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：
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
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/10266121
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 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

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.

项目概要/摘要每年，阿尔茨海默病和相关痴呆症 (ADRD) 都会给数百万美国人带来不利影响。社会成本超过 2 亿美元。1 同时，患有 ADRD 的美国人中有一半从未获得过治疗诊断。2-7 早期检测有助于 ADRD 患者及其护理人员更好地制定计划，并有可能减少目前使用生物标志物进行早期治疗的研究方法造成了漫长且昂贵的负担。国家老龄化研究所呼吁，这种检测是侵入性的、成本高昂，而且有时患者无法获得。开发有效、可扩展且低成本的方法来早期检测 ADRD (RFA-AG-20-051)。目前，初级保健监护人为患有 ADRD 的老年人提供大部分护理。1-5 跨学科科学团队开发并测试了可扩展的早期检测方法。10, 11 我们正在提议评估嵌入年度健康访问 (AWV) 的综合方法，该方法利用电子健康记录系统、机器学习模型和患者报告的结果，以部署低我们提出的研究将利用以前的方法来早期检测 ADRD。开发了机器学习模型（被动数字标记）和患者报告的结果（快速痴呆症）我们提出的研究的设计基于这样的概念：进行患者筛查是为了我们将确定更有针对性的转诊群体以获取适用的诊断和管理服务。两项互补的多地点研究，以评估两种可扩展方法的有效性第一项研究将是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定量表 (QDRS) 嵌入 EHR 系统中的报告结果 (PRO) 以及两者的组合 (PDM + QDRS)。第二项研究将是一项实用的整群随机对照有效性试验与仅使用 AWV 的流程相比，嵌入 AWV 的筛选方法可以提高在研究的最后一年，我们将分享被动数字的代码。 Marker 和 Epic 总部的 QDRS 确保这些代码可用于任何医疗保健全国范围内与Epic系统。治疗阿尔茨海默病的高昂费用以及患者和护理人员产生的有形费用和无形的，是对公共卫生和美国经济的主要威胁。集成到 EHR 数据中的仪器和评估将帮助医生进行早期检测、更多和更好的诊断和具有临床意义的护理建议，具有成本效益、可扩展且无创。迫切需要模型来主动降低这些成本并延长医疗护理时间。