Abstract
BACKGROUND: Peripheral nerve palsy is a prevalent clinical condition that significantly impairs patients' quality of life. To advance clinical practice and mitigate the risk of drug-induced peripheral nerve palsy, this study aimed to identify adverse drug reaction signals related to peripheral nerve palsy through data mining of the FDA Adverse Event Reporting System (FAERS). Timely detection of high-risk medications provides a crucial basis for enhancing the safety of clinical drug use. METHODS: Adverse events (AEs) related to peripheral nerve palsy between 2004 and the third quarter of 2024 were extracted from the FAERS database. To identify potential drug safety signals associated with peripheral nerve palsy, four established pharmacovigilance statistical methods were employed: the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-Item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). RESULTS: A total of 5,787 reports of drug-associated peripheral nerve palsy adverse events involving 1,141 drugs were identified in this analysis. Among these, natalizumab had the highest number of reported cases, followed by interferon beta-1a and dalfampridine. The most commonly implicated drug classes were antineoplastic drugs and immunosuppressants. A total of 30 drugs exhibited positive risk signals, of which 19 met the criteria for a positive signal across all four analytical algorithms. Notably, both the ROR and BCPNN methods indicated that bupivacaine, dalfampridine, natalizumab, minocycline, and ocrelizumab are among the high-risk drugs associated with peripheral nerve palsy. CONCLUSION: This study identified that several medications, including bupivacaine, dalfampridine, natalizumab, minocycline, and ocrelizumab, are significantly associated with an increased risk of peripheral nerve palsy. These findings contribute to a deeper understanding of drug safety profiles, support the promotion of rational medication use, and provide valuable insights to inform clinical decision-making.