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”.

摘要 尽管美国的心血管总死亡率下降，但心力衰竭（HF）死亡率， 以及住院和再入院，随着死亡率的增加而增加 在65岁以下的非西班牙裔黑人成年人中观察到。识别HF风险个体 不同样本中的特定HF亚型（HFrEF和HFpEF）对于提供急需的信息至关重要。 减轻HF负担的策略。尽管指南指导的医学治疗越来越多 对于射血分数降低的HF（HFrEF），5年生存率为50%，预后仍然很差。此外，本发明还 对于射血分数保留的HF患者，目前存在几种有效的疾病改善疗法 （HFpEF），这是最常见的HF亚型。心力衰竭的重大和日益增长的负担强调了 在出现临床症状之前进行预防性干预的必要性。因此，风险 预测HF的靶向预防，特别是HFpEF，是改善的关键下一步 结果。而基于风险的预防（将预防的强度与 个体）在动脉粥样硬化性心血管疾病的一级预防中被广泛接受，但没有这样的 目前存在HF的预防模式，部分原因是缺乏明确的和可推广的风险 模型为了解决多社会实践指南的建议，我们的小组最近制定和 使用经典统计模型验证了预防心力衰竭的合并队列方程（PCP-HF） 技术在一个基于人口的队列样本。目前的建议建立在我们以前的工作基础上， 它利用新的机器学习方法，有效地整合大型多维数据， 多个领域和两个综合卫生系统（西北医学和凯撒永久）。 这将使我们能够创建一个地理上、种族/民族上和社会经济上多样化的现实世界 约80万人组成的队列，为有效和公平的基于风险的预防提供信息 战略重点是HF。我们将分析来自两个卫生系统的个人层面数据（例如，临床风险 因素水平、合并症、药物使用、健康的社会决定因素）， 技术（例如，机器学习）来开发最佳风险预测模型。本提案的目的 （1）开发和验证HF事件和HF亚型（HFrEF和 HFpEF）和（2）定义了预防性HF治疗的比较有效性（例如，血管紧张素转换 酶抑制剂、钠葡萄糖协同转运蛋白2抑制剂）。该项目将 为未来临床决策支持工具的传播和实施奠定基础， HF预防策略。这些目标的完成将直接涉及《2010年科学和技术展望》中概述的一个科学重点领域。 2019年NHLBI/心血管科学部战略愿景实施计划，有可能 对“减轻HF相关负担”有显著影响。