Advanced Heart Failure: Epidemiology and Outcomes

项目总结/摘要 晚期心力衰竭的特征是进行性衰弱症状和反复住院 降低生活质量。没有一个诊断晚期心力衰竭的标准;定义是 复杂和具有挑战性的广泛应用于人口。因此，我们对晚期心力衰竭的了解 由于它是基于转诊人群和便利样本的信息，因此被截断和扭曲。 加强我们对晚期心脏病患者的流行病学、经验和结局的理解 失败对于制定改善护理和生活质量的干预措施至关重要。为了弥补这些差距， 知识，该提案利用各种数据源和定量和定性的新应用程序， 评估晚期心力衰竭患者流行病学和预后的方法。 在目标1中，我们将把一个先进的心力衰竭定义应用于地理上定义的个体人群 在罗切斯特流行病学项目的赞助下，我们将确定 晚期心力衰竭，检查人群的人口统计学和临床特征，并评估 其发展的时间以及与结果风险的关联。在目标2中，我们将使用机器学习 开发计算机算法（可计算表型）以识别晚期心脏病患者的技术 使用电子健康记录数据失败。然后我们将利用国家病人的基础设施- 中心临床研究网络（PCORnet），以验证可计算表型的性能 在不同的患者群体中。这将能够准确有效地识别晚期HF， 未来的应用。在目标3中，我们将使用目标2中开发的可计算表型， 识别患有晚期HF的个体。然后，我们将评估他们的治疗和疾病负担， 调查和半结构化定性访谈相结合。这些信息将用于通知 制定针对晚期HF患者需求的姑息治疗干预措施。我们将 评估利益相关者（患者，护理人员， 临床医生）。 这些分析的结果将提供协同信息，以澄清流行病学，病例组合， 负担和结果的个体与先进的心力衰竭。他们将提供一个姑息治疗的原型 为减轻晚期心力衰竭患者的负担并改善其生活质量而量身定制的干预措施。最后 开发的可计算表型可用于识别晚期HF患者，以提高未来的质量 改善计划、观察性研究和干预性研究。

PROJECT SUMMARY/ABSTRACT Advanced heart failure is characterized by progressive debilitating symptoms and repeated hospitalizations that degrade quality of life. There is no one criterion to diagnose advanced heart failure; the definition is complex and challenging to apply broadly to populations. As such, our knowledge of advanced heart failure is truncated and skewed as it is based on information from referral populations and convenience samples. Enhancing our understanding of the epidemiology, experiences, and outcomes of patients with advanced heart failure is critical to developing interventions to improve care and quality of life. To address these gaps in knowledge, this proposal leverages diverse data sources and novel applications of quantitative and qualitative methods to assess the epidemiology and outcomes of individuals with advanced heart failure. In Aim 1, we will apply an advanced heart failure definition to a geographically-defined population of individuals with heart failure under the auspices of the Rochester Epidemiology Project. We will determine the prevalence of advanced heart failure, examine the demographic and clinical features of the population, and evaluate the timing of its development and association with risk of outcomes. In Aim 2, we will use machine learning techniques to develop computer algorithms (computable phenotypes) to identify patients with advanced heart failure using electronic health record data. We will then leverage the infrastructure of the National Patient- Centered Clinical Research Network (PCORnet) to validate the performance of the computable phenotypes across diverse patient populations. This will enable the accurate and efficient identification of advanced HF for future applications. In Aim 3, we will use the computable phenotype developed in Aim 2 to prospectively identify individuals living with advanced HF. We will then assess their treatment and illness burdens using a combination of surveys and semi-structured qualitative interviews. This information will be used to inform the development of a palliative care intervention that is tailored to the needs of patients with advanced HF. We will assess the acceptability of the tailored palliative care intervention to stakeholders (patients, caregivers, clinicians). The results of these analyses will provide synergistic information to clarify the epidemiology, case mix, burdens, and outcomes of individuals with advanced heart failure. They will provide a prototype palliative care intervention tailored to decrease burden and improve quality of life in advanced heart failure. Finally, the computable phenotype developed can be used to identify patients with advanced HF for future quality improvement programs, observational studies, and interventional research.