Huanhuan Ji, Meiling Gong, Li Gong, Ni Zhang, Ruiou Zhou, Dongmei Deng, Ya Yang, Lin Song, Yuntao Jia
{"title":"致死性扭转中具有临床意义的药物-药物相互作用的检测:食品和药物管理局不良事件报告系统的歧化分析。","authors":"Huanhuan Ji, Meiling Gong, Li Gong, Ni Zhang, Ruiou Zhou, Dongmei Deng, Ya Yang, Lin Song, Yuntao Jia","doi":"10.2196/65872","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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<sub>positive</sub> and P<sub>negative</sub> 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e65872"},"PeriodicalIF":5.8000,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Clinically Significant Drug-Drug Interactions in Fatal Torsades de Pointes: Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System.\",\"authors\":\"Huanhuan Ji, Meiling Gong, Li Gong, Ni Zhang, Ruiou Zhou, Dongmei Deng, Ya Yang, Lin Song, Yuntao Jia\",\"doi\":\"10.2196/65872\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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<sub>positive</sub> and P<sub>negative</sub> 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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":16337,\"journal\":{\"name\":\"Journal of Medical Internet Research\",\"volume\":\"27 \",\"pages\":\"e65872\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Internet Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/65872\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Internet Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/65872","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
背景:点扭转(TdP)是一种罕见但具有潜在致命性的心律失常,通常由药物引起。药物-药物相互作用(ddi)是TdP发展的主要危险因素,但增加这种风险的特定药物组合尚未得到广泛研究。目的:本研究旨在识别具有临床意义的高优先级DDI,为今后降低TdP风险和有效管理DDI风险提供基础。方法:采用美国食品药品监督管理局不良事件报告系统(FAERS)数据库中的4种频率统计模型:Ω收缩测量、组合风险比、卡方统计和加性模型检测DDI信号。关注的不良事件为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DDI信号,并辅以文献中的真实数据。结果:截至2023年9月,本研究共纳入TdP病例4313例,报告至少3例的药物有721种,联合用药有4230种。Ω收缩率测度模型的信号检测最保守,而卡方统计模型与Ω收缩率测度模型的信号检测趋势最相似。κ值为0.972 (95% CI 0.942 ~ 1.002), p阳性和p阴性值分别为0.987和0.985。使用4种频率统计模型共检测到2158种组合,其中241种组合被Drugs.com或Lexicomp检索,105种组合被两者检索。最常见的相互作用药物是胺碘酮、西酞普兰、喹硫平、昂丹西琼、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与喹硫平、胺碘酮与环丙沙星、胺碘酮与依西酞普兰、胺碘酮与氟西汀、环丙沙星与索他洛尔、胺碘酮与西酞普兰。虽然Drugs.com和Lexicomp检索了38个ddi,但4种模型均未检测到它们。结论:ddi的临床证据有限,并不是所有的心率校正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TdP风险的药物。本研究提供了药物诱导TdP的全面现实概述,划分了临床显著ddi和阴性ddi,为各种药物的安全性提供了有价值的见解,并为临床实践的优化提供了信息。
Detection of Clinically Significant Drug-Drug Interactions in Fatal Torsades de Pointes: Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System.
Background: 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.
Objective: 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.
Methods: 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.
Results: 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 Ppositive and Pnegative 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.
Conclusions: 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.
期刊介绍:
The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades.
As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor.
Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.