@Article{info:doi/10.2196/65872,
author="Ji, Huanhuan
and Gong, Meiling
and Gong, Li
and Zhang, Ni
and Zhou, Ruiou
and Deng, Dongmei
and Yang, Ya
and Song, Lin
and Jia, Yuntao",
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",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="25",
volume="27",
pages="e65872",
keywords="torsades de pointes; FAERS database; drug-drug interactions; QTc-prolonging drugs; adverse drug events",
abstract="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: $\Omega$ 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 $\Omega$ 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 $\Omega$ shrinkage measure model. The $\kappa$ 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. ",
issn="1438-8871",
doi="10.2196/65872",
url="https://www.jmir.org/2025/1/e65872",
url="https://doi.org/10.2196/65872"
}