Digital Detection of Dementia (D Cubed) Studies: D3

NCT ID: NCT06224205

Last Updated: 2025-10-27

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 3150 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-11-08
• Study Completion Date: 2025-11-08

Brief Summary

The specific aim of the pragmatic trial is to evaluate the practical utility and effect of the PDM, the QDRS, and the combined approach (PDM + QDRS) in improving the annual rate of new documented ADRD diagnosis in primary care practices.

Detailed Description

Alzheimer's disease and related dementias (ADRD) negatively impact millions of Americans with an annual societal cost of more than $200 million.1 Currently, half of Americans living with ADRD never receive a diagnosis.2-7 For those who do, the diagnosis often occurs two to five years after the onset of symptoms.6-9 As stated by the National Institute on Aging (NIA) (RFA-AG-20-051) "The inability to diagnose and treat cognitive impairment results in prolonged and expensive medical care" and "early detection could help persons with dementia and their care partners plan for the future". Furthermore, if the development of disease modifying therapeutics for ADRD is successful, this may require the use of such therapeutics at a very early stage of ADRD.1 However, the current approaches of using cognitive tests or biomarkers for early detection of ADRD are not scalable due to their low acceptance, their invasive nature, their cost, or their lack of accessibility in rural or underserved areas. Thus, the NIA called out for the development of low cost, effective, and scalable approaches for early detection of ADRD (RFA-AG-20-051).

In response to the RFA-AG-20-051 call for the "validation, and translation of screening and assessment tools for measuring cognitive decline a pragmatic cluster-randomized controlled comparative effectiveness (NIH Stage IV) trial will be executed in Eskenazi Health in central Indiana and one additional replicated pragmatic trial among patients from diverse rural, suburban and urban primary care practices in south Florida. The pragmatic trial will incorporate the Passive Digital Marker (PDM) and the Quick Dementia Rating Scale (QDRS) within the Medicare paid Annual Wellness Visit (AWV) for a cohort of patients from practices across the two independent sites, with practices randomized in each pragmatic trial to one of the 3 arms (AWV alone, the AWV with PDM and the PDM and the QDRS).

Quick Dementia Rating Scale (QDRS)- is a validated patient reported outcome (PRO) tool.

Passive Digital Marker (PDM) - is a Machine Learning (ML) algorithm which can predict ADRD one year and three years prior to its onset by using routine care electronic health record (EHR) data. The algorithm was trained using structured and unstructured data from three EHR datasets: diagnosis (Dx), prescriptions (Rx), and medical notes (Nx). Individual algorithms derived from each of the three datasets were developed and compared to a combined one that included all three datasets.

Conditions

Alzheimer Disease and Related Dementias (ADRD)

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: DOUBLE

Study Groups

Annual Well Visit or any other visit to Primary Care Doctor

Annual Well Visit or any other visit to Primary Care Doctor: This is the usual care arm. Electronic Health Record Data for patients from the clinics randomized to usual care will be collected for comparison with the other 2 arms. Patients from these primary care clinics must have had a visit to their doctor either as an annual well visit (AWV) or any other type of visit. These clinics will not have to do anything for the study but run their business as usual without altering anything.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Passive Digital Marker (PDM)

Passive Digital Marker (PDM): Electronic Health Record Data from those clinics randomized to PDM will be run through the PDM, a machine learning algorithm which can predict ADRD one year and three years prior to its onset.

Group Type: EXPERIMENTAL

Passive Digital Marker for screening for ADRD

Intervention Type: OTHER

Patients in the primary care clinics randomized to PDM+QDRS will have Electronic Health Record Data of their patients run through the PDM, a machine learning algorithm which can predict ADRD one year and three years prior to its onset. In addition, patients from these clinics will have their patients complete the QDRS, a validated patient reported outcome (PRO) tool. This combined approach will assess the value of early detection of ADRD and if the annual well visit can overcome the barriers related to early detection of ADRD.

Passive Digital Marker (PDM) + Quick Dementia Rating Scale (QDRS)

Patients in the primary care clinics randomized to PDM+QDRS will have Electronic Health Record Data of their patients run through the PDM, a machine learning algorithm which can predict ADRD one year and three years prior to its onset. In addition, patients from these clinics will have their patients complete the QDRS, a validated patient reported outcome (PRO) tool. This combined approach will assess the value of early detection of ADRD and if the annual well visit can overcome the barriers related to early detection of ADRD.

Group Type: ACTIVE_COMPARATOR

Passive Digital Marker for screening for ADRD

Intervention Type: OTHER

Patients in the primary care clinics randomized to PDM+QDRS will have Electronic Health Record Data of their patients run through the PDM, a machine learning algorithm which can predict ADRD one year and three years prior to its onset. In addition, patients from these clinics will have their patients complete the QDRS, a validated patient reported outcome (PRO) tool. This combined approach will assess the value of early detection of ADRD and if the annual well visit can overcome the barriers related to early detection of ADRD.

Interventions

Passive Digital Marker for screening for ADRD

Patients in the primary care clinics randomized to PDM+QDRS will have Electronic Health Record Data of their patients run through the PDM, a machine learning algorithm which can predict ADRD one year and three years prior to its onset. In addition, patients from these clinics will have their patients complete the QDRS, a validated patient reported outcome (PRO) tool. This combined approach will assess the value of early detection of ADRD and if the annual well visit can overcome the barriers related to early detection of ADRD.

Intervention Type: OTHER

Other Intervention Names

Patient reported outcome (QDRS) for screening for ADRD

Eligibility Criteria

Inclusion Criteria

• 65 years or older
• At least one visit to primary care practice within the past year
• Ability to provide informed consent
• Ability to communicate in English or Spanish
• Available EHR data from at least the past three years

Exclusion Criteria

• Prior ADRD or mild cognitive impairment diagnosis as determined by ICD-10 code
• Evidence of any history of prescription for a cholinesterase inhibitors or memantine.
• Has serious mental illness such as bipolar or schizophrenia as determined by ICD-10 code
• Permanent resident of a nursing facility

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute on Aging (NIA)

NIH

Sponsor Role: collaborator

Indiana University

OTHER

Sponsor Role: lead

Responsible Party

MALAZ BOUSTANI

Professor of Aging Research

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Malaz Boustani, MD, MPH

Role: PRINCIPAL_INVESTIGATOR

Indiana University

Locations

University of Miami School of Medicine

Boca Raton, Florida, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Malaz Boustani, MD, MPH

Role: CONTACT

mboustan@iu.edu

317-274-8536

Katrina Coppedge, BA

Role: CONTACT

kcoppedg@iu.edu

317-278-1602

Facility Contacts

James Galvin, MD

Role: primary

Other Identifiers

R01AG069765-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2008372812b

Identifier Type: -

Identifier Source: org_study_id