Digital Detection of Dementia Studies (D cubed Studies).
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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.
