Decision support tool that uses novel feature extraction and machine learning approaches for early identification of Alzheimer's patients who are candidates for palliative and/or hospice care.

决策支持工具，使用新颖的特征提取和机器学习方法来早期识别适合姑息治疗和/或临终关怀的阿尔茨海默病患者。

• 批准号: 10383512
• 负责人: Anant Vasudevan
• 金额: $ 35.08万
• 依托单位: RADIAL ANALYTICS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10383512
• 关键词: Address; Advance Care Planning; Algorithms; Alzheimer' s Disease; Alzheimer' s disease care; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer' s disease related dementia; American; Area; Caregivers; Caring; Characteristics; Clinical; Clinical Decision Support Systems; Complement; Contracts; Data; Data Set; Decision Trees; Development; Diagnosis; Dimensions; Disease; Disease Progression; Distress; Early identification; Economics; Elements; Engineering; Ensure; Environment; Evaluation; Family; Family Caregiver; Financial Hardship; Future; Goals; Government; Growth; Hand; Health system; Home; Hospice Care; Hospitalization; Hospitals; Inpatients; Institutionalization; Intervention; Intuition; Lead; Left; Life; Link; Literature; Machine Learning; Medical; Medicare; Medicare claim; Methodology; Methods; Mission; Modeling; Occupations; Outcome; Outpatients; Palliative Care; Patients; Pattern; Persons; Phase; Physicians; Population; Population Characteristics; Prevalence; Progressive Disease; Provider; Proxy; Public Health; Quality of Care; Quality of life; Radial; Recording of previous events; Research; Resources; Savings; Services; Social Behavior; Social isolation; State Hospitals; Symptoms; System; Techniques; Technology; Time; Training; United States National Institutes of Health; Vendor; Work; base; burnout; care costs; care giving burden; care systems; clinical application; clinical efficacy; cognitive function; cohort; cost; cost effective; deep learning; end of life; evidence base; experience; feature extraction; hospice environment; improved; innovation; machine learning classifier; machine learning model; meetings; mortality; neural network; novel; palliative; programs; prospective; risk stratification; statistics; supervised learning; support tools; tool

Nearly 6 million Americans had Alzheimer’s disease (AD) in 2019, a figure that will double by 2050. This progressive disease can be difficult to manage at home for patients, and for their families and caregivers. Timely and appropriate referral to palliative care can help. The medical literature suggests that as Alzheimer’s progresses, palliative services can help improve quality of life and reduce the total cost of care (currently on track to exceed $578 billion/year by 2050). Benefits include avoiding unnecessary hospitalizations, providing support for in-home caregivers, and alleviating distressing symptoms. Despite these benefits, too few Alzheimer’s sufferers are referred to palliative care services and, when they are referred, it is often too late. Studies suggest that existing legacy tools do a poor job of timely identifying palliative care candidates among AD patients. Even in cases where palliative care suitability is clear, medical professionals often lack the time required or are uncomfortable discussing advance care planning. The aim of this research is to improve timely and appropriate palliative care referrals for AD patients. We plan to develop and validate a novel clinical application of cutting-edge machine learning techniques to identify AD patients for earlier palliative care intervention. We will predict 12-month mortality as a proxy for palliative care appropriateness, building upon previous research but also addressing its limitations. Our specific objectives are to (a) utilize six years of CMS national Medicare claims data to generate the most detailed analysis to date of AD patient utilization history, (b) develop a rich feature set of relevance to AD disease progression, encompassing medical utilization, clinical, functional, socio-behavioral, and demographic dimensions, (c) train and evaluate an array of supervised ML classifiers to predict 12-month mortality, and (d) develop a risk stratification score that may be used clinically to rank-order AD patients in terms of their appropriateness for referral to palliative care. Our risk stratification score will combine both the clinical appropriateness for palliative care (i.e., the need) and the likelihood of a successful referral (i.e., the feasibility). The aforementioned “feasibility” element is especially novel, and potentially represents a “missing link” that has hindered prior research. If our Phase I effort is successful, the outcome will be a validated novel data-driven approach to risk-stratify AD patients for earlier palliative care intervention. In a future Phase II proposal we would seek to demonstrate clinical efficacy by productizing and deploying the risk stratifier into a real-time clinical decision support system and prospectively evaluating this methodology in a clinical environment. Our proposal is responsive to the NIH/NIA’s mission “to conduct research leading to the development of innovative products and/or services that may advance progress in …caring for and treating AD/ADRD patients.” The planned work is specifically aligned with NIA Priority Topic DBSR-C, which calls for innovations to support “evidence-based methods, technologies, and interventions to reduce the burden of caregiving for persons with AD.”

2019年，近600万美国人患有阿尔茨海默病（AD），到2050年这一数字将翻一番。这 对于患者及其家人和护理人员来说，进行性疾病可能很难在家管理。 及时和适当地转诊到姑息治疗可以有所帮助。医学文献表明， 姑息治疗可以帮助改善生活质量，降低护理的总成本（目前 预计到2050年将超过5780亿美元/年）。好处包括避免不必要的住院治疗， 支持家庭照顾者，减轻痛苦的症状。尽管有这些好处， 老年痴呆症患者被转介到姑息治疗服务，当他们被转介时，往往为时已晚。 研究表明，现有的传统工具在及时识别姑息治疗候选人方面做得很差， AD患者。即使在姑息治疗的适用性是明确的情况下，医疗专业人员往往缺乏时间 需要或不舒服讨论提前护理计划。本研究的目的是及时改善 以及为AD患者提供适当的姑息治疗转诊。我们计划开发和验证一种新型临床 应用先进的机器学习技术来识别AD患者，以进行早期姑息治疗 干预我们将预测12个月的死亡率作为姑息治疗适当性的代表， 以前的研究，但也解决其局限性。我们的具体目标是：（a）利用为期六年的CMS 国家医疗保险索赔数据，以生成迄今为止最详细的AD患者使用历史分析， (b)开发与AD疾病进展相关的丰富特征集，包括医疗利用， 临床，功能，社会行为和人口统计学方面，（c）培训和评估一系列 监督ML分类器预测12个月死亡率，以及（d）开发风险分层评分， 在临床上用于对AD患者进行姑息治疗的适当性排序。我们的风险 分层评分将联合收割机结合姑息治疗的临床适当性（即，的需要）和 成功推荐的可能性（即，的可行性）。上述“可行性”要素尤其是 新的，并可能代表了一个“缺失的环节”，阻碍了先前的研究。如果我们的第一阶段工作 如果成功，结果将是一种经过验证的新型数据驱动方法，用于早期AD患者的风险分层。 姑息治疗干预。在未来的第二阶段提案中，我们将寻求通过以下方式证明临床疗效： 将风险分层器生产并部署到实时临床决策支持系统中，并且前瞻性地 在临床环境中评估这种方法。我们的建议是响应NIH/NIA的使命“， 开展研究，开发创新产品和/或服务， 在.护理和治疗AD/ADRD患者方面取得进展。计划的工作与NIA特别一致 优先主题DBSR-C，它呼吁创新，以支持“基于证据的方法，技术， 采取干预措施，减轻AD患者的生育负担。”