Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database

Qinglan PENG; Jialu LUO; Jiayi YU; Kaixin WANG; Fang WU; Chuanzhi XU

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Qinglan PENG, Jialu LUO, Jiayi YU, Kaixin WANG, Fang WU, Chuanzhi XU. Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database[J]. Journal of Kunming Medical University.

Citation:

Qinglan PENG, Jialu LUO, Jiayi YU, Kaixin WANG, Fang WU, Chuanzhi XU. Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database[J]. Journal of Kunming Medical University.

Qinglan PENG, Jialu LUO, Jiayi YU, Kaixin WANG, Fang WU, Chuanzhi XU. Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database[J]. Journal of Kunming Medical University.

Citation:

Qinglan PENG, Jialu LUO, Jiayi YU, Kaixin WANG, Fang WU, Chuanzhi XU. Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database[J]. Journal of Kunming Medical University.

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Signal Mining and Analysis of Adverse Events of Azithromycin Based FAERS Database

Qinglan PENG^{1, 2},
Jialu LUO¹,
Jiayi YU¹,
Kaixin WANG¹,
Fang WU²,
Chuanzhi XU^{3, 4
,
,}

1.
College of Medicine and Health Sciences
2.
Institute of Dermatology and Venereology，China Three Gorges University，Yichang Hubei 443000
3.
School of Public Health，Kunming Medical University，Kunming Yunnan 650500
4.
Key Laboratory of Public Health & Disease Prevention and Control of Yunnan Provincial Education Department，Kunming Yunnan 650500，China

Received Date: 2024-03-20
Available Online: 2024-07-08

Abstract

Abstract

Objective To explore and analyze adverse drug events （ADE） signals of azithromycin, identify common adverse events and suspected adverse reactions that occur easily in the real clinical application of azithromycin. Methods The adverse drug event reports related to azithromycin recorded in the US FDA Adverse Event Reporting System database from October 1, 2003, to October 1, 2023, were mined using the OpenVigil 2.1 drug surveillance platform. Signal detection and analysis were carried out using the reporting odds ratio （ROR） and Bayesian confidence propagation neural network method （BCPNN）. Results A total of 5498 ADE signals were identified, with a total of 112485 reports. Eventually, 31, 041 azithromycin-associated adverse event reports were extracted, involving 898 positive signals, 27 system organ classes, mainly concentrated in respiratory system diseases, infections, gastrointestinal system diseases, and others. Additionally, some adverse reactions possibly caused by azithromycin were discovered, such as asthma, wheezing, and potential reactions like incomplete abortion. Conclusion When using azithromycin, besides paying attention to the adverse reactions listed in the drug instructions, we should also pay attention to some adverse reactions that have not been timely recorded or updated, in order to prevent the medication safety risks caused by outdated instructions, provide reasonable medication advice, and ensure the medication safety of patients.
- Azithromycin,
- Adverse drug events,
- FDA adverse event reporting system,
- Data mining and analysis data mining

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References(21)

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