Significant high order drug interactions in the emergency department setting among older patient population

老年人(≥65岁)约占美国人口的17%，但约占总人口的57% 使用≥3药物的人(多药房)。目前，约有3000万美国成年人使用≥3药物，这会增加他们的暴露 到高阶药物-药物相互作用(HDDI)，即涉及≥3药物的药物-药物相互作用。HDDI是一个主要风险 导致急诊科就诊或住院的严重不良药物事件(ADE)的因素。 与年轻人相比，老年人住院的可能性是7倍，住院的可能性是2到3倍 去教育署参加ADE。然而，关于如何确定HDDI-ADE关联的知识、关于 不同三联用药的安全性比较及临床和药代动力学机制 ADE协会都受到严格的限制。目前的项目旨在通过以下方式解决这些主要差距 具体地说：(1)应用我们最先进的数据挖掘技术来发现与以下各项相关的HDDI 较高的不良反应风险以及与较低的不良反应风险相关的风险；(2)进行相对安全 对涉及抗凝剂、抗糖尿病药物和阿片类药物的3种药物组合进行评估；以及(3)验证 评价HDDI-ADE联合用药的临床有效性和药理作用机制。 这些目标将通过重点关注在现实世界基础上进行急诊的老年患者来实现 来自医疗保险索赔(MarketScan和Medicare)和电子健康记录(EHR)数据的数据。我们 将重点关注胃肠道(GI)出血、低血糖和阿片类药物诱导的ADE作为主要的ADE 对所有这三个目标的兴趣。这三种ADE占所有ADE诱导的ED就诊的60%，是一个目标 美国卫生与公众服务部优先进行预防和监测。我们的初步数据来自 仅MarketScan声称的数据(2012-2018年)就证实，老年人患ADE的风险更高，而且 这些不良反应的风险随着患者使用的伴随药物的数量而增加。我们会把我们的混合物 药物计数反应(MDCR)和图形模型，以确定具有以下特性的特定3或4种药物组合 老年人在ED环境中发生ADE的风险最高和风险最低(目标1)。对于目标2，我们 将应用药物流行病学研究设计和随机性推断方法来评估比较 特定的高风险和低风险3种药物组合的安全性。我们将控制潜在的混杂影响 几个患者级别(人口统计、临床、慢性合并症、药物特征、医疗保健利用等) 以及提供者/医疗保健系统级别的因素，以评估高风险与低风险之间的因果关系 风险3-药物组合。最后，我们将使用临床专家团队来评估HDDI-ADE对和 从目标1和目标2中确定的临床有效性和实用性的关联。 识别高风险和低风险3种药物组合是重要的知识，可以指导 开发新的综合药房处方建议，以帮助解决重大公共卫生问题 ADE制造的挑战。

Older adults (≥65 years old) constitute about 17% of the US population but account for ~57% of the population who use ≥3 drugs (polypharmacy). Currently, ~30million US adults use ≥3 drugs which increase their exposure to high-order drug-drug interactions (HDDIs), i.e. drug-drug interactions involving ≥3 drugs. HDDI is a major risk factor of serious adverse drug events (ADEs) that result in emergency department (ED) visits or hospitalization. Older adults, compared to younger adults, are 7-times more likely to be hospitalized and 2 to 3-times more likely to visit the ED for ADEs. However, knowledge on how to identify HDDI-ADE associations, data on the comparative safety of different 3-drug combinations and clinical and pharmacokinetic mechanisms for HDDI- ADE associations are all critically limited. The current project is designed to address these major gaps by specifically: (1) applying our state-of-art data mining techniques to discover HDDIs that are associated with higher risk of ADEs as well as those associated with lower ADE risk; (2) performing a comparative safety assessment of 3-drug combinations involving anticoagulants, antidiabetic agents and opioids; and (3) validating and evaluating the clinical validity and pharmacologic mechanisms of HDDI-ADE associations. These aims will be implemented by focusing on older patients who had an ED visit based on real-world data from health insurance claims (MarketScan and Medicare) and Electronic Health Records (EHR) data. We will focus on gastrointestinal (GI) bleeding, hypoglycemia and opioid-induced ADEs as the primary ADEs of interest for all three aims. These three ADEs account for 60% of all ADE-induced ED visits and are a target priority for prevention and surveillance by the US Health and Human Services. Our preliminary data from the MarketScan claims data (2012-2018) alone has confirmed that older adults are at higher risk of ADEs and that the risk of these ADEs increase with the counts of concomitant drugs used by patients. We will apply our mixture drug-count response (MDCR) and graphical models to identify the specific 3- or 4-drug combinations that have the highest risk and those with the lowest risk of ADEs in ED settings among older adults (Aim 1). For Aim 2, we will apply pharmacoepidemiologic study designs and casual inference approaches to assess the comparative safety of specific high and low-risk 3-drug combinations. We will control for the potential confounding effects of several patient-level (demographic, clinical, chronic comorbidities, drug characteristics, healthcare utilization, etc) and provider/healthcare system-level factors to assess the causal associations between high-risk versus low- risk 3-drug combinations. Finally, we will use a clinician expert team to evaluate the HDDI-ADE pairs and associations identified from Aims 1 and 2 for clinical validity and utility. The identification of high- and low-risk 3-drug combinations is vital knowledge that could guide the development of new polypharmacy prescribing recommendations to help address the significant public health challenge created by ADEs.