A real-world pharmacovigilance study of Cilostazol based on the FDA Adverse Event Reporting System:A Cross-Sectional Disproportionality Analysis

BACKGROUND: Cilostazol is used for the treatment of intermittent claudication, but long-term efficacy and safety data are predominantly derived from case reports or clinical trials, with a notable absence of large-scale, real-world comprehensive safety data. In this study, we applied data mining algorithms to identify adverse drug reaction signals associated with cilostazol that are not currently listed on its drug label, with the objective of enhancing drug safety. METHODS: From 2004 to 2024, adverse event (AE) reports associated with cilostazol were examined in the Food and Drug Administration Adverse Event Reporting System (FAERS) database. For signal detection, three disproportionality metrics were calculated: the reporting odds ratio, the United Kingdom Medicines and Healthcare Products Regulatory Agency omnibus standard, and Bayesian Confidence Propagation Neural Network. The identified signals were compared against the adverse drug reactionslisted on the drug label. RESULTS: A total of 513 reports identified cilostazol as the primary suspect, with 774 AEs associated with cilostazol. Twenty-eight AE signals were detected that met the data mining criteria for reporting odds ratio, United Kingdom Medicines and Healthcare Products Regulatory Agency omnibus standard, and Bayesian Confidence Propagation Neural Network. These signals belong to 10 different system organ classifications (SOCs). The majority of the AEs were related to cardiac disorders and investigations. The detected signals were compared against the drug label, revealing 11 new signals not previously listed. Of these, six were included in the important medical event (IME) terms list, namely cerebral infarction, thrombosis in medical devices, ventricular fibrillation (VF), lacunar infarction, shock, and disseminated intravascular coagulation (DIC). CONCLUSION: This study offers critical support for the clinical monitoring and risk identification of cilostazol-related AEs.