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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：
10092237
负责人：
MALAZ BOUSTANI
金额：
$ 109.43万
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/10092237
关键词：
Affect Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease therapeutic American Area 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 screening societal costs suburb tool 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.

项目总结/文摘