Ji Huanhuan, Gong Meiling, Gong Li, Zhang Ni, Zhou Ruiou, Deng Dongmei, Yang Ya, Song Lin, Jia Yuntao
Department of Pharmacy, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation base of Child development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
School of Pharmacy, Chongqing Medical University, Chongqing, China.
J Med Internet Res. 2025 Mar 25;27:e65872. doi: 10.2196/65872.
Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied.
This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future.
We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: Ω shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as "suspect," "interacting," or "concomitant drugs" in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature.
As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The Ω shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the Ω shrinkage measure model. The κ value was 0.972 (95% CI 0.942-1.002), and the P and P values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models.
Clinical evidence on DDIs is limited, and not all combinations of heart rate-corrected QT interval (QTc)-prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice.
尖端扭转型室性心动过速(TdP)是一种罕见但可能致命的心律失常,常由药物引起。药物相互作用(DDIs)是TdP发生的主要危险因素,但增加这种风险的具体药物组合尚未得到广泛研究。
本研究旨在识别具有临床意义的高优先级药物相互作用,为未来降低TdP风险和有效管理药物相互作用风险提供基础。
我们使用以下4种频率统计模型,通过美国食品药品监督管理局不良事件报告系统(FAERS)数据库检测药物相互作用信号:Ω收缩测量法、组合风险比、卡方统计量和相加模型。感兴趣的不良事件是TdP,目标药物均在FAERS中注册并分类为“可疑”“相互作用”或“伴随药物”。使用Lexicomp和Drugs.com数据库识别和评估药物相互作用信号,并辅以文献中的真实世界数据。
截至2023年9月,本研究纳入4313例TdP病例,有721种药物和4230种药物组合报告至少3例。Ω收缩测量法模型显示出最保守的信号检测,而卡方统计量模型在信号检测趋势上与Ω收缩测量法模型表现出最接近的相似性。κ值为0.972(95%CI 0.942 - 1.002),P和P值分别为0.987和0.985。我们使用4种频率统计模型检测到2158种组合,其中241种组合在Drugs.com或Lexicomp中有索引,105种组合在两者中均有索引。最常相互作用的药物是胺碘酮、西酞普兰、喹硫平、昂丹司琼、环丙沙星、美沙酮、艾司西酞普兰、索他洛尔和伏立康唑。最常见的组合是西酞普兰和喹硫平、胺碘酮和环丙沙星、胺碘酮和艾司西酞普兰、胺碘酮和氟西汀、环丙沙星和索他洛尔以及胺碘酮和西酞普兰。尽管有38种药物相互作用在Drugs.com和Lexicomp中有索引,但未被4种模型中的任何一种检测到。
关于药物相互作用的临床证据有限,并非所有校正心率的QT间期(QTc)延长药物组合都会导致TdP,即使涉及高风险药物或已知有TdP风险的药物。本研究提供了药物诱导TdP的全面真实世界概述,界定了具有临床意义的药物相互作用和阴性药物相互作用,为各种药物的安全性概况提供了有价值的见解,并为优化临床实践提供了参考。