Digital Detection of Dementia Studies (D cubed Studies).

痴呆症研究的数字检测（D 立方研究）。

• 批准号: 10266121
• 负责人: MALAZ BOUSTANI
• 金额: $ 104.9万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10266121
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease therapeutic; American; Area Under Curve; Assessment tool; Caregivers; Caring; Clinical; Clinical dementia rating scale; Code; Cost of Illness; Data; Dementia; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Elderly; Electronic Health Record; Ensure; Florida; Funding; Future; Gold; Healthcare Systems; Impaired cognition; Incidence; Indiana; Individual; Investigation; Machine Learning; Measures; Medical; Medical Care Costs; Medicare; Methods; Modeling; National Institute on Aging; Nature; Patient Care; Patient Outcomes Assessments; Patients; Performance; Persons; Physicians; Primary Health Care; Process; Public Health; Quick Test for Liver Function; Randomized; Recommendation; Rural; Sampling; Screening procedure; Services; Site; Staging; Symptoms; System; Testing; Therapeutic; Time; Translations; United States National Institutes of Health; Universities; Validation; Visit; arm; base; cognitive testing; cohort; comparative effectiveness; comparative effectiveness trial; cost; cost effective; design; diagnosis standard; digital; early detection biomarkers; effectiveness evaluation; improved; instrument; machine learning algorithm; patient screening; response; rural area; screening; societal costs; suburb; tool; underserved area; validation studies

PROJECT SUMMARY/ABSTRACT Every year Alzheimer’s disease and related dementias (ADRD) adversely affect millions of Americans at a societal cost of more than $200 million.1 Concurrently, half of Americans living with ADRD never receive a diagnosis.2-7 Early detection helps those with ADRD and their caregivers better plan and potentially lessen the burden of lengthy and costly medical care. Current investigational approaches using biomarkers for early detection are invasive, costly, and sometimes inaccessible to patients. The National Institute on Aging calls for the development of effective, scalable and low cost approaches for early detection of ADRD (RFA-AG-20-051). Currently, primary care clinicians provide the majority of care to older adults living with ADRD.1-5 Our interdisciplinary scientific teams have developed and tested scalable early detection approaches.10, 11 We are proposing to evaluate an integrated approach embedded in the Annual Wellness Visit (AWV) that leverages Electronic Health Record systems, machine learning models, and patient reported outcomes to deploy a low- cost and scalable approach for early detection of ADRD. Our proposed studies will leverage previously developed machine learning models (Passive Digital Marker) and patient reported outcomes (Quick Dementia Rating Scale). The design of our proposed studies is predicated on the notion that patient screening is done to identify a more targeted group of referral for applicable diagnostic and management services. We will conduct two complementary multi-site studies to evaluate the effectiveness of two scalable approaches for early detection of ADRD. The first study will be a clinical validation study of the three scalable approaches; the Passive Digital Marker (PDM) that uses EHR data, the Quick Dementia Rating Scale (QDRS) that uses patient reported outcomes (PROs) imbedded within the EHR system, and the combination of both (PDM + QDRS). The second study will be a pragmatic cluster-randomized controlled comparative effectiveness trial of two screening approaches embedded within the AWV, as compared to the AWV-only process, in increasing the incidence rate of new ADRD. In the final year of the study, we will share our codes for both the Passive Digital Marker and the QDRS with Epic headquarters to ensure that these codes are available for any healthcare system with Epic nationwide. The high costs of treating Alzheimer’s disease and the costs incurred by patients and caregivers, both tangible and intangible, are a major threat to public health and the US economy. Developing scalable and low cost instruments and assessments integrated into EHR data will assist physicians in early detection, more and better diagnoses, and clinically meaningful care recommendations. Cost effective, scalable, and noninvasive models are urgently needed to proactively mitigate these costs and prolonged medical care.

项目总结/摘要 每年阿尔茨海默病和相关痴呆症（ADRD）都会对数百万美国人产生不利影响， 社会成本超过2亿美元。1同时，一半的美国ADRD患者从未接受过治疗。 2 -7早期发现有助于ADRD患者及其护理人员更好地计划并可能减少 长期和昂贵的医疗负担。目前使用生物标志物进行早期诊断的研究方法 检测是侵入性的、昂贵的，并且有时患者无法获得。国家老龄化研究所呼吁 开发早期检测ADRD的有效、可扩展和低成本方法（RFA-AG-20-051）。 目前，初级保健临床医生为患有ADRD的老年人提供大部分护理。 跨学科科学团队已经开发并测试了可扩展的早期检测方法。 建议评估年度健康访问（AWV）中嵌入的综合方法， 电子健康记录系统、机器学习模型和患者报告的结果，以部署低成本的 用于早期检测ADRD的成本和可扩展方法。我们提出的研究将利用以前的 开发的机器学习模型（被动数字标记）和患者报告的结果（快速痴呆） 等级量表）。我们提出的研究设计是基于这样一个概念，即患者筛查是为了 确定更有针对性的转诊群体，以获得适用的诊断和管理服务。我们会进行 两项互补的多地点研究，以评估两种可扩展方法的有效性， ADRD的检测第一项研究将是三种可扩展方法的临床验证研究; 被动数字标记（PDM）使用EHR数据，快速痴呆评定量表（QDRS）使用患者 嵌入EHR系统的报告结果（PRO），以及两者的组合（PDM + QDRS）。 第二项研究将是一个实用的集群随机对照比较有效性试验， 与AWV单独流程相比，AWV中嵌入的筛选方法在增加 新发ADRD发生率。在研究的最后一年，我们将分享我们的代码，无论是被动数字 标记和QDRS与Epic总部，以确保这些代码可用于任何医疗保健 Epic在全国范围内 治疗阿尔茨海默病的高昂费用以及患者和护理人员的费用， 和无形的，是对公共卫生和美国经济的主要威胁。开发可扩展的低成本 集成到EHR数据中的工具和评估将有助于医生早期发现， 更好的诊断和有临床意义的护理建议。经济高效、可扩展且无创 迫切需要模型来主动降低这些成本和延长医疗护理。