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Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems
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 app...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552361/ https://www.ncbi.nlm.nih.gov/pubmed/36221141 http://dx.doi.org/10.1186/s13063-022-06809-5 |
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author | Kleiman, Michael J. Plewes, Abbi D. Owora, Arthur Grout, Randall W. Dexter, Paul Richard Fowler, Nicole R. Galvin, James E. Miled, Zina Ben Boustani, Malaz |
author_facet | Kleiman, Michael J. Plewes, Abbi D. Owora, Arthur Grout, Randall W. Dexter, Paul Richard Fowler, Nicole R. Galvin, James E. Miled, Zina Ben Boustani, Malaz |
author_sort | Kleiman, Michael J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9552361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95523612022-10-12 Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems Kleiman, Michael J. Plewes, Abbi D. Owora, Arthur Grout, Randall W. Dexter, Paul Richard Fowler, Nicole R. Galvin, James E. Miled, Zina Ben Boustani, Malaz Trials Study Protocol 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. BioMed Central 2022-10-11 /pmc/articles/PMC9552361/ /pubmed/36221141 http://dx.doi.org/10.1186/s13063-022-06809-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Study Protocol Kleiman, Michael J. Plewes, Abbi D. Owora, Arthur Grout, Randall W. Dexter, Paul Richard Fowler, Nicole R. Galvin, James E. Miled, Zina Ben Boustani, Malaz Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title | Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title_full | Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title_fullStr | Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title_full_unstemmed | Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title_short | Digital detection of dementia (D(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
title_sort | digital detection of dementia (d(3)): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552361/ https://www.ncbi.nlm.nih.gov/pubmed/36221141 http://dx.doi.org/10.1186/s13063-022-06809-5 |
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