Acton Emily K, Hennessy Sean, Brensinger Colleen M, Bilker Warren B, Miano Todd A, Dublin Sascha, Horn John R, Chung Sophie, Wiebe Douglas J, Willis Allison W, Leonard Charles E
Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Front Pharmacol. 2022 May 10;13:845485. doi: 10.3389/fphar.2022.845485. eCollection 2022.
Growing evidence suggests that drug interactions may be responsible for much of the known association between opioid use and unintentional traumatic injury. While prior research has focused on pairwise drug interactions, the role of higher-order (i.e., drug-drug-drug) interactions (3DIs) has not been examined. We aimed to identify signals of opioid 3DIs with commonly co-dispensed medications leading to unintentional traumatic injury, using semi-automated high-throughput screening of US commercial health insurance data. We conducted bi-directional, self-controlled case series studies using 2000-2015 Optum Data Mart database. Rates of unintentional traumatic injury were examined in individuals dispensed opioid-precipitant base pairs during time exposed vs unexposed to a candidate interacting precipitant. Underlying cohorts consisted of 16-90-year-olds with new use of opioid-precipitant base pairs and ≥1 injury during observation periods. We used conditional Poisson regression to estimate rate ratios adjusted for time-varying confounders, and semi-Bayes shrinkage to address multiple estimation. For hydrocodone, tramadol, and oxycodone (the most commonly used opioids), we examined 16,024, 8185, and 9330 drug triplets, respectively. Among these, 75 (0.5%; hydrocodone), 57 (0.7%; tramadol), and 42 (0.5%; oxycodone) were significantly positively associated with unintentional traumatic injury (50 unique base precipitants, 34 unique candidate precipitants) and therefore deemed potential 3DI signals. The signals found in this study provide valuable foundations for future research into opioid 3DIs, generating hypotheses to motivate crucially needed etiologic investigations. Further, this study applies a novel approach for 3DI signal detection using pharmacoepidemiologic screening of health insurance data, which could have broad applicability across drug classes and databases.
越来越多的证据表明，药物相互作用可能是阿片类药物使用与非故意伤害之间已知关联的主要原因。虽然先前的研究集中在两两药物相互作用上，但高阶（即药物 - 药物 - 药物）相互作用（3DIs）的作用尚未得到研究。我们旨在通过对美国商业健康保险数据进行半自动高通量筛选，识别阿片类药物3DIs与通常联合配药的药物导致非故意伤害的信号。我们使用2000 - 2015年Optum数据集市数据库进行了双向、自我对照的病例系列研究。在暴露于候选相互作用沉淀剂与未暴露于候选相互作用沉淀剂的时间段内，对使用阿片类药物 - 沉淀剂碱基对的个体的非故意伤害发生率进行了检查。基础队列包括16至90岁新使用阿片类药物 - 沉淀剂碱基对且在观察期内至少发生1次伤害的个体。我们使用条件泊松回归来估计针对随时间变化的混杂因素进行调整的率比，并使用半贝叶斯收缩来处理多重估计。对于氢可酮、曲马多和羟考酮（最常用的阿片类药物），我们分别检查了16,024、8185和9330个药物三联体。其中，75个（0.5%；氢可酮）、57个（0.7%；曲马多）和42个（0.5%；羟考酮）与非故意伤害显著正相关（50种独特的碱基沉淀剂，34种独特的候选沉淀剂），因此被视为潜在的3DI信号。本研究中发现的信号为未来阿片类药物3DIs的研究提供了有价值的基础，产生了假设以推动急需的病因学调查。此外，本研究应用了一种使用健康保险数据的药物流行病学筛查进行3DI信号检测的新方法，该方法可能在药物类别和数据库中具有广泛的适用性。
