Guan Yuyao, Ji Lei, Zheng Lei, Yang Jing, Qin Yizhuo, Ding Ning, Miao Ting, Liu Xuemei
Department of Pharmacy, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Shandong Provincial Key Laboratory of Applied Microbiology, Ecology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Front Pharmacol. 2022 Oct 4;13:892503. doi: 10.3389/fphar.2022.892503. eCollection 2022.
Adverse drug reaction (ADR) signal mining is essential for assessing drug safety. However, the currently available methods for this are rather cumbersome. We aimed to develop a drug risk analysis and assessment system using Java language and conduct pharmacovigilance data mining for fluoroquinolones at our hospital. We used ADR data reported by Shandong Provincial Third Hospital between July 2007 and August 2021. The signal detection methods included proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), Medicines and Healthcare products Regulatory Agency (MHRA). The BCPNN method was used as the reference standard for comparing the remaining three signal detection methods based on sensitivity, specificity, positive predictive value, negative predictive value, and Jorden index. The hospital database contained a total of 2,621 ADR reports, among which 263 were attributed to fluoroquinolones. There were 391 fluoroquinolone-ADR pairs. Using the PRR, ROR, MHRA, and BCPNN method, we detected 13 signals, 13 signals, 10 signals, and 11 weak signals, respectively. After signal detection, levofloxacin and moxifloxacin were shown to induce high risk signals for mental and sleep disorders, with the signal intensity of moxifloxacin being the most significant. Compared with BCPNN, the PRR and ROR methods showed better sensitivity, whereas the MHRA method showed better specificity. We developed a drug risk analysis and assessment system that can help hospitals and other medical institutions to detect and analyse ADR signals in the self-reporting system database, and thus improve drug safety. Further, it indicates that the central nervous system damage caused by fluoroquinolones should be monitored closely, and thus provides a reference for the clinical application of these drugs.
药物不良反应（ADR）信号挖掘对于评估药物安全性至关重要。然而，目前可用的方法相当繁琐。我们旨在使用Java语言开发一个药物风险分析和评估系统，并在我院对氟喹诺酮类药物进行药物警戒数据挖掘。我们使用了山东省立第三医院2007年7月至2021年8月期间报告的ADR数据。信号检测方法包括比例报告比（PRR）、报告比值比（ROR）、贝叶斯置信传播神经网络（BCPNN）、药品和保健产品监管局（MHRA）。BCPNN方法被用作参考标准，基于灵敏度、特异度、阳性预测值、阴性预测值和约登指数来比较其余三种信号检测方法。医院数据库共有2621份ADR报告，其中263份归因于氟喹诺酮类药物。有391对氟喹诺酮-ADR。使用PRR、ROR、MHRA和BCPNN方法，我们分别检测到13个信号、13个信号、10个信号和11个弱信号。信号检测后，左氧氟沙星和莫西沙星显示出引发精神和睡眠障碍的高风险信号，其中莫西沙星的信号强度最为显著。与BCPNN相比，PRR和ROR方法显示出更好的灵敏度，而MHRA方法显示出更好的特异度。我们开发了一个药物风险分析和评估系统，该系统可以帮助医院和其他医疗机构在自我报告系统数据库中检测和分析ADR信号，从而提高药物安全性。此外，这表明应密切监测氟喹诺酮类药物引起的中枢神经系统损害，从而为这些药物的临床应用提供参考。
