High-throughput screening for antihypertensive prescribing cascades

项目总结 高血压(HTN)在近一半的美国成年人中很普遍，并接受了300万英镑的抗高血压药物治疗 尽管常用的降压药一般耐受性良好， 它们的普遍使用使数百万成年人暴露于可能限制治疗的不良事件(AEs)，其中一些 这些都是众所周知的，但其中许多是非特异性的或与HTN相关的症状无法区分 很容易归因于令人不快的抗高血压药。未能将这些不良反应与致病因素联系起来可能 催促额外的治疗方法来治疗AE--也就是所谓的“处方级联”--具有潜在的重要性 关于多药、不必要的费用、暴露于额外副作用、治疗的影响 不坚持，生活质量下降，特别是在老年人中。迄今为止最多的处方级联研究 一直狭隘地专注于具有众所周知的AE的药物，这种药物对药物具有高度特异性，严重限制了 我们对由于不太知名或非特定的血管紧张素转换酶引起的级联反应的理解。这 这种方法导致知识生成缓慢，并错失了全面评估的机会 以及发现新的处方级联。与NHLBI战略目标7保持一致，即“利用新兴市场 数据科学中的机会，以开辟研究的新领域，“这项建议寻求开发和利用 高通量筛选处方级联和发现新药的新方法 使用具有全国代表性的行政索赔数据来源的抗高血压处方级联。 这一目标将通过以下目标来实现：1)阐明与降压相关的候选处方 使用副作用资源的级联，这是一个包括药物不良反应和药物不良反应的处方标签集合 药物适应症；2)识别在实际使用抗高血压药物期间出现的处方级联信号 使用Medicare数据库；以及3)对级联信号检测进行分类，并通过 专家小组。预计拟议的工作将1)确定和表征共同的 抗高血压处方级联，包括那些以前未知的；2)开发有效的框架 用于处方级联检测的大范围评估；以及，3)为 已知的瀑布。这一建议也为应用这一框架的未来研究奠定了逻辑基础 发现用心血管(和其他)治疗方法开出级联，评估下游 开出连锁处方的后果，并测试临床决策辅助工具以防止开处方 瀑布。

PROJECT SUMMARY Hypertension (HTN) is prevalent in nearly half of U.S. adults and treated with >3 million antihypertensive prescription fills per day in the U.S. Although commonly-used antihypertensives are generally well-tolerated, their ubiquitous use exposes millions of adults to potentially treatment-limiting adverse events (AEs), some of which are well-known, but many are non-specific or indistinguishable from HTN-related symptoms and not easily attributed to the offending antihypertensive. Failure to associate these AEs with the causative agent may prompt additional therapy to treat the AE—known as a “prescribing cascade”—with potentially important implications regarding polypharmacy, unnecessary costs, exposure to additional side effects, treatment nonadherence, and reduced quality of life, especially in older adults. Most prescribing cascade studies to date have been narrowly focused on drugs with a well-known AE that is highly specific to the drug, severely limiting our understanding of prescribing cascades occurring due to less well-known or non-specific AEs. This approach has resulted in slow knowledge generation and missed opportunities for comprehensively assessing and discovering new prescribing cascades. In line with NHLBI Strategic Objective 7 to “leverage emerging opportunities in data science to open new frontiers in research,” this proposal seeks to develop and utilize a novel methodologic approach for high-throughput screening of prescribing cascades and discover novel antihypertensive prescribing cascades using a nationally-representative administrative claims data source. This goal will be achieved via the following Aims: 1) elucidate candidate antihypertensive-related prescribing cascades using the SIDe Effect Resource, a collection of prescription labeling which include drug AEs and drug indications; 2) identify prescribing cascade signals occurring during real world use of antihypertensives using a Medicare database; and, 3) classify cascade signal detection and prioritize further research via an expert panel. The proposed work is expected to 1) identify and characterize the magnitude of common antihypertensive prescribing cascades, including those previously unknown; 2) develop an efficient framework for wide-scale assessment of prescribing cascade detection; and, 3) establish the basis for a compendium of known cascades. This proposal also builds logically towards future research applying this framework for discovery of prescribing cascades with cardiovascular (and other) treatments, assessing downstream consequences of prescribing cascades, and testing clinical decision support aids to prevent prescribing cascades.