Dementia and Heart Failure in the Community

作为对PA-17-089的回应，我们建议对与电子健康记录(EHR)数据相关联的现有流行病学心力衰竭(HF)队列进行探索性分析，以解决阿尔茨海默病和相关痴呆(AD/ADRD)和HF并存的综合负担。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人数的大幅增加，而心力衰竭的不受控制的流行损害了防治心血管疾病的进展。应对人口老龄化疾病--心力衰竭的流行，需要了解共存疾病如何与心力衰竭相互作用，从而影响结局和卫生保健利用。AD/ADRD在共存条件下占据着明显的位置，因为心衰增加了AD/ADRD的风险，部分是通过共享的通路。然而，这种关联，特别是异质性心力衰竭综合征的哪种表型与AD/ADRD相关，还不是很清楚。AD/ADRD可能对心力衰竭的结局产生不利影响，心力衰竭是一种严重的慢性病，需要有效的自我管理和认知技能。然而，AD/ADRD如何影响心力衰竭的卫生保健利用和结局还不是很清楚。这些知识差距可以通过对现有数据进行二次分析来弥补。我们的目标是在流行病学队列中研究导致AD/ADRD风险较高的HF的特征，以及AD/ADRD对预后(死亡、心血管事件、住院和进入熟练护理机构)的影响。我们的建议直接涉及PA的关键点，例如：“两种或两种以上并存疾病的特定组合对特定不良健康后果风险的影响，两种或两种以上慢性病的特定组合的影响，…患有多种慢性病的复杂老年患者的疾病过程和健康结果之间的相互作用。这项工作将利用与REP丰富环境中全面的EHR数据相联系的大规模流行病学HF队列，最适合我们建议的二次分析。这项建议利用了我们团队在心力衰竭流行病学方面的经验，以及易于应用于对现有EHR数据集进行二次分析的新数据科学方法。目标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、心房颤动的发生和其他共存情况。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力衰竭患者的经历。更好地了解AD/ADRD和HF的共存将确定这一关联在社区中的流行情况，并确定与AD/ADRD相关的HF的类型，这可能使早期诊断成为可能，从而使干预能够预防或预防早期疾病。最后，提高我们对AD/ADRD如何影响结果的了解将促进更精确的管理。

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.