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A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs

Zhao, Z.; Liu, R.; Wang, L.; Li, L.; Song, C.; Zhang, P.

2022-01-14 health informatics
10.1101/2022.01.07.22268907 medRxiv
Show abstract

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveil-lance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at https://github.com/Zhizhen-Zhao/Age-Risk-Identification

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