Digital Detection of Dementia Studies (D cubed Studies).

痴呆症研究的数字检测（D 立方研究）。

基本信息

批准号：
10662223
负责人：
MALAZ BOUSTANI
金额：
$ 87.82万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
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/10662223
关键词：
Affect Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease therapeutic American Area Under Curve Assessment tool Caregivers Caring Clinical Clinical dementia rating scale Code Data Dementia Detection Development Diagnosis Diagnostic Digital biomarker Disease Early Diagnosis Elderly Electronic Health Record Florida Funding Future Healthcare Systems Impaired cognition Incidence Indiana Individual Machine Learning Measures Medical Medical Care Costs Medicare Methods Modeling National Institute on Aging Nature Patient Outcomes Assessments Patients Performance Persons Physicians Process Public Health Randomized Recommendation Rural Sampling Screening procedure Services Site Staging Symptoms Testing Therapeutic Time Translations United States National Institutes of Health Universities Validation Visit arm cognitive testing cohort comparative effectiveness comparative effectiveness trial cost cost effective design diagnosis standard digital early detection biomarkers effectiveness evaluation electronic health record system improved instrument machine learning algorithm machine learning model patient screening primary care clinician primary care patient primary care practice response rural area screening societal costs suburb tool underserved area validation studies

Affect Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease therapeutic American Area Under Curve Assessment tool Caregivers Caring Clinical Clinical dementia rating scale Code Data Dementia Detection Development Diagnosis Diagnostic Digital biomarker Disease Early Diagnosis Elderly Electronic Health Record Florida Funding Future Healthcare Systems Impaired cognition Incidence Indiana Individual Machine Learning Measures Medical Medical Care Costs Medicare Methods Modeling National Institute on Aging Nature Patient Outcomes Assessments Patients Performance Persons Physicians Process Public Health Randomized Recommendation Rural Sampling Screening procedure Services Site Staging Symptoms Testing Therapeutic Time Translations United States National Institutes of Health Universities Validation Visit arm cognitive testing cohort comparative effectiveness comparative effectiveness trial cost cost effective design diagnosis standard digital early detection biomarkers effectiveness evaluation electronic health record system improved instrument machine learning algorithm machine learning model patient screening primary care clinician primary care patient primary care practice response rural area screening societal costs suburb tool underserved area validation studies

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项目摘要

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.

项目摘要/摘要每年阿尔茨海默氏病和相关痴呆症（ADRD）都会不利影响数百万美国人社会成本超过2亿美元。1同时，有一半的美国人从未收到过诊断2-7早期检测可帮助患有ADRD及其护理人员的人更好地计划，并可能更少冗长且昂贵的医疗保健负担。当前使用生物标志物早期使用生物标志物的研究方法检测是侵入性的，昂贵的，有时是患者无法访问的。美国国家老化呼吁早期检测有效，可扩展和低成本方法的发展（RFA-AG-20-051）。目前，初级保健临床医生为患有ADRD的老年人提供大多数护理。1-5我们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。10，11我们是提议评估嵌入在年度健康访问（AWV）中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 - 成本和可扩展的ADRD检测方法。我们提出的研究以前将利用开发的机器学习模型（被动数字标记）和患者报告的结果（快速痴呆评分量表）。预测我们提出的研究的设计是根据患者筛查进行的观念为适用的诊断和管理服务确定更具针对性的推荐组。我们将进行两项完整的多站点研究，以评估两种可扩展方法的早期有效性检测ADRD。第一项研究将是对三种可扩展方法的临床验证研究。使用EHR数据的被动数字标记（PDM），使用患者的快速痴呆评级量表（QDRS）报告的结果（PRO）嵌入了EHR系统中，以及两者的组合（PDM + QDR）。第二项研究将是两项务实的聚类群集对照比较有效性试验与仅AWV的过程相比，嵌入在AWV中的筛选方法在增加新ADRD的发病率。在研究的最后一年，我们将分享被动数字的代码标记和带有史诗总部的QDR，以确保这些代码可用于任何医疗保健全国史诗般的系统。治疗阿尔茨海默氏病以及患者和看护者所产生的成本的高昂成本，都有形无形的是对公共卫生和美国经济的主要威胁。开发可扩展和低成本整合到EHR数据中的工具和评估将帮助医生早期检测，更多以及更好的诊断和临床意义的护理建议。具有成本效益，可扩展性和无创的迫切需要模型来主动减轻这些成本并延长医疗服务。