Strengthening the Evidence-Base for Drug-Disease Interactions in Older Adults

加强老年人药物与疾病相互作用的证据基础

• 批准号: 10617649
• 负责人: Tobias Gerhard
• 金额: $ 47万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617649
• 关键词: Adherence; Adrenal Cortex Hormones; Adverse drug effect; Adverse effects; Affect; Allopurinol; Biological; Biology; Case Study; Characteristics; Chronic Disease; Clinical; Clinical Medicine; Clinical Trials; Data; Data Set; Databases; Diabetes Mellitus; Dialysis procedure; Disease; Dose; Drug Evaluation; Drug Interactions; Drug Kinetics; Drug usage; Effectiveness; Elderly; Electronic Health Record; Epidemiologic Methods; Eszopiclone; Evaluation; Evidence based practice; Exclusion; Exclusion Criteria; Expert Opinion; Fright; Gastrointestinal Hemorrhage; Guidelines; Hepatic; Hip Fractures; Impairment; Individual; Kidney; Kidney Transplantation; Label; Lactic Acidosis; Link; Literature; Measures; Medicare; Metformin; Methodology; Methods; Modernization; Osteoporosis; Outcome; Patient-Focused Outcomes; Patients; Peptic Ulcer; Performance; Pharmaceutical Preparations; Pharmacodynamics; Physicians; Polypharmacy; Population Study; Predisposition; Prevalence; Process; Public Health; Recommendation; Regimen; Research; Research Design; Risk; Risk Reduction; Safety; Severities; Statistical Methods; Subgroup; Symptoms; System; Taxonomy; Testing; Therapeutic; Work; Zaleplon; adverse drug reaction; adverse outcome; aging population; clinical decision-making; clinical practice; clinically relevant; comorbidity; computerized; data resource; design; drug action; drug classification; evidence base; evidence based guidelines; fall risk; fracture risk; health service use; improved; individualized medicine; innovation; mortality; multiple chronic conditions; novel; personalized medicine; pharmacologic; predict clinical outcome; prevent; treatment effect; treatment guidelines; treatment planning; virtual; zolpidem

Drug-disease interactions (DDSIs) occur when drug effects, such as risks for rare but severe adverse effects, are altered by a preexisting disease. DDSIs affect up to 50% of older adults and have been associated with increased mortality and use of health services. DDSIs are of particular concern in older adults because both polypharmacy and chronic illness become progressively more prevalent with advanced age. Although drug labels, treatment guidelines, and drug information compendia include warnings and contraindications for many thousands of drug-disease combinations, extremely little evidence and research exists on their clinical relevance. DDSI guidance generally relies on case reports, pharmacological mechanism, or structural similarity to related drugs, and thus commonly represents untested hypotheses rather than evidence from well-designed population-based studies. As a result, physicians and their patients are often unable to distinguish between warnings for clinically relevant DDSIs that should be followed to avoid increased risk for adverse drug effects, and warnings for purely theoretical DDSIs that should be ignored in order to allow initiation of treatment with the otherwise indicated drug of choice. Better evidence on DDSIs is thus urgently needed to allow physicians to recommend evidence-based personalized therapy for their patients. Using existing data resources on millions of patients from Medicare (US) and the Clinical Practice Research Datalink (UK), the proposed study will use four carefully selected examples of highly prevalent drugs to demonstrate a new methodological framework for the systematic assessment of DDSIs from large observational datasets: Metformin and renal impairment increasing risk of lactic acidosis (Aim 1), Z-drugs and osteoporosis increasing risk of hip fracture (Aim 2), systemic corticosteroids and peptic ulcer disease increasing risk of gastrointestinal bleeding (Aim 3), and allopurinol and renal impairment reducing risk of dialysis or kidney transplant (Aim 4). These examples were selected considering a number of explicit criteria including severity of the adverse outcome, disagreement about relevance in the literature and clinical practice, ability to measure disease and adverse clinical outcome in the databases, and availability of therapeutic alternatives that do not share the hypothesized DDSI. We included interactions across a spectrum of prevalence and expected effect sizes to evaluate the performance of the proposed approach in different situations and sought some effects very likely to be absent (Aim 1) or present (Aim 2) to show we can reproduce expected findings, and uncertain effects (Aims 3 and 4). The proposed study puts forward a novel framework that comprehensively classifies DDSIs according to their underlying biological mechanisms and represents the first systematic attempt to apply modern epidemiological and statistical methods to the examination of DDSIs. Its results will begin a line of work that will ultimately enable physicians to practice evidence-based personalized medicine by providing reliable data on the effects of patient-specific comorbidities on the safety and effectiveness of their therapeutic regimens.

药物-疾病相互作用（DDSI）发生在药物效应，如罕见但严重的不良反应风险， 影响，被先前存在的疾病改变。DDSI影响高达50%的老年人， 死亡率和医疗服务使用率都在上升。DDSI在老年人中特别值得关注，因为 多种药物和慢性疾病随着年龄的增长而越来越普遍。虽然 药物标签、治疗指南和药物信息纲要包括警告和禁忌症， 成千上万的药物-疾病组合，很少有证据和研究存在于其临床 本案无关DDSI指南通常依赖于病例报告、药理学机制或结构相似性 因此，通常代表未经检验的假设，而不是来自精心设计的证据 基于人口的研究。因此，医生和他们的病人往往无法区分 应遵循临床相关DDI的警告，以避免药物不良反应风险增加， 和纯理论性DDI的警告，应忽略这些警告，以便开始使用 其他指定药物的选择。因此，迫切需要更好的DDSI证据， 为他们的病人推荐基于证据的个性化治疗。使用现有数据资源 来自医疗保险（美国）和临床实践研究数据链（英国）的数百万患者， 我将使用四个精心挑选的高度流行的药物的例子来展示一种新的方法 大型观察性数据集DDSI系统评估的框架：代谢和肾脏 损伤增加乳酸性酸中毒的风险（目的1），Z-药物和骨质疏松症增加髋部骨折的风险 (Aim 2）、全身性皮质类固醇激素和消化性溃疡疾病增加消化道出血的风险（目的3）， 别嘌呤醇和肾损害，降低透析或肾移植的风险（目标4）。这些示例 根据一些明确的标准进行选择，包括不良结局的严重程度， 关于文献和临床实践的相关性、测量疾病和不良临床结局的能力 在数据库中，以及不共享假设DDSI的治疗替代品的可用性。我们 包括在患病率和预期效应大小范围内的相互作用，以评估性能 在不同情况下的拟议办法，并寻求一些很可能不存在的效果（目标1），或 目前（目标2），以表明我们可以重现预期的结果，和不确定的影响（目标3和4）。的 拟议的研究提出了一个新的框架，全面分类DDSI根据其 潜在的生物学机制，并代表了第一次系统地尝试应用现代流行病学 和统计学方法来检查DDSI。它的结果将开始一系列的工作， 通过提供可靠的疗效数据，使医生能够实践基于证据的个性化医疗 患者特异性合并症对其治疗方案的安全性和有效性的影响。