Digital detection of dementia (D3): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems

Abstract

Background Early detection of Alzheimer’s disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. Methods We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient’s physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. Discussion This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices. Trial registration ClinicalTrials.gov Identifier: NCT05231954. Registered February 9, 2022.

Design and Rationale for a Real-World Observational Cohort of Patients Living with Progressive Neurological Disease and their care partners: TARGET-NEURO Study

“…Recent work has shown that early identi cation of ADRD risk is possible with a Passive Digital Marker (PDM) derived from EHR data. [29,30] The exploratory aim for participants in the Age cohort will be to routinely screen structured and unstructured EHR data using a PDM for validation across health centers and diverse populations. Over time, incident cognitive impairment and dementia diagnoses will be compared with the PDM predictions.…”

Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review

“…The timely diagnosis of Alzheimer's disease and other related dementias (ADRD) is suboptimal and often occurs 2-5 years after the onset of symptoms. [1][2][3][4][5][6][7][8][9] As stated by the National Institutes of Health (NIH), "the inability to diagnose and treat cognitive impairment results in prolonged and expensive medical care." 10 Furthermore, suppose the development of disease-modifying therapeutics for ADRD is successful.…”

“…1 The current approaches of using cognitive tests, imaging or spinal fluid-based biomarkers for early detection of ADRD are not scalable due to their low acceptance, invasive nature, cost, or lack of accessibility in rural or underserved areas. 9 Thus, the NIH called for developing low-cost, effective, and scalable approaches for early detection of ADRD. 11 Over the past two decades, most American healthcare delivery organizations have adopted electronic health records (EHR).…”

“…14 The traditional ADRD biomarkers that use new active data captured by brain imaging, lumbar puncture, or cognitive performance are expensive, difficult to access, invasive, and time-consuming. 9 Compared to active data collection, passive data collection is more scalable and less burdensome for patients or clinicians. However, utilizing the already existing EHR data is limited by the quality of the data and by the computational complexity and feasibility of converting such passive data into actionable insights.…”

“…In that case, using such therapeutics at a very early stage of ADRD, such as mild cognitive impairment (MCI), is paramount 1 . The current approaches of using cognitive tests, imaging or spinal fluid‐based biomarkers for early detection of ADRD are not scalable due to their low acceptance, invasive nature, cost, or lack of accessibility in rural or underserved areas 9 . Thus, the NIH called for developing low‐cost, effective, and scalable approaches for early detection of ADRD 11…”