Zhan Chen, Roughead Elizabeth, Liu Lin, Pratt Nicole, Li Jiuyong
School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia 5095, Australia.
School of Pharmacy and Medical Sciences, Quality Use of Medicines and Pharmacy Research Centre, Sansom Institute, University of South Australia, Adelaide, South Australia 5000, Australia.
Artif Intell Med. 2020 Apr;104:101839. doi: 10.1016/j.artmed.2020.101839. Epub 2020 Feb 27.
Adverse drug events (ADEs) may occur and lead to severe consequences for the public, even though clinical trials are conducted in the stage of pre-market. Computational methods are still needed to fulfil the task of pharmacosurveillance. In post-market surveillance, the spontaneous reporting system (SRS) has been widely used to detect suspicious associations between medicines and ADEs. However, the passive mechanism of SRS leads to the hysteresis in ADE detection by SRS based methods, not mentioning the acknowledged problem of under-reporting and duplicate reporting in SRS. Therefore, there is a growing demand for other complementary methods utilising different types of healthcare data to assist with global pharmacosurveillance. Among those data sources, prescription data is of proved usefulness for pharmacosurveillance. However, few works have used prescription data for signalling ADEs. In this paper, we propose a data-driven method to discover medicines that are responsible for a given ADE purely from prescription data. Our method uses a logistic regression model to evaluate the associations between up to hundreds of suspected medicines and an ADE spontaneously and selects the medicines possessing the most significant associations via Lasso regularisation. To prepare data for training the logistic regression model, we adapt the design of the case-crossover study to construct case time and control time windows for the extraction of medicine use information. While the case time window can be readily determined, we propose several criteria to select the suitable control time windows providing the maximum power of comparisons. In order to address confounding situations, we have considered diverse factors in medicine utilisation in terms of the temporal effect of medicine and the frequency of prescription, as well as the individual effect of patients on the occurrence of an ADE. To assess the performance of the proposed method, we conducted a case study with a real-world prescription dataset. Validated by the existing domain knowledge, our method successfully traced a wide range of medicines that are potentially responsible for the ADE. Further experiments were also carried out according to a recognised gold standard, our method achieved a sensitivity of 65.9% and specificity of 96.2%.
不良药物事件（ADEs）可能会发生，并给公众带来严重后果，即便在上市前阶段进行了临床试验。仍需要计算方法来完成药物监测任务。在上市后监测中，自发报告系统（SRS）已被广泛用于检测药物与ADEs之间的可疑关联。然而，SRS的被动机制导致基于SRS的方法在ADE检测中存在滞后性，更不用说SRS中公认的漏报和重复报告问题了。因此，越来越需要利用不同类型医疗保健数据的其他补充方法来协助全球药物监测。在这些数据来源中，处方数据已被证明对药物监测有用。然而，很少有研究使用处方数据来发出ADEs信号。在本文中，我们提出了一种数据驱动的方法，仅从处方数据中发现导致特定ADE的药物。我们的方法使用逻辑回归模型自动评估多达数百种可疑药物与一种ADE之间的关联，并通过套索正则化选择具有最显著关联的药物。为了准备用于训练逻辑回归模型的数据，我们调整了病例交叉研究的设计，构建病例时间和对照时间窗口以提取用药信息。虽然病例时间窗口可以很容易地确定，但我们提出了几个标准来选择合适的对照时间窗口，以提供最大的比较效力。为了解决混杂情况，我们从药物的时间效应和处方频率以及患者个体对ADE发生的影响等方面考虑了药物使用中的各种因素。为了评估所提出方法的性能，我们使用一个真实世界的处方数据集进行了案例研究。经现有领域知识验证，我们的方法成功追踪到了一系列可能导致ADE的药物。还根据公认的金标准进行了进一步实验，我们的方法灵敏度达到65.9%，特异性达到96.2%。
