School of Pharmacy, Naval Medical University, Shanghai 200433, China

Keywords: Asthma Underage population FAERS database Data mining Pharmacovigilance

DOI: 10.12173/j.issn.1005-0698.202305076

Reference: CHEN Yan, LI Bing, LIU Xia, LIU Hongyan, SHAN Qing, GUO Jinmin.Mining pharmacovigilance signals for asthma adverse events in underage population based on the FAERS database[J].Yaowu Liuxingbingxue Zazhi,2024, 33(1):28-36.DOI: 10.12173/j.issn.1005-0698.202305076.[Article in Chinese]  Copied

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Abstract

Objective To conduct data mining of asthma-inducing medications in underage populations based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database, so as to provide reference for the clinical application of related medications.

Methods Adverse drug event (ADE) reports from the first quarter of 2013 to the fourth quarter of 2022 in the FAERS database were collected and screened for reports of asthma adverse events in the this population (under 18 years old), which were categorized into infants, toddlers, children, and adolescents according to different age groups, and were subjected to medication signal mining by using the reporting odds ratio method, the composite standardized method, and the information component method.

Results A total of 1 915 reports were obtained after screening, involving 1 042 (54.41%) males and 831 (43.39%) females; the highest percentage of the reporting population was between 12 and under 18 years old, with a total of 762 (39.79%); 60.78% of the reports were reported by health professionals; and the results of the clinical referrals showed that serious adverse events occurred in 85.90% of the cases. 306 suspected drugs were screened, 52 drugs were determined to be valid signals, and 1 044 adverse events were reported, of which 16 drug inserts did not mention the risk of asthma, in order of elosulfatase alpha, canakinumab, tobramycin, vancomycin, ceftriaxone, cetirizine, phenylephrine, imiglucerase, cefuroxime, betamethasone, atropine, tadalafil, riscovastatin, cyclophosphamide, octreotide, and omeprazole.

Conclusion The FAERS database was mined for adverse drug event signals and evaluated using the proportional disequilibrium method to identify 16 medicines that may trigger pharmacogenetic asthma and are not documented in the specification, which can be used to provide a good early warning for the clinic. At the same time, focusing on special populations, strengthening the assessment of lung function before medication and monitoring during and after medication, timely interventions were taken to reduce the harm of drug-derived adverse reactions and ensure the safe use of medication.

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