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

Michael J Kleiman, Abbi D Plewes, Arthur Owora, Randall W Grout, Paul Richard Dexter, Nicole R Fowler, James E Galvin, Zina Ben Miled, Malaz Boustani, Michael J Kleiman, Abbi D Plewes, Arthur Owora, Randall W Grout, Paul Richard Dexter, Nicole R Fowler, James E Galvin, Zina Ben Miled, Malaz Boustani

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.

Keywords: Alzheimer’s disease and related dementias; Clinical decision support; Electronic health records; Machine learning; Patient reported outcome.

Conflict of interest statement

RWG has received unrelated institutional grant funding from Pfizer. No other authors have anything to disclose. The NIH had no input on the design of the study or the collection, analysis, and interpretation of data, or in writing the manuscript.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Our two trials are highly pragmatic as indicated by scores of 4 and 5 (‘most pragmatic’) in the PRECIS-2 summary wheel depicted here. The diverse primary care settings, minimal inclusion/exclusion criteria, highly relevant clinical outcomes to patients and providers, close EHR-based data follow-up, and expertise in developing scalable low-cost cognitive assessments results in a very pragmatic design. We have strong partnerships with local primary care clinics. Patient input was received in designing QDRS and study flow. Other pragmatic elements include not asking clinicians to deny patients any routine procedures such as cognitive tests and using a variety of patient identification methods

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