Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of Pharmaceutical Sciences, Kitasato University, 5-9-1, Shirogane, Minato-ku, Tokyo, 108-8641, Japan.
EPS Corporation, 6-29, Shin-ogawachou, Shinjuku-ku, Tokyo, 162-0814, Japan.
Ther Innov Regul Sci. 2021 Jul;55(4):685-695. doi: 10.1007/s43441-021-00265-0. Epub 2021 Mar 15.
This study aimed to identify factors that influence the decision to take safety regulatory actions in routine signal management based on spontaneous reports. For this purpose, we analyzed the safety signals identified from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and related information.
From the signals that the FDA identified in the FAERS between 2008 1Q and 2014 4Q, we selected 216 signals for which regulatory action was or was not taken. Characteristics of the signals were extracted from the FAERS quarterly reports that give information about what signals were identified from the FAERS and what actions were taken for them, and the FAERS data released in the same quarter when the signal was published. Univariate and multivariable logistic regression analysis was used to assess the relationship between the characteristics of each of the signals and the decision on regulatory action.
As a result of the univariate logistic regression analysis, we selected 5 factors (positive rechallenge, number of cases accumulated in the last one-year period before the signal indication, previous awareness, serious outcome, risk for special populations) to include in the multivariable logistic regression model (p < 0.2). The multivariate logistic regression analysis showed that the number of cases accumulated in the last one-year period before the signal indication and previous awareness were associated with the regulatory action (p < 0.05).
The present study showed that number of cases accumulated in the last one-year period before the signal indication and previous awareness potentially associated with the United States regulatory action. When assessing safety signals, we should be careful of the adverse events with a large number of cases accumulated rapidly in a short period. In addition, we should pay attention to new information on not only unknown risks but also previously identified and potential risks.
本研究旨在根据自发报告识别影响常规信号管理中采取安全监管措施决策的因素。为此，我们分析了从美国食品和药物管理局（FDA）不良事件报告系统（FAERS）中识别出的安全性信号及其相关信息。
从 2008 年 1 季度至 2014 年 4 季度 FDA 在 FAERS 中识别出的信号中，我们选择了 216 个采取或未采取监管行动的信号。从 FAERS 季度报告中提取信号的特征，该报告提供了从 FAERS 中识别出哪些信号以及对这些信号采取了哪些行动的信息，以及在发布信号的同一季度发布的 FAERS 数据。采用单变量和多变量逻辑回归分析评估每个信号的特征与监管行动决策之间的关系。
单变量逻辑回归分析的结果，我们选择了 5 个因素（阳性再激发、信号指示前最后一年期间积累的病例数、先前意识、严重后果、特殊人群风险）纳入多变量逻辑回归模型（p<0.2）。多变量逻辑回归分析表明，信号指示前最后一年期间积累的病例数和先前意识与监管行动相关（p<0.05）。
本研究表明，信号指示前最后一年期间积累的病例数和先前意识可能与美国监管行动相关。在评估安全性信号时，我们应该小心在短时间内快速积累大量病例的不良事件。此外，我们应该注意不仅是未知风险，还有先前确定的和潜在风险的新信息。
