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: 10115556
• 负责人: Tobias Gerhard
• 金额: $ 56.24万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115556
• 关键词: Adherence; Adrenal Cortex Hormones; Adverse drug effect; Adverse effects; Affect; Allopurinol; Biological; Biology; Case Study; Characteristics; Chronic; Chronic Disease; Clinical; Clinical Medicine; Clinical Trials; Data; Data Set; Databases; Diabetes Mellitus; Dialysis procedure; Disease; Disease Outcome; Dose; Drug Evaluation; Drug Interactions; Drug Kinetics; Drug usage; Effectiveness; Elderly; Electronic Health Record; Epidemiologic Methods; Eszopiclone; Evaluation; Evidence based practice; Exclusion Criteria; Expert Opinion; Fright; Gastrointestinal Hemorrhage; Guidelines; Hepatic; Hip Fractures; Impairment; Individual; Kidney; Kidney Transplantation; Label; Lactic Acidosis; Literature; Measures; Medicare; Metformin; Methodology; Methods; Modernization; Osteoporosis; Outcome; Patient-Focused Outcomes; Patients; Peptic Ulcer; Performance; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Physicians; Polypharmacy; Population Study; Predisposition; Prevalence; Process; Public Health; Recommendation; Regimen; Research; Research Design; Risk; Risk Reduction; Safety; Severities; Statistical Methods; Structure; Subgroup; Symptoms; System; Taxonomy; Testing; Therapeutic; Work; Zaleplon; adverse drug reaction; adverse outcome; aging population; base; clinical decision-making; clinical practice; clinically relevant; comorbidity; computerized; data resource; design; drug action; evidence base; evidence based guidelines; fall risk; fracture risk; health service use; improved; individualized medicine; innovation; mortality; novel; personalized medicine; 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)发生在以下情况时：药物效应，如罕见但严重不良反应的风险 影响，会因先前存在的疾病而改变。DDSI影响高达50%的老年人，并与 随着死亡率和卫生服务使用率的增加。DDSI在老年人中特别令人担忧，因为 随着年龄的增长，服用多种药物和慢性病都会逐渐变得更加普遍。虽然 药品标签、治疗指南和药品信息汇编包括以下警告和禁忌症 数以千计的药物-疾病组合，关于它们的临床证据和研究非常少 关联性。DDSI指导通常依赖于病例报告、药理机制或结构相似性 相关药物，因此通常代表未经检验的假说，而不是来自精心设计的证据 以人口为基础的研究。结果，医生和他们的病人往往无法区分 应遵循临床相关DDSI的警告，以避免增加药物不良反应的风险； 以及对纯粹理论上的DDSI的警告，应被忽略，以便允许开始治疗 另有说明的首选药物。因此，迫切需要关于DDSI的更好的证据，以使医生 为他们的患者推荐循证个性化治疗。使用现有数据资源 来自联邦医疗保险(美国)和临床实践研究数据链(英国)的数百万患者，建议的研究 将使用四个精心挑选的高流行药物的例子来演示一种新的方法学 从大型观测数据集中系统评估DDSI的框架：二甲双胍和肾脏 损伤增加乳酸酸中毒的风险(目标1)、Z-药物和骨质疏松增加髋部骨折的风险 (目标2)，全身性皮质类固醇和消化性溃疡疾病增加胃肠道出血风险(目标3)， 以及别嘌醇和肾脏损伤，减少透析或肾移植的风险(目标4)。这些例子 是根据一些明确的标准来选择的，包括不良后果的严重性、不一致 关于文献和临床实践的相关性，衡量疾病和不良临床结果的能力 数据库中，以及不共享假设的DDSI的治疗替代方案的可用性。我们 包括一系列流行率和预期效果大小的交互作用，以评估绩效 在不同情况下对提议的方法进行评估，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)。这个 建议的研究提出了一种新的框架，根据DDSI的类型对其进行全面分类 并代表了应用现代流行病学的第一次系统尝试 以及统计方法对DDSI的检验。它的结果将开始一系列工作，最终将 通过提供关于疗效的可靠数据，使医生能够实践基于证据的个性化药物 患者特定的合并症对其治疗方案的安全性和有效性的影响。