A Longitudinal Examination to Predict Quality of Life and Care Transitions for Persons with Alzheimer’s Disease and Related Dementias at End-of-Life

预测阿尔茨海默病和相关痴呆症患者临终生活质量和护理转变的纵向检查

• 批准号: 10162470
• 负责人: Suzanne Sierra Sullivan
• 金额: $ 7.98万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162470
• 关键词: Address; Advance Care Planning; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Attention; Caregivers; Caring; Cessation of life; Chronic Disease; Chronic stress; Clinical; Cognitive; Communities; Complex; Data Set; Decision Making; Decision Support Model; Dementia; Development; Elderly; Ensure; Ethnic Origin; Family Caregiver; Feeling; Foundations; Future; Geographic Locations; Geography; Goals; Gold; Health; Healthcare; Healthcare Systems; Hospice Care; Hospitalization; Hospitals; Individual; Inpatients; Institute of Medicine (U.S.); Knowledge; Lead; Machine Learning; Medical; Modeling; National Institute on Aging; Nature; Nursing Research; Outcome; Patients; Persons; Population; Prevalence; Provider; Quality of Care; Quality of life; Race; Recommendation; Reporting; Research; Risk; Science; Socioeconomic Status; Terminal Disease; Time; Time Series Analysis; United States; Update; base; care outcomes; caregiver strain; caregiving; end of life; end of life care; ethnic diversity; experience; high risk; hospice environment; improved; inpatient service; loved ones; novel strategies; person centered; personalized approach; personalized care; phrases; predictive modeling; preference; prevent; racial and ethnic; secondary analysis; socioeconomics; tool; trait; trend

ABSTRACT Alzheimer's disease and related dementias (AD/ADRD) are a group of progressive, terminal illnesses that will affect an estimated 14-million people in the United States by the year 2050. Caregivers experience chronic stress and they report feeling burdened and unprepared for making difficult end-of-life (EOL) care decisions, which may lead to unnecessary hospitalizations or avoidable transitions in care that affect end-of-life quality-of-life (EOL-QOL). Advance care planning (ACP), which improves EOL-QOL, is the gold-standard approach for improving concordance between preferences and actual care received at EOL. However, despite decades of research aimed at raising their rates, only 50% of those with AD/ADRD have a written ACP. Furthermore, there is a lack of current research evidence investigating the factors associated with transitions in care and EOL-QOL for persons with AD/ADRD, which could help guide EOL decision-making. To date, the state of the science is primarily cross-sectional in nature, and does not account for the influence of trajectories of decline, the effect these changes have on caregivers, nor how longitudinal changes in caregiving ultimately affect EOL care outcomes. Therefore, there is a critical need to discover new approaches for preparing persons with AD/ADRD and their caregivers in making informed, in-the-moment decisions, to ensure high EOL-QOL care and to support appropriate transitions in care as circumstances change over time. Using the National Health and Aging Trends Study (NHATS) and National Study on Caregiving (NSOC), we plan to use a machine learning based framework to identify the key determinants for predicting the risk for EOL care transitions and the traits of EOL-QOL among older adults residing in the community. This study has two specific aims: 1) Develop predictive model of factors related to end-of-life care transitions (e.g. inpatient death versus hospice) in persons with AD/ADRD longitudinally; and 2) Develop a predictive model of factors related to end-of-life quality-of-life (EOL-QOL) in persons with AD/ADRD. Discovering knowledge in a large population- level dataset is foundational for the future development of a generalizable/scalable model for guiding persons with AD/ADRD and their caregivers as they navigate a fragmented healthcare system while making difficult decisions for their loved ones. Our approach fills a critical gap between the current approaches for improving EOL-QOL and EOL transitions in care that focus on ACP as a singular outcome, by addressing the comprehensive needs of individuals with AD/ADRD and their caregivers that change over time. Our study will provide a predictive model for EOL-QOL and EOL care transitions. This is a critical first step for the future development of an approach for personalizing care to guide persons with AD/ADRD and their caregivers in making EOL care decisions. These results will have an important positive impact on EOL care, which aligns with the strategic goals of the National Institutes of Aging (NIA) and Nursing Research (NINR) to address clinical and translational gaps in the study of EOL care needs of people with AD/ADRD.

摘要 阿尔茨海默病和相关痴呆(AD/ADRD)是一组进行性、终末期疾病 据估计，到2050年，美国将有1400万人受到影响。护理者体验 慢性压力，他们报告说感觉负担过重，没有准备好进行困难的临终关怀 可能导致不必要的住院或影响临终关怀的可避免的护理过渡的决定 生活质量(EOL-QOL)。高级护理计划(ACP)是改善EOL-QOL的黄金标准 改善在EOL接受的偏好和实际护理之间一致性的方法。然而，尽管 几十年来旨在提高AD/ADRD发病率的研究表明，只有50%的AD/ADRD患者有书面的ACP。 此外，目前还缺乏研究证据来调查与转变相关的因素。 为AD/ADRD患者提供护理和EOL-QOL，有助于指导EOL决策。到目前为止， 科学的状态主要是横截面性质的，没有考虑轨迹的影响 这些变化对照顾者的影响，以及最终照顾者的纵向变化 影响EOL护理结果。因此，迫切需要找到新的筹备方法。 AD/ADRD患者及其照顾者在做出明智的、即时的决定时，确保高 EOL-QOL护理，并支持随着时间的推移情况发生变化时适当过渡护理。使用 国家健康和老龄化趋势研究(NHATS)和国家护理研究(NSOC)，我们计划使用 基于机器学习的框架，以确定预测EOL护理风险的关键决定因素 居住在社区的老年人的生活质量变化及生活质量特征。这项研究有两个 具体目标：1)建立与临终关怀过渡相关因素的预测模型(例如，住院死亡 与临终关怀相比)；和2)建立相关因素的预测模型 AD/ADRD患者的临终生活质量(EOL-QOL)。在庞大的人群中发现知识-- 水平数据集是未来开发可泛化/可扩展的导游人员模型的基础 与AD/ADRD及其照顾者一起应对支离破碎的医疗体系，同时使 为他们所爱的人做决定。我们的方法填补了当前改进方法之间的关键差距 关注ACP作为一个单一结果的EOL-QOL和EOL护理过渡，通过解决 AD/ADRD患者及其照顾者的全面需求随着时间的推移而变化。我们的研究将 为EOL-QOL和EOL护理过渡提供预测性模型。这是面向未来的关键的第一步。 开发个性化护理方法，以指导AD/ADRD患者及其照顾者 做出EOL护理决策。这些结果将对EOL关怀产生重要的积极影响，这与 国家老龄研究所(NIA)和护理研究(NINR)的战略目标要解决 AD/ADRD患者EOL护理需求研究中的临床和翻译空白。

项目成果

Exploring Web-Based Twitter Conversations Surrounding National Healthcare Decisions Day and Advance Care Planning From a Sociocultural Perspective: Computational Mixed Methods Analysis.

• DOI: 10.2196/35795
• 发表时间: 2022-04-13
• 期刊: JMIR FORMATIVE RESEARCH
• 影响因子: 2.2
• 作者: Lattimer, Tahleen A.;Tenzek, Kelly E.;Ophir, Yotam;Sullivan, Suzanne S.
• 通讯作者: Sullivan, Suzanne S.