阿片类药物的流行已成为美国严重的国家危机。对阿片类药物相关不良事件(AE)进行深入的系统分析可以阐明阿片类药物暴露带来的风险,以及特定阿片类药物的个体风险概况和阿片类药物之间的潜在关系。在这项研究中,从所有食品和药物管理局(FDA)批准的药物清单中确定了92种阿片类药物,由RxNorm注释,分为13个阿片类药物组:丁丙诺啡,可待因,双氢可待因,芬太尼,氢可酮,氢吗啡酮,哌替啶,美沙酮,吗啡,羟考酮,羟吗啡酮,他他他多,还有曲马多.从2004年第1季度至2020年第3季度,从FDA不良事件报告系统(FAERS)检索并下载了总共14,970,399份不良事件报告。经过数据处理,然后应用经验贝叶斯几何平均值(EBGM),在13个阿片类药物组中识别出3317对潜在风险信号。基于这些潜在的安全信号,我们进行了一项比较分析,以提供FDA批准的所有13组处方阿片类药物的阿片类药物相关不良事件的全球概况.然后呈现每个阿片类药物类别的前10个报告最多的AE。同时进行网络分析和层次聚类分析以进一步探讨阿片类药物之间的关系。网络分析结果显示芬太尼之间有密切的联系,羟考酮,氢可酮,和氢吗啡酮,共有22个以上的AE。此外,在二氢可待因中常见的AE要少得多,哌替啶,羟吗啡酮,和他他他多.相反,通过比较存在/不存在AE的全部概况,层次聚类分析将13种阿片类药物进一步分为两组.网络分析和层次聚类分析的结果不仅相互一致和交叉验证,而且对13种阿片类药物之间的关联和关系提供了更好和更深入的理解。
The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (
AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported
AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22
AEs. In addition, much less commonly reported
AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of
AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.