Ibrahim Heba, Saad Amr, Abdo Amany, Sharaf Eldin A
BioMedical Informatics Specialty, Department of Information Systems, Faculty of Computers and Information, Helwan University, Egypt.
Egyptian Pharmaceutical Vigilance Center (EPVC), Egyptian Drug Authority, Egypt; National Organization for Drug Control and Research (NODCAR), Egypt.
J Biomed Inform. 2016 Apr;60:294-308. doi: 10.1016/j.jbi.2016.02.009. Epub 2016 Feb 20.
Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori".
The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns.
Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among the elements which composed a pattern. The average performance of the method showed 85% precision, 80% negative predictive value, 81% sensitivity and 84% specificity. The LR modeling could provide the statistical context to guard against spurious DIAEs.
The proposed method could efficiently detect DIAE signals from SRS data as well as, identifying rare adverse drug reactions (ADRs).
药物警戒（PhV）是一项重要的临床活动，对人群健康和临床研究具有重大影响。药物警戒的主要目标是及时发现临床性质、严重程度和/或频率方面具有新颖性的药物不良事件（ADEs）。药物相互作用（DI）在新药研发和上市后药物警戒中是一个重要问题，占所有意外药物不良事件的6% - 30%。因此，早期发现药物相互作用至关重要。自20世纪60年代以来，自发报告系统（SRS）一直是上市后药物警戒的核心数据收集系统。我们研究的主要目的是特别从自发报告系统中识别药物相互作用的信号。此外，我们还提出了一种名为“混合Apriori”的优化定制挖掘算法。
所提出的算法基于一种优化和改进的关联规则挖掘（ARM）方法。一种混合Apriori算法已应用于美国食品药品监督管理局（U.S. FDA）不良事件报告系统（FAERS）的自发报告系统，以提取药物相互作用 - 不良事件（DIAE）的显著关联模式。我们使用两种不同的分类特征对所得的药物相互作用不良事件进行了定性和定量评估：一种三元分类法和三个性能指标。这些特征应用于100个相互作用和100个非相互作用的药物相互作用不良事件模式的两个随机样本。此外，我们采用逻辑回归（LR）统计方法来量化相互作用的大小和方向，以测试未知相互作用的药物相互作用不良事件模式中合并用药的混杂情况。
混合Apriori算法提取了2933个相互作用的药物相互作用不良事件模式（包括1256个严重模式）和530个非相互作用的药物相互作用不良事件模式。参考使用四种不同可靠药物相互作用资源的现有知识，结果表明所提出的方法可以提取严重相互作用的药物相互作用不良事件的信号。基于构成一个模式的元素之间的关系，可以识别各种关联模式。该方法的平均性能显示精度为85%，阴性预测值为80%，灵敏度为81%，特异性为84%。逻辑回归建模可以提供统计背景以防范虚假的药物相互作用不良事件。
所提出的方法可以有效地从自发报告系统数据中检测药物相互作用不良事件信号，并识别罕见的药物不良反应（ADRs）。
