'Heart Failure in the Community: Multimorbidity and Outcomes'

描述（由申请人提供）：我们面临着心力衰竭（HF）住院的“流行病”，这通常与HF无关。我们假设，广泛的努力并没有成功地减少心衰的住院率，因为管理标准依赖于以疾病为中心的临床指南，在以提供者为中心的护理系统中应用。护理模式往往忽略了心衰综合征是在一个复杂的、人们知之甚少的多发病背景下发生的，以及根据心衰类型（保留型还是减少型）出现的共病情况是如何的尚不清楚。此外，作为一种与多种其他疾病共存的慢性疾病，心衰会导致老年患者缺乏所需支持的自我管理困难。了解共存疾病的流行病学和成功自我管理的决定因素是设计新的实践模式和减少心衰住院的基础。我们修订后的申请的中心目标是通过实现以下三个目标来响应对新方法的迫切需求：首先，了解心衰共存疾病的复杂流行病学；其次，通过借鉴慢性护理模式，确定有利于成功自我管理的以患者为中心的属性；第三，开发整合心血管特征、共存疾病和自我护理决定因素的模型，用于护理点，为高危患者提供社区支持。我们的3个具体目标旨在实现这些目标，与卫生与公众服务部在其报告“多种慢性病：战略框架”中概述的优先事项保持一致。目的1将在罗切斯特流行病学项目中研究心衰患者社区队列中心衰共存条件的流行病学。我们将评估并存疾病（合并症、老年综合征）的患病率，并确定哪一类疾病最影响住院治疗。我们将根据HF的类型（保留EF vs减少EF）评估HF诊断后共存疾病的出现和进展及其与住院治疗的关系。目的2将在前瞻性心衰患者队列中评估以患者为中心的因素（社会支持和自我管理）在慢性护理模式指导下的心衰住院治疗中的作用。目的3将开发和评估预测模型，以确定HF患者的住院高风险。为此，我们将利用由健康信息技术资助的明尼苏达东南灯塔项目国家协调员办公室建立的独特的健康信息交换（HIE）能力和社区伙伴关系，并评估调整现有的HIE架构以在护理点实施此类模型的可行性。识别住院风险高的患者，并在患者住院时提醒社区护理协调员，以便提供积极的社区支持。通过实现这些目标，我们将了解共存疾病的流行病学以及以患者为中心的因素如何影响心衰患者的住院负担。我们将开发风险预测模型，整合心血管特征、共存条件和以患者为中心的因素，优化干预工具，以减少心衰住院率。

DESCRIPTION (provided by applicant): We face an "epidemic" of hospitalizations in heart failure (HF), which are often unrelated to HF. We hypothesize that extensive efforts have not successfully reduced hospitalizations in HF because management standards rely on disease-centric clinical guidelines, applied within provider-centric systems of care. Care models often overlook that the HF syndrome arises within a complex and poorly understood multi-morbidity context and how comorbid conditions present according to the type of HF (preserved vs. reduced EF) is not known. Further, as a chronic disease that coexists with multiple other conditions, HF causes self-management difficulties in elderly patients who may lack the needed support. Understanding the epidemiology of coexisting conditions and the determinants of successful self-management is fundamental to design new practice models and reduce hospitalizations in HF. The central goal of our revised application is to respond to the urgent need for new approaches by fulfilling 3 objectives: firstly, understand the complex epidemiology of coexisting conditions in HF, secondly, identify patient-centric attributes conducive to successful self-management by drawing upon the Chronic Care Model, thirdly, develop models that integrate cardiovascular characteristics, coexisting conditions and determinants of self-care to be used at the point of care to engage community support for at risk patients. Our 3 Specific Aims, designed to address these objectives, align with the priorities of the Department of Health and Human Services, outlined in their report "Multiple Chronic Conditions: A Strategic Framework". Aim 1 will study the epidemiology of coexisting conditions in HF in a community cohort of persons with HF within the Rochester Epidemiology Project. We will assess the prevalence of coexisting conditions (comorbid diseases, geriatric syndromes) and determine which clusters of conditions most impact hospitalizations. We will assess the emergence and progression of coexisting conditions after HF diagnosis and their association with hospitalizations, according to the type of HF (preserved vs. reduced EF). Aim 2 will evaluate the role of patient-centric factors (social support and self-management) on hospitalizations in HF guided by the Chronic Care Model in a prospective cohort of patients living with HF. Aim 3 will develop and evaluate prediction models to identify patients with HF at high risk for hospitalizations. In doing so, we will leverage the unique Health Information Exchange (HIE) capabilities and community partnerships established by the Office of the National Coordinator for Health Information Technology-Funded Southeast Minnesota Beacon Program and assess the feasibility of adapting an existing HIE architecture to implement such models at the point of care, identify patients at high risk for hospitalizations and alert community care coordinators when their patients have been hospitalized to enable proactive community support. By executing these aims, we will understand how the epidemiology of coexisting conditions and how patient-centric factors contribute to the burden of hospitalizations in HF. We will develop risk prediction models, which integrate cardiovascular characteristics, coexisting conditions and patient-centric factors to optimize tools for interventions to reduce hospitalizations in HF.