Signal mining and risk analysis of Alprazolam adverse events based on the FAERS database

Abstract

Abstract This study aims to evaluate the safety of Alprazolam by analyzing the FAERS database, provide data analysis for monitoring adverse drug reactions. This research encompasses adverse event (AE) reports related to Alprazolam from the first quarter of 2004 to the second quarter of 2023. Four signal mining and analysis methods were utilized, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). Further exploration was conducted regarding patient characteristics and types of AEs. A total of 23,575 AE reports in which Alprazolam was the primary suspect drug were collected, identifying 347 Preferred Term (PT) signals and 27 System Organ Classes (SOCs). The number of AE reports increased annually, especially in 2015, 2018, 2019, and 2020. The main affected groups were females and the age range of 18 to 45. Psychiatric disorders, Nervous system disorders, and Gastrointestinal disorders were the most common the organ system in which the AEs occurred. There is a certain risk of drug abuse and suicide with Alprazolam. Most notably, several AEs not recorded in the Alprazolam leaflet appeared among the top 30 PTs in signal strength, including but not limited to Benzodiazepine drug level abnormal, Acquired amegakaryocytic thrombocytopenia, Cutaneous T-cell dyscrasia, and Coronary No-reflow Phenomenon. For the first time, AEs related to the cardiovascular system and platelet function were unveiled. The severe AE reports that resulted in "hospitalization" and "death" accounted for 30.96% and 21.86%. This study highlights the risks of suicide and misuse of Alprazolam. Other potential severe or fatal AEs, such as those related to the cardiovascular system, platelet function, and others, require further research to determine their precise mechanisms and risk factors.

Keywords

• Alprazolam
• FAERS
• Real-world data analysis
• Adverse events
• Adverse drug reaction