- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06224205
Digital Detection of Dementia (D Cubed) Studies: D3
Study Overview
Status
Intervention / Treatment
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.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Malaz Boustani, MD, MPH
- Phone Number: 317-274-8536
- Email: mboustan@iu.edu
Study Contact Backup
- Name: Katrina Coppedge, BA
- Phone Number: 317-278-1602
- Email: kcoppedg@iu.edu
Study Locations
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Florida
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Boca Raton, Florida, United States, 33431 -6437
- Recruiting
- University of Miami School of Medicine
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Contact:
- James Galvin, MD
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Older Adult
Accepts Healthy Volunteers
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
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No Intervention: 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.
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|
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Experimental: 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.
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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.
Other Names:
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Active Comparator: 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.
|
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.
Other Names:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Incidence of ADRD
Time Frame: 12 months
|
The primary outcome measure will be any new ADRD case identified (documented in the EHR) within 12 months of the Annual Wellness Visit (index visit).
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12 months
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Incidence of ADRD services
Time Frame: 12 months after index date
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The secondary outcome measures will be any services related to cognitive diagnostic assessment in the post Annual Wellness Visit (index) period that providers may order to diagnose or exclude ADRD. Specifically, the metrics of diagnostic assessment will be evaluated as proportions of patients with a record of 1 or more of:
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12 months after index date
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Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Malaz Boustani, MD, MPH, Indiana University
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Brain Diseases
- Central Nervous System Diseases
- Nervous System Diseases
- Mental Disorders
- Neurocognitive Disorders
- Dementia
- Tauopathies
- Neurodegenerative Diseases
- Alzheimer Disease
- Health Services Administration
- Health Care Quality, Access, and Evaluation
- Investigative Techniques
- Epidemiologic Methods
- Diagnostic Techniques and Procedures
- Diagnosis
- Data Collection
- Health Care Evaluation Mechanisms
- Quality of Health Care
- Public Health
- Environment and Public Health
- Health Services
- Health Care Facilities Workforce and Services
- Preventive Health Services
- Health Care Economics and Organizations
- Outcome Assessment, Health Care
- Outcome and Process Assessment, Health Care
- Diagnostic Services
- Health Surveys
- Surveys and Questionnaires
- Public Health Practice
- Health Planning
- Health Care Surveys
- Health Services Research
- Patient Outcome Assessment
- Mass Screening
- Patient Reported Outcome Measures
Other Study ID Numbers
- 2008372812b
- R01AG069765-01 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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