High-throughput screening for antihypertensive prescribing cascades

抗高血压处方级联的高通量筛选

• 批准号: 10682502
• 负责人: Steven Michael Smith
• 金额: $ 11.44万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10682502
• 关键词: Accounting; Adherence; Adrenergic beta-Antagonists; Adult; Adverse drug event; Adverse effects; Adverse event; Age; American; Antihypertensive Agents; Biological; Blood Pressure; Calcium Channel Blockers; Cardiovascular system; Chronic; Classification; Clinical; Collection; Data; Data Analyses; Data Science; Data Sources; Databases; Detection; Disease; Disparity; Diuretics; Drug Prescriptions; Economics; Edema; Elderly; Ethnic Origin; Exposure to; Failure; Future; Generations; Goals; Guidelines; Hypertension; Individual; Insurance; Intervention Studies; Investigation; Knowledge; Label; Medicare; Medicine; Methodology; National Heart, Lung, and Blood Institute; Outcome; Patients; Pharmaceutical Preparations; Physicians; Polypharmacy; Prevention; Process; Quality of life; Race; Records; Regimen; Research; Resources; Signal Transduction; Subgroup; Symptoms; Testing; Time; Work; blood pressure control; candidate identification; cardiovascular risk factor; clinical decision support; clinically significant; cost; frontier; high throughput screening; human disease; innovation; novel; precision drugs; prevent; screening; sex; side effect; thiazide

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.

项目摘要 高血压（HTN）在近一半的美国成年人中普遍存在，并使用> 300万种抗高血压药物进行治疗 尽管常用的抗高血压药通常耐受良好， 它们的普遍使用使数百万成年人暴露于潜在的治疗限制性不良事件（AE）， 这是众所周知的，但许多是非特异性的或与HTN相关的症状无法区分， 很容易归因于抗高血压药未能将这些AE与致病因子联系起来， 提示额外的治疗，以治疗AE-称为“处方级联”-与潜在的重要 关于多种药物治疗的影响、不必要的费用、暴露于额外的副作用、治疗 不依从和生活质量下降，尤其是老年人。迄今为止，大多数处方级联研究 一直狭隘地集中在具有已知AE的药物上，这些AE对药物具有高度特异性，严重限制了 我们对因不太为人所知或非特异性AE而发生的处方级联的理解。这 这种方法导致知识生成缓慢，错过了全面评估的机会， 并发现新的处方级联。根据NHLBI战略目标7，“利用新兴 数据科学的机会，以开辟新的研究前沿，”这一建议旨在开发和利用一个 用于处方级联的高通量筛选的新方法学途径，并发现新的 抗高血压处方级联使用全国代表性的行政索赔数据源。 这一目标将通过以下目的实现：1）阐明候选抗高血压相关处方 使用SIDe Effect Resource的级联，SIDe Effect Resource是处方标签的集合，包括药物AE和 药物适应症; 2）识别在抗高血压药物的真实的使用期间发生的处方级联信号 使用医疗保险数据库;以及，3）对级联信号检测进行分类，并通过 专家小组。拟议的工作预计将1）确定和表征共同的规模 降压处方级联，包括那些以前未知的; 2）制定一个有效的框架 大规模评估处方级联检测;和，3）建立基础的简编 已知的级联。这一建议也建立在逻辑上对未来的研究应用这一框架， 发现心血管（和其他）治疗的处方级联，评估下游 处方级联的后果，并测试临床决策支持辅助工具，以防止处方 瀑布