Risk-Based Primary Prevention of Heart Failure

基于风险的心力衰竭一级预防

基本信息

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
项目类别：
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

项目摘要

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”.

抽象的尽管美国心血管死亡率总体下降，但心力衰竭（HF）死亡率以及住院率和再入院率都在增加，死亡率增幅最大在 65 岁以下的非西班牙裔黑人成年人中观察到的心力衰竭风险个体的识别。不同样本中的特定 HF 亚型（HFrEF 和 HFpEF）对于了解急需的信息至关重要尽管越来越多的指南指导的药物治疗可用，以减轻心力衰竭的负担。对于射血分数降低的心衰 (HFrEF)，预后仍然很差，5 年生存率为 50%。对于射血分数保留的心力衰竭患者，目前几乎没有有效的疾病缓解疗法 (HFpEF)，这是最常见的心力衰竭亚型，心力衰竭的负担日益严重。在出现临床症状之前需要采取预防性干预措施。预测目标预防心力衰竭（特别是 HFpEF）是改善心力衰竭的下一步关键结果，包括基于风险的预防（将预防强度与绝对风险相匹配）。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，但没有这样的目前心力衰竭的预防范式存在，部分原因是缺乏公认的、普遍的风险为了解决多社会实践指南建议，我们的小组最近开发并提出了模型。使用经典统计模型验证预防心力衰竭的队列方程 (PCP-HF) 当前的提案基于我们之前的工作并进行了扩展。它利用新颖的机器学习方法来有效地集成跨领域的大型多维数据多个领域和两个综合卫生系统（西北医学和凯撒医疗机构）。这将使我们能够创造一个地理、种族/民族和社会多样化的现实世界约 800,000 人组成的队列，为有效且公平的基于风险的预防提供信息我们将分析来自两个卫生系统的个人数据（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）当前提案的目标。事件包括：(1) 开发并验证 HF 和 HF 亚型（HFrEF 和 HFpEF）和（2）定义了预防性心力衰竭治疗的比较有效性（例如，血管紧张素转换酶该项目将按预测的 HF 风险进行分层。为未来传播和实施临床决策支持工具以实现个性化奠定基础心力衰竭预防战略的完成将直接解决《报告》中概述的科学重点领域。 2019 NHLBI/心血管科学部战略愿景实施计划有潜力对“减轻HF相关负担”具有重大影响。