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.

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 进行性疾病可能很难在家里对患者及其家人和照顾者进行管理。 及时和适当地转介到姑息治疗可能会有所帮助。医学文献表明，由于阿尔茨海默氏症 姑息治疗可以帮助提高生活质量，降低护理总成本(目前在 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，提供 支持居家照顾者，并缓解痛苦症状。尽管有这些好处，但太少了 阿尔茨海默氏症患者被转介到姑息治疗服务机构，当他们被转介时，往往已经太晚了。 研究表明，现有的遗留工具在及时识别姑息治疗候选者方面做得很差 AD患者。即使在姑息治疗的适宜性很明显的情况下，医疗专业人员也往往缺乏时间 需要或不舒服地讨论提前护理计划。这项研究的目的是为了及时提高 以及为AD患者提供适当的姑息治疗转介。我们计划开发和验证一种新的临床 应用尖端机器学习技术识别AD患者进行早期姑息治疗 干预。我们将预测12个月的死亡率作为姑息治疗适当性的替代指标， 以前的研究也解决了它的局限性。我们的具体目标是：(A)利用六年的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)开发与阿尔茨海默病进展相关的丰富特征集，包括医疗用途， 临床、功能、社会行为和人口统计方面，(C)培训和评估一系列 有监督的ML分类器，以预测12个月的死亡率，以及(D)制定风险分层评分，可能是 临床上用来对AD患者进行排序，根据他们是否适合转介到姑息治疗。我们的风险 分层评分将结合姑息治疗的临床适宜性(即需要)和 成功转介的可能性(即可行性)。前面提到的“可行性”要素尤其是 新奇的，并且可能是阻碍先前研究的“缺失的一环”。如果我们的第一阶段工作是 成功后，结果将是一种经过验证的新的数据驱动方法，用于对早期AD患者进行风险分层 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展示临床疗效 将风险分层程序生成并部署到实时临床决策支持系统中，并预期 在临床环境中评估这一方法。我们的建议是为了响应NIH/NIA的使命 进行研究，以开发可能会进步的创新产品和/或服务 …的研究进展照顾和治疗AD/ADRD患者。“计划的工作特别与NIA保持一致 优先主题DBSR-C，该主题呼吁创新以支持“基于证据的方法、技术和 减轻阿尔茨海默病患者护理负担的干预措施。

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.”