Risk-Based Primary Prevention of Heart Failure

基于风险的心力衰竭一级预防

• 批准号: 10516468
• 负责人: Sadiya Sana Khan
• 金额: $ 13.5万
• 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/10516468
• 关键词: Address; Adrenergic beta-Antagonists; Adult; Age; Agonist; Algorithms; 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; Clinical Trials; Coronary heart disease; Data; Development; Diabetes Mellitus; Disease; Dissemination and Implementation; EFRAC; Echocardiography; Effectiveness; Electronic Health Record; Equation; 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; Recommendation; Research; Risk; Risk Estimate; Risk Factors; Sampling; Science; Societies; Sodium; Statistical Models; Strategic vision; Symptoms; Techniques; Testing; Time; Treatment Failure; United States; Work; base; blood glucose regulation; clinical decision support; clinical development; clinical practice; clinical risk; cohort; college; comorbidity; comparative effectiveness; comparative safety; data harmonization; design; enhancing factor; future implementation; high risk; 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; racial and ethnic; recruit; risk prediction; risk prediction model; screening; sex; social determinants; social health determinants; social integration; 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 尽管美国的总心血管死亡率、心力衰竭(HF)死亡率、 以及住院和再入院的人数正在增加，死亡率的上升幅度最大 在65岁以下的非西班牙裔黑人成年人中观察到。心力衰竭高危人群的识别 而不同样本中特定的HF亚型(HFrEF和HFpEF)对于提供急需的信息至关重要 减轻心力衰竭负担的策略。尽管指南指导的医疗疗法越来越多地可用 对于射血分数(HFrEF)降低的HF，预后仍然很差，5年存活率为50%。此外， 目前，对于射血分数保留的心衰患者，几乎没有有效的疾病修正疗法。 (HFpEF)，这是最常见的HF亚型。心力衰竭日益沉重的负担凸显了 在出现临床症状之前进行预防性干预的必要性。因此，风险 预测目标预防HF，特别是HFpEF，是下一步需要改进的关键 结果。而基于风险的预防(将预防的强度与 个人)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 目前存在心力衰竭的预防范例，部分原因是缺乏公认的和可推广的风险 模特。为了解决多社会实践指南的建议，我们小组最近开发了和 使用经典统计建模验证预防心力衰竭(PCP-HF)的汇集队列方程 以人群为基础的队列样本中的技术。目前的建议建立在我们先前工作的基础上，并扩展了 IT利用新的机器学习方法高效地集成大型、多维数据 来自两个综合卫生系统(西北医学和Kaiser Permanente)的多个领域。 这将使我们能够创造一个地理、种族/民族和社会经济多样化的现实世界 约800,000人组成的队列，为有效和公平的基于风险的预防提供信息 战略的重点是HF。我们将分析来自两个医疗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、健康的社会决定因素)以及创新的统计 开发最佳风险预测模型的技术(例如，机器学习)。当前提案的目的 (1)开发和验证发生心力衰竭和心力衰竭亚型(HFrEF和HFrEF)的特定性别风险预测模型 HFpEF)和(2)定义了预防性心力衰竭治疗(例如，血管紧张素转换)的比较有效性 酶抑制剂、葡萄糖共转运蛋白2抑制剂)按预测的心力衰竭风险分层。这个项目将 为今后个性化临床决策支持工具的推广和实施奠定基础 心衰预防策略。完成这些目标将直接涉及 2019年NHLBI/心血管科学部战略愿景实施计划，有可能 对“减少与心力衰竭有关的负担”有重大影响。