Dementia and Heart Failure in the Community

社区中的痴呆症和心力衰竭

• 批准号: 9973133
• 负责人: Alanna M Chamberlain
• 金额: $ 23.85万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9973133
• 关键词: Address; Admission activity; Affect; Aging; Alzheimer' s disease related dementia; Atrial Fibrillation; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Characteristics; Chronic; Chronic Disease; Clinical; Communities; Complex; Data; Data Science; Data Set; Dementia; Disease; Early Diagnosis; Electronic Health Record; Emergency department visit; Ensure; Environment; Epidemic; Epidemiology; Event; Fostering; Geography; Health; Heart Diseases; Heart failure; Hospitalization; Intervention; Knowledge; Link; Machine Learning; Methods; Mortality Decline; Optimum Populations; Outcome; Outpatients; Pathway interactions; Patient Care; Patients; Persons; Phenotype; Population; Positioning Attribute; Prevalence; Process; Research; Risk; Scanning; Secondary to; Self Management; Skilled Nursing Facilities; Structure; Syndrome; Translating; United States; Visit; Work; base; clinically relevant; cognitive function; cognitive skill; cohort; comorbidity; dementia risk; experience; follow-up; health care service utilization; heart function; heart imaging; high risk; improved; interest; mortality; multiple chronic conditions; novel; older patient; personalized approach; phenome; prevent; response; secondary analysis; therapy design; trend

In response to PA-17-089, we propose exploratory analyses with an existing epidemiologic heart failure (HF) cohort linked to electronic health record (EHR) data to address the combined burden of coexisting Alzheimer’s disease and related dementias (AD/ADRD) and HF. We will capitalize on the Rochester Epidemiology Project (REP) and apply novel data science methods to existing community datasets. Recent adverse trends in cardiovascular disease (CVD) mortality are due to a major increase in HF deaths, and the uncontrolled epidemic of HF compromises progress against CVD. Addressing the epidemic of HF, a disease of aging populations, requires understanding how coexisting conditions interact with HF to impact outcomes and health care utilization. AD/ADRD occupies a distinct position within coexisting conditions as HF increases the risk of AD/ADRD, in part, via shared pathways. However, this association and, in particular, which phenotype of the heterogeneous HF syndrome is associated with AD/ADRD is not well known. AD/ADRD can adversely impact the outcomes of HF, a serious chronic disease that requires effective self-management and cognitive skills. Yet, how AD/ADRD impacts health care utilization and outcomes in HF is not well understood. These knowledge gaps can be addressed by secondary analyses of existing data. Our objectives are to study, in an epidemiological cohort, the characteristics of HF that impart a higher risk of AD/ADRD, and the impact of AD/ADRD on outcomes (death, cardiovascular events, hospitalizations and admission to skilled nursing facility). Our proposal directly addresses key points of the PA, such as “Effects of specific combinations of two or more comorbid conditions on risks for specific adverse health outcomes, impact of specific combinations of two or more chronic conditions, …interactions among disease processes, and health outcomes in complex older patients with multiple chronic conditions.” This work will capitalize on a large epidemiological HF cohort linked to comprehensive EHR data within the rich environment of the REP, optimally suited to our proposed secondary analyses. This proposal leverages the experience of our team with the epidemiology of HF and with novel data science methods readily applicable to secondary analyses of existing EHR data sets. Aim 1 will apply machine learning to discover HF phenotypes associated with AD/ADRD while considering cardiac function, occurrence of atrial fibrillation and other coexisting conditions. Aim 2 will evaluate the impact of AD/ADRD on outcomes of HF. Conducting this work in a cohort within a geographically defined population and extensive longitudinal follow up will ensure that our results reflect the experience of community patients with HF. An improved understanding of the coexistence of AD/ADRD and HF will establish the prevalence of this association in the community and characterize the type of HF associated with AD/ADRD, which may allow earlier diagnosis, thereby enabling interventions to prevent or forestall early disease. Finally, improving our knowledge of how AD/ADRD affects outcomes will foster more precise management.

为了响应PA-17-089，我们提出了一项与电子健康记录（EHR）数据相关的现有流行病学心力衰竭（HF）队列的探索性分析，以解决合并阿尔茨海默病和相关痴呆（AD/ADRD）和心力衰竭的联合负担。我们将利用罗切斯特流行病学项目（REP），并将新的数据科学方法应用于现有的社区数据集。最近心血管疾病（CVD）死亡率的不利趋势是由于心力衰竭死亡的大幅增加，而心力衰竭的不受控制的流行阻碍了对抗CVD的进展。心衰是一种老年人群的疾病，要解决这一流行病，需要了解共存的疾病如何与心衰相互作用，从而影响预后和医疗保健利用。AD/ADRD在共存条件下占有独特的地位，因为HF增加AD/ADRD的风险，部分是通过共享途径。然而，这种关联，特别是异质性HF综合征的哪种表型与AD/ADRD相关尚不清楚。AD/ADRD可对心衰（一种需要有效自我管理和认知技能的严重慢性疾病）的预后产生不利影响。然而，AD/ADRD如何影响心衰患者的医疗保健利用和预后尚不清楚。这些知识差距可以通过对现有数据的二次分析来解决。我们的目的是在流行病学队列中研究HF导致AD/ADRD高风险的特征，以及AD/ADRD对结局（死亡、心血管事件、住院和进入专业护理机构）的影响。我们的提案直接解决了PA的关键点，例如“两种或两种以上合并症的特定组合对特定不良健康结果风险的影响，两种或两种以上慢性病的特定组合的影响，……疾病过程之间的相互作用，以及患有多种慢性疾病的复杂老年患者的健康结果。”这项工作将利用与REP丰富环境中的全面电子病历数据相关的大型流行病学心衰队列，最适合我们提出的二次分析。本提案利用我们团队在心衰流行病学方面的经验，以及易于适用于现有电子病历数据集的二次分析的新颖数据科学方法。Aim 1将应用机器学习来发现与AD/ADRD相关的HF表型，同时考虑心功能、房颤的发生和其他共存条件。目的2将评估AD/ADRD对心衰预后的影响。在地理上确定的人群中进行这项工作，并进行广泛的纵向随访，将确保我们的结果反映社区心衰患者的经验。对AD/ADRD和HF共存的更好理解将建立这种关联在社区中的流行程度，并确定与AD/ADRD相关的HF类型，这可能允许早期诊断，从而使干预措施能够预防或预防早期疾病。最后，提高我们对AD/ADRD如何影响结果的认识将促进更精确的管理。