'Heart Failure in the Community: Multimorbidity and Outcomes'

描述（由申请人提供）：我们面临心力衰竭（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HF的住院治疗，因为管理标准依赖于以疾病为中心的临床指南，应用于以提供者为中心的护理系统。护理模式通常忽略了HF综合征是在复杂且知之甚少的多发病背景下出现的，以及根据HF类型（EF保留与EF降低）如何存在共病状况尚不清楚。此外，作为一种与多种其他疾病共存的慢性疾病，HF导致缺乏所需支持的老年患者自我管理困难。了解并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HF住院的基础。我们修改后的应用程序的中心目标是通过实现3个目标来响应对新方法的迫切需求：首先，了解HF中共存疾病的复杂流行病学，其次，通过借鉴慢性护理模型，确定有助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的模型，同时存在的条件和自我保健的决定因素，将用于护理点，以吸引社区对风险患者的支持。我们的3个具体目标，旨在解决这些目标，符合卫生和人类服务部的优先事项，在他们的报告“多种慢性疾病：战略框架”中概述。目的1将在罗切斯特流行病学项目的HF患者社区队列中研究HF共存疾病的流行病学。我们将评估共存疾病（共病，老年综合征）的患病率，并确定哪些疾病最影响住院治疗。我们将根据HF的类型（EF保留vs.降低）评估HF诊断后共存疾病的出现和进展及其与住院的相关性。目的2将在一个前瞻性的HF患者队列中，评价以患者为中心的因素（社会支持和自我管理）对慢性护理模式指导的HF住院的作用。目标3将开发和评估预测模型，以识别住院高风险的HF患者。在这样做的过程中，我们将利用独特的健康信息交换（HIE）能力和社区合作伙伴关系建立的国家协调员办公室的健康信息技术资助的东南明尼苏达州灯塔计划，并评估适应现有的HIE架构的可行性，以实现这种模式在护理点，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导致HF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，以优化干预工具，减少HF住院。

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.