Risk-Based Primary Prevention of Heart Failure

基于风险的心力衰竭一级预防

• 批准号: 10689211
• 负责人: Sadiya Sana Khan
• 金额: $ 12万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10689211
• 关键词: Address; Adrenergic beta-Antagonists; Adult; Age; Agonist; Algorithms; American; American Heart Association; Angiotensin-Converting Enzyme Inhibitors; Area; Atherosclerosis; Biological Markers; Black race; Blood Pressure; Cardiology; Cardiovascular system; Cause of Death; Chronic Kidney Failure; Clinical; Coronary heart disease; Data; Development; Diabetes Mellitus; Disease; Disparity; Dissemination and Implementation; EFRAC; Echocardiography; Effectiveness; Electronic Health Record; Equation; Equity; Ethnic Origin; Funding; Future; GLP-I receptor; Geography; Glucose; Goals; Guide prevention; Guidelines; Health system; Heart failure; Hospitalization; Hypertension; Individual; Integrated Health Care Systems; Intervention; Machine Learning; Medical; Medicine; Methodology; Methods; Mission; Modeling; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Neighborhoods; Not Hispanic or Latino; Outcome Study; Participant; Patients; Pharmaceutical Preparations; Phenotype; Practice Guidelines; Prevalence; Prevention; Prevention strategy; Preventive; Preventive therapy; Primary Prevention; Prognosis; Public Health; Race; Recommendation; Research; Risk; Risk Estimate; Risk Factors; Risk Reduction; Sampling; Science; Societies; Sodium; Statistical Models; Strategic vision; Symptoms; Techniques; Testing; Time; Treatment Failure; United States; Work; blood glucose regulation; clinical decision support; clinical development; clinical practice; clinical risk; clinical trial recruitment; cohort; college; comorbidity; comparative effectiveness; comparative safety; data harmonization; design; effectiveness evaluation; enhancing factor; future implementation; high risk; high risk population; hospital readmission; improved; improved outcome; individualized prevention; inhibitor; innovation; machine learning method; mortality; multidimensional data; novel; novel therapeutics; population based; preservation; prevent; preventive intervention; prospective; risk prediction; risk prediction model; screening; sex; social determinants; social health determinants; socioeconomics; support tools; symporter; tool

ABSTRACT Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates, as well as hospitalizations and readmissions, are increasing with the greatest increases in mortality rates observed among non-Hispanic Black adults under the age of 65 years. Identification of individuals at risk of HF and specific HF subtypes (HFrEF and HFpEF) within diverse samples is critical to inform much-needed strategies to reduce the burden of HF. Although guideline-directed medical therapies are increasingly available for HF with reduced ejection fraction (HFrEF), prognosis remains dismal with 50% survival at 5 years. Further, few effective disease-modifying therapies currently exist for patients with HF with preserved ejection fraction (HFpEF), which is the most common HF subtype. The significant and growing burden of heart failure highlights the need for preventive interventions prior to the development of clinical symptoms. As a result, risk prediction to target prevention of HF, particularly for HFpEF, is a critical next step to improve outcomes. Whereas risk-based prevention (matching the intensity of prevention with the absolute risk of the individual) is widely accepted in the primary prevention of atherosclerotic cardiovascular disease, no such prevention paradigm currently exists for HF, in part, due to the lack of a well-established and generalizable risk model. To address multi-society practice guideline recommendations, our group recently developed and validated the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) using classic statistical modeling techniques in a population-based cohort sample. The current proposal builds upon our prior work and expands it to leverage novel machine learning methods to efficiently integrate large, multidimensional data across multiple domains and from two integrated health systems (Northwestern Medicine and Kaiser Permanente). This will allow us to create a geographically, racially/ethnically, and socioeconomically diverse real-world cohort of approximately 800,000 individuals to inform effective and equitable risk-based prevention strategies focused on HF. We will analyze individual-level data from the two health systems (e.g., clinical risk factor levels, comorbidities, medication use, social determinants of health) alongside innovative statistical techniques (e.g., machine learning) to develop optimal risk prediction models. The aims of the current proposal are: (1) develop and validate sex-specific risk prediction models for incident HF and HF subtype (HFrEF and HFpEF) and (2) define the comparative effectiveness of preventive HF therapies (e.g., angiotensin converting enzyme inhibitors, sodium glucose co-transporter 2 inhibitors) stratified by predicted HF risk. This project will lay the groundwork for future dissemination and implementation of clinical decision support tools to personalize HF prevention strategies. Completion of these aims will directly address a scientific focus area outlined in the 2019 NHLBI/Division of Cardiovascular Sciences Strategic Vision Implementation Plan with the potential to have significant impact on “reducing burden related to HF”.

抽象的 尽管美国的心血管死亡率总数下降，但心力衰竭（HF）死亡率，但 以及住院和再入院，随着死亡率的最大增加而增加 在65岁以下的非西班牙裔黑人成年人中观察到。识别有HF风险的个体 潜水员样品中的特定HF亚型（HFREF和HFPEF）对于急需的信息至关重要 减少HF燃烧的策略。尽管指导指导的医疗疗法越来越多 对于射血分数降低（HFREF）的HF，预后仍然令人沮丧，在5年时存活率为50％。更远， 目前，对于HF的患者，射血分数保留的患者目前很少有有效的疾病改良疗法 （HFPEF），这是最常见的HF亚型。心力衰竭的巨大燃烧强大 在发展临床症状之前需要进行预防性干预措施。结果，风险 预测预防HF的预测，特别是对于HFPEF，是改进的关键下一步 结果。而基于风险的预防（将预防强度与绝对风险相匹配 个人）在主要预防动脉粥样硬化心血管疾病中被广泛接受，没有这样 HF目前存在预防范式，部分原因是缺乏良好且可普遍的风险 模型。为了解决多社会实践指南建议，我们的小组最近开发了 使用经典统计建模验证了汇总队列方程以防止心力衰竭（PCP-HF） 基于人群的队列样本中的技术。当前的提案建立在我们先前的工作基础上，并扩展 它利用新颖的机器学习方法有效地整合了大型的多维数据 多个领域以及两个综合卫生系统（西北医学和凯撒永久）。 这将使我们能够在地理，种族/种族和社会经济上创建一个现实世界 大约800,000个人的队列，以告知有效且公平的基于风险的预防 专注于HF的策略。我们将分析来自两个卫生系统的个人级别数据（例如，临床风险 因素水平，合并症，药物使用，健康的社会决定者）以及创新的统计数据 技术（例如机器学习）以开发最佳风险预测模型。当前建议的目的 为：（1）为事件HF和HF亚型开发和验证特定性别的风险预测模型（HFREF和 HFPEF）和（2）定义预防性HF疗法的比较有效性（例如，血管紧张素转化 酶抑制剂，葡萄糖共转运蛋白2抑制剂）通过预测的HF风险分层。这个项目将 为将来的传播和实施临床决策支持工具奠定基础 HF预防策略。这些目标的完成将直接解决概述的科学重点领域 2019 NHLBI/心血管科学战略远景实施计划的司 对“减少与HF相关的燃烧”有重大影响。