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<p>This study aims to mine and analyze adverse events (AEs) of argatroban based on the FAERS database to better understand its safety and potential risks in the real-world. Data from the first quarter of 2004 to the third quarter of 2024 were collected, and researchers employed various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). The study collected 54,336,884 reports of AEs, of which 2,233 were related to argatroban. Signal mining identified 110 Preferred Terms involving 25 system organ classes. There were most reports involving adults (≥ 65&#xa0;years), with a slightly higher proportion in men than women. We identified known AEs, including prolonged coagulation time, hemorrhage, abnormal hepatic function. New potential AE signals were identified, such as vascular pseudoaneurysm, retroperitoneal hematoma, gangrene, thrombotic thrombocytopenic purpura, acute cardiac failure, atrial septal defect, rhabdomyolysis. The median time to event was 2.00&#xa0;days, with the majority of AEs occurring within 30&#xa0;days. Analysis of the FAERS database identified argatroban-associated AEs, including newly identified potential risks with the aim of enhancing clinicians’ and pharmacists’ awareness of drug-related risk signals and facilitate timely preventive and treatment interventions for patient safety.</p> Graphical Abstract <p></p>

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Argatroban-Associated Adverse Events: An Analysis Based on FDA Adverse Event Reporting System (FAERS)

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Changyu Ren,
  • Guimu Guo,
  • YiXuan Wang,
  • Tingting Chen,
  • Jintuo Zhou,
  • Peiguang Niu,
  • Jinhua Zhang

摘要

This study aims to mine and analyze adverse events (AEs) of argatroban based on the FAERS database to better understand its safety and potential risks in the real-world. Data from the first quarter of 2004 to the third quarter of 2024 were collected, and researchers employed various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). The study collected 54,336,884 reports of AEs, of which 2,233 were related to argatroban. Signal mining identified 110 Preferred Terms involving 25 system organ classes. There were most reports involving adults (≥ 65 years), with a slightly higher proportion in men than women. We identified known AEs, including prolonged coagulation time, hemorrhage, abnormal hepatic function. New potential AE signals were identified, such as vascular pseudoaneurysm, retroperitoneal hematoma, gangrene, thrombotic thrombocytopenic purpura, acute cardiac failure, atrial septal defect, rhabdomyolysis. The median time to event was 2.00 days, with the majority of AEs occurring within 30 days. Analysis of the FAERS database identified argatroban-associated AEs, including newly identified potential risks with the aim of enhancing clinicians’ and pharmacists’ awareness of drug-related risk signals and facilitate timely preventive and treatment interventions for patient safety.

Graphical Abstract