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Foucher J, Azizi L, Öijerstedt L, Kläppe U, Ingre C. The usage of population and disease registries as pre-screening tools for clinical trials, a systematic review. Syst Rev 2024; 13:111. [PMID: 38654383 PMCID: PMC11040983 DOI: 10.1186/s13643-024-02533-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE This systematic review aims to outline the use of population and disease registries for clinical trial pre-screening. MATERIALS AND METHODS The search was conducted in the time period of January 2014 to December 2022 in three databases: MEDLINE, Embase, and Web of Science Core Collection. References were screened using the Rayyan software, firstly based on titles and abstracts only, and secondly through full text review. Quality of the included studies was assessed using the List of Included Studies and quality Assurance in Review tool, enabling inclusion of publications of only moderate to high quality. RESULTS The search originally identified 1430 citations, but only 24 studies were included, reporting the use of population and/or disease registries for trial pre-screening. Nine disease domains were represented, with 54% of studies using registries based in the USA, and 62.5% of the studies using national registries. Half of the studies reported usage for drug trials, and over 478,679 patients were identified through registries in this review. Main advantages of the pre-screening methodology were reduced financial burden and time reduction. DISCUSSION AND CONCLUSION The use of registries for trial pre-screening increases reproducibility of the pre-screening process across trials and sites, allowing for implementation and improvement of a quality assurance process. Pre-screening strategies seem under-reported, and we encourage more trials to use and describe their pre-screening processes, as there is a need for standardized methodological guidelines.
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Affiliation(s)
- Juliette Foucher
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
| | - Louisa Azizi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Linn Öijerstedt
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Ulf Kläppe
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Caroline Ingre
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
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Idnay B, Liu J, Fang Y, Hernandez A, Kaw S, Etwaru A, Juarez Padilla J, Ramírez SO, Marder K, Weng C, Schnall R. Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias. J Am Med Inform Assoc 2024; 31:1062-1073. [PMID: 38447587 PMCID: PMC11031244 DOI: 10.1093/jamia/ocae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/13/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants. OBJECTIVE This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies. METHODS A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models. RESULTS Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability. DISCUSSION Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening. CONCLUSION User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.
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Affiliation(s)
- Betina Idnay
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Jianfang Liu
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Yilu Fang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Alex Hernandez
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Shivani Kaw
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Alicia Etwaru
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Janeth Juarez Padilla
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
- New York University Grossman School of Medicine, New York, NY 10016, United States
| | - Sergio Ozoria Ramírez
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
- New York University Steinhardt School of Culture, Education, and Human Development, New York, NY 10003, United States
| | - Karen Marder
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Rebecca Schnall
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
- Mailman School of Public Health, Columbia University, New York, NY 10032, United States
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Bader I, Bader I, Lopes Alves I, Vállez García D, Vellas B, Dubois B, Boada M, Marquié M, Altomare D, Scheltens P, Vandenberghe R, Hanseeuw B, Schöll M, Frisoni GB, Jessen F, Nordberg A, Kivipelto M, Ritchie CW, Grau-Rivera O, Molinuevo JL, Ford L, Stephens A, Gismondi R, Gispert JD, Farrar G, Barkhof F, Visser PJ, Collij LE. Recruitment of pre-dementia participants: main enrollment barriers in a longitudinal amyloid-PET study. Alzheimers Res Ther 2023; 15:189. [PMID: 37919783 PMCID: PMC10621165 DOI: 10.1186/s13195-023-01332-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/13/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND The mismatch between the limited availability versus the high demand of participants who are in the pre-dementia phase of Alzheimer's disease (AD) is a bottleneck for clinical studies in AD. Nevertheless, potential enrollment barriers in the pre-dementia population are relatively under-reported. In a large European longitudinal biomarker study (the AMYPAD-PNHS), we investigated main enrollment barriers in individuals with no or mild symptoms recruited from research and clinical parent cohorts (PCs) of ongoing observational studies. METHODS Logistic regression was used to predict study refusal based on sex, age, education, global cognition (MMSE), family history of dementia, and number of prior study visits. Study refusal rates and categorized enrollment barriers were compared between PCs using chi-squared tests. RESULTS 535/1856 (28.8%) of the participants recruited from ongoing studies declined participation in the AMYPAD-PNHS. Only for participants recruited from clinical PCs (n = 243), a higher MMSE-score (β = - 0.22, OR = 0.80, p < .05), more prior study visits (β = - 0.93, OR = 0.40, p < .001), and positive family history of dementia (β = 2.08, OR = 8.02, p < .01) resulted in lower odds on study refusal. General study burden was the main enrollment barrier (36.1%), followed by amyloid-PET related burden (PCresearch = 27.4%, PCclinical = 9.0%, X2 = 10.56, p = .001), and loss of research interest (PCclinical = 46.3%, PCresearch = 16.5%, X2 = 32.34, p < .001). CONCLUSIONS The enrollment rate for the AMYPAD-PNHS was relatively high, suggesting an advantage of recruitment via ongoing studies. In this observational cohort, study burden reduction and tailored strategies may potentially improve participant enrollment into trial readiness cohorts such as for phase-3 early anti-amyloid intervention trials. The AMYPAD-PNHS (EudraCT: 2018-002277-22) was approved by the ethical review board of the VU Medical Center (VUmc) as the Sponsor site and in every affiliated site.
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Affiliation(s)
- Ilse Bader
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV, Amsterdam, The Netherlands.
| | - Ilona Bader
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, 1081 HV, The Netherlands
| | - Isadora Lopes Alves
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, 1081 HV, The Netherlands
- Brain Research Center, 1081 GN, Amsterdam, The Netherlands
| | - David Vállez García
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, 1081 HV, The Netherlands
| | - Bruno Vellas
- Gérontopole of Toulouse, University Hospital of Toulouse (CHU-Toulouse), 31300, Toulouse, France
- UMR INSERM 1027, University of Toulouse III, 31062, Toulouse, France
| | - Bruno Dubois
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain Institute, Salpetriere Hospital, Sorbonne University, 75013, Paris, France
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Daniele Altomare
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, 25123, Brescia, Italy
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV, Amsterdam, The Netherlands
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, 3001, Louvain, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, 1200, Brussels, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, 1200, Brussels, Belgium
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02155, USA
- WELBIO Department, WEL Research Institute, Avenue Pasteur, 6, 1300, Wavre, Belgium
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 405 30, Gothenburg, Sweden
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30, Gothenburg, Sweden
- Dementia Research Centre, Queen Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet, 171 77, Stockholm, Sweden
- Theme Inflammation, Karolinska University Hospital, Stockholm, 171 77, Sweden
- Theme Aging, Karolinska University Hospital, Stockholm, 171 77, Sweden
| | - Miia Kivipelto
- Kuopio University Hospital, 70210, Kuopio, Finland
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet, 171 77, Stockholm, Sweden
- Imperial College London, London, SW7 2AZ, UK
| | | | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005, Barcelona, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005, Barcelona, Spain
- H. Lundbeck A/S, 2500, Copenhagen, Denmark
| | - Lisa Ford
- Janssen Research and Development, Titusville, NJ, 08560, USA
| | | | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005, Barcelona, Spain
| | - Gill Farrar
- GE Healthcare, Pharmaceutical Diagnostics, Amersham, HP7 9LL, UK
| | - Frederik Barkhof
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, 1081 HV, The Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, WC1N 3BG, UK
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Lyduine E Collij
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, 1081 HV, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, 221 00, Malmö, Sweden
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Leroy V, Gana W, Aïdoud A, N'kodo JA, Balageas AC, Blanc P, Bomia D, Debacq C, Fougère B. Digital health technologies and Alzheimer's disease clinical trials: might decentralized clinical trials increase participation by people with cognitive impairment? Alzheimers Res Ther 2023; 15:87. [PMID: 37106429 PMCID: PMC10133908 DOI: 10.1186/s13195-023-01227-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/05/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Therapeutic trials in Alzheimer's disease (AD) face many obstacles-particularly with regard to screening and recruitment. DISCUSSION Decentralized clinical trials (DCTs) are being developed in other diseases and appear to be of value for overcoming these difficulties. The use of remote visits offers hope of broader recruitment and thus a reduction in inequalities due to age, geography, and ethnicity. Furthermore, it might be easier to involve primary care providers and caregivers in DCTs. However, further studies are needed to determine the feasibility of DCTs in AD. A mixed-model DCT might constitute the first step towards completely remote trials in AD and should be assessed first.
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Affiliation(s)
- Victoire Leroy
- Department of Geriatrics, Tours University Hospital, Tours, France.
- Memory Clinic, Tours University Hospital, Tours, France.
| | - Wassim Gana
- Department of Geriatrics, Tours University Hospital, Tours, France
| | - Amal Aïdoud
- Department of Geriatrics, Tours University Hospital, Tours, France
- EA4245 T2i, Université de Tours, Tours, France
| | | | | | - Pascal Blanc
- Department of Geriatrics, Tours University Hospital, Tours, France
- Memory Clinic, Tours University Hospital, Tours, France
| | | | - Camille Debacq
- Department of Geriatrics, Tours University Hospital, Tours, France
| | - Bertrand Fougère
- Department of Geriatrics, Tours University Hospital, Tours, France
- EA 7505 Education, Ethics, Health, Tours University, Tours, France
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Saunders S, Gregory S, Clement MHS, Birck C, van der Geyten S, Ritchie CW. The European Prevention of Alzheimer's Dementia Programme: An Innovative Medicines Initiative-funded partnership to facilitate secondary prevention of Alzheimer's disease dementia. Front Neurol 2022; 13:1051543. [PMID: 36484017 PMCID: PMC9723139 DOI: 10.3389/fneur.2022.1051543] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/28/2022] [Indexed: 08/08/2023] Open
Abstract
INTRODUCTION Tens of millions of people worldwide will develop Alzheimer's disease (AD), and only by intervening early in the preclinical disease can we make a fundamental difference to the rates of late-stage disease where clinical symptoms and societal burden manifest. However, collectively utilizing data, samples, and knowledge amassed by large-scale projects such as the Innovative Medicines Initiative (IMI)-funded European Prevention of Alzheimer's Dementia (EPAD) program will enable the research community to learn, adapt, and implement change. METHOD In the current article, we define and discuss the substantial assets of the EPAD project for the scientific community, patient population, and industry, describe the EPAD structure with a focus on how the public and private sector interacted and collaborated within the project, reflect how IMI specifically supported the achievements of the above, and conclude with a view for future. RESULTS The EPAD project was a €64-million investment to facilitate secondary prevention of AD dementia research. The project recruited over 2,000 research participants into the EPAD longitudinal cohort study (LCS) and included over 400 researchers from 39 partners. The EPAD LCS data and biobank are freely available and easily accessible via the Alzheimer's Disease Data Initiative's (ADDI) AD Workbench platform and the University of Edinburgh's Sample Access Committee. The trial delivery network established within the EPAD program is being incorporated into the truly global offering from the Global Alzheimer's Platform (GAP) for trial delivery, and the almost 100 early-career researchers who were part of the EPAD Academy will take forward their experience and learning from EPAD to the next stage of their careers. DISCUSSION Through GAP, IMI-Neuronet, and follow-on funding from the Alzheimer's Association for the data and sample access systems, the EPAD assets will be maintained and, as and when sponsors seek a new platform trial to be established, the learnings from EPAD will ensure that this can be developed to be even more successful than this first pan-European attempt.
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Affiliation(s)
- Stina Saunders
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Serge van der Geyten
- Janssen Research and Development, Division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Craig W. Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Brain Health Scotland, Edinburgh, United Kingdom
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Tsujimoto M, Suzuki K, Saji N, Sakurai T, Ito K, Toba K. Organized Registration for the Assessment of Dementia by the Nationwide General Consortium Toward Effective Treatment (ORANGE) Registry: Current Status and Perspectives of Mild Cognitive Impairment. J Alzheimers Dis 2022; 88:1423-1433. [DOI: 10.3233/jad-220039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: With increasingly aging societies, a comprehensive strategy for dementia research is important. The Organized Registration for the Assessment of dementia by the Nationwide General consortium toward Effective treatment (ORANGE) Registry is the first longitudinal multicenter prospective trial-ready cohort in Japan. Objective: To establish a large cohort for use in clinical trials and research in Japan. Methods: This registry, based on communities, hospitals, and nursing homes, covers three dementia stages (preclinical, mild cognitive impairment [MCI], and advanced dementia), and includes more than 30 hospitals. We analyzed enrollment and 1-year follow-up data for disease progression. Results: There were 1450 registered patients (649 men, 801 women; mean age, 77.92±6.70 years; mean Mini-Mental State Examination [MMSE] score, 25.19±2.76). The conversion rates from MCI to dementia and MCI to normal were 14.3% and 1.1%, respectively. High Clinical Dementia Rating score (odds ratio [OR] = 11.085, 95% confidence interval [CI]:1.619–75.913, p = 0.014), low MMSE score (OR = 0.835, 95% CI: 0.761–0.917, p < 0.001), high Geriatric Depression Scale score (OR = 1.093, 95% CI: 1.005–1.189, p = 0.038), and low body mass index (OR = 0.895, 95% CI: 0.829–0.967, p = 0.005) at enrollment were significant factors for conversion. Conclusion: The ORANGE MCI Registry is an established registry that facilitates creation of trial-ready cohorts to accelerate promotion of clinical trials with low reversion rates as it originates from a hospital. One-year follow-up analysis suggested assessing various factors for conversion risk. Further analyses will be possible in future with registry expansion. We will continue to refine this registry, including how it can be used more efficiently.
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Affiliation(s)
- Masashi Tsujimoto
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Keisuke Suzuki
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Naoki Saji
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Takashi Sakurai
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kengo Ito
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- Centre for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kenji Toba
- Centre for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
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Aisen PS, Jimenez-Maggiora GA, Rafii MS, Walter S, Raman R. Early-stage Alzheimer disease: getting trial-ready. Nat Rev Neurol 2022; 18:389-399. [PMID: 35379951 PMCID: PMC8978175 DOI: 10.1038/s41582-022-00645-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2022] [Indexed: 12/15/2022]
Abstract
Slowing the progression of Alzheimer disease (AD) might be the greatest unmet medical need of our time. Although one AD therapeutic has received a controversial accelerated approval from the FDA, more effective and accessible therapies are urgently needed. Consensus is growing that for meaningful disease modification in AD, therapeutic intervention must be initiated at very early (preclinical or prodromal) stages of the disease. Although the methods for such early-stage clinical trials have been developed, identification and recruitment of the required asymptomatic or minimally symptomatic study participants takes many years and requires substantial funds. As an example, in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease Trial (the first phase III trial to be performed in preclinical AD), 3.5 years and more than 5,900 screens were required to recruit and randomize 1,169 participants. A new clinical trials infrastructure is required to increase the efficiency of recruitment and accelerate therapeutic progress. Collaborations in North America, Europe and Asia are now addressing this need by establishing trial-ready cohorts of individuals with preclinical and prodromal AD. These collaborations are employing innovative methods to engage the target population, assess risk of brain amyloid accumulation, select participants for biomarker studies and determine eligibility for trials. In the future, these programmes could provide effective tools for pursuing the primary prevention of AD. Here, we review the lessons learned from the AD trial-ready cohorts that have been established to date, with the aim of informing ongoing and future efforts towards efficient, cost-effective trial recruitment. Consensus is growing that intervention in the very early stages of Alzheimer disease is necessary for disease modification. Here, the authors discuss the challenges of recruiting asymptomatic or mildly symptomatic participants for clinical trials, focusing on ‘trial-ready’ cohorts as a potential solution. Trial-ready cohorts are an effective strategy for the identification of participants eligible for clinical trials in early-stage Alzheimer disease (AD). Building these cohorts requires considerable planning and technological infrastructure to facilitate recruitment, remote longitudinal assessment, data management and data storage. Trial-ready cohorts exist for genetically determined populations at risk of AD, such as those with familial AD and Down syndrome; the longitudinal data from these cohorts is improving our understanding of the disease progression in early stages, informing clinical trial design and accelerating recruitment to intervention studies. So far, the challenges experienced by trial-ready cohorts for early-stage AD have included difficulties recruiting an ethnically and racially representative cohort; and for online cohorts, difficulty retaining participants. The results of ongoing work will reveal the success of strategies to improve cohort diversity and retention, and the rates of referral to clinical trials.
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Ingala S, De Boer C, Masselink LA, Vergari I, Lorenzini L, Blennow K, Chételat G, Di Perri C, Ewers M, van der Flier WM, Fox NC, Gispert JD, Haller S, Molinuevo JL, Muniz‐Terrera G, Mutsaerts HJMM, Ritchie CW, Ritchie K, Schmidt M, Schwarz AJ, Vermunt L, Waldman AD, Wardlaw J, Wink AM, Wolz R, Wottschel V, Scheltens P, Visser PJ, Barkhof F. Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort. Alzheimers Dement 2021; 17:1189-1204. [PMID: 33811742 PMCID: PMC8359976 DOI: 10.1002/alz.12292] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/11/2020] [Accepted: 12/22/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND We classified non-demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles. MATERIALS AND METHODS From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut-offs of 1000 pg/mL for amyloid beta (Aß)1-42 and 27 pg/mL for p-tau181 were validated using Gaussian mixture models. Given strong correlation of p-tau and t-tau (R2 = 0.98, P < 0.001), neurodegeneration was defined by age-adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages. RESULTS Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001). DISCUSSION In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Casper De Boer
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Larissa A Masselink
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Ilaria Vergari
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Kaj Blennow
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Gaël Chételat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders,”Institut Blood and Brain @ Caen‐NormandieCyceronCaenFrance
| | - Carol Di Perri
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Michael Ewers
- Institute for Stroke and Dementia ResearchKlinikum der Universitat MünchenLudwig‐Maximilians‐Universitat LMUMunichGermany
| | - Wiesje M van der Flier
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Nick C Fox
- Dementia Research CentreDepartment of Neurodegenerative Disease & UK Dementia Research InstituteInstitute of NeurologyUniversity College LondonLondonUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Sven Haller
- CIRD Centre d'Imagerie Rive DroiteGenevaSwitzerland
| | - José Luís Molinuevo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Hopsital Clínic‐IDIBAPSAlzheimer's Disease & Other Cognitive Disorders UnitBarcelonaSpain
| | - Graciela Muniz‐Terrera
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Henri JMM Mutsaerts
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI)Ghent UniversityGhentBelgium
| | - Craig W Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Karen Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Adam J Schwarz
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
| | - Lisa Vermunt
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Adam D Waldman
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Joanna Wardlaw
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Alle Meije Wink
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | | | - Viktor Wottschel
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Philip Scheltens
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Department of Psychiatry & NeuropsychologySchool for Mental Health and NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Institutes of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - the EPAD consortium
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
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9
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Petersen RC, Wiste HJ, Weigand SD, Fields JA, Geda YE, Graff‐Radford J, Knopman DS, Kremers WK, Lowe V, Machulda MM, Mielke MM, Stricker NH, Therneau TM, Vemuri P, Jack CR. NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages. Ann Neurol 2021; 89:1145-1156. [PMID: 33772866 PMCID: PMC8131266 DOI: 10.1002/ana.26071] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND To operationalize the National Institute on Aging - Alzheimer's Association (NIA-AA) Research Framework for Alzheimer's Disease 6-stage continuum of clinical progression for persons with abnormal amyloid. METHODS The Mayo Clinic Study of Aging is a population-based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross-sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months. RESULTS Among 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44-59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short-term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1). INTERPRETATION The frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145-1156.
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Affiliation(s)
| | | | | | - Julie A. Fields
- Department of Psychiatry and PsychologyMayo ClinicRochesterMN
| | - Yonas E. Geda
- Department of NeurologyBarrow Neurological InstitutePhoenixAZ
| | | | | | | | - Val Lowe
- Department of RadiologyMayo ClinicRochesterMN
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10
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Sato K, Ihara R, Suzuki K, Niimi Y, Toda T, Jimenez‐Maggiora G, Langford O, Donohue MC, Raman R, Aisen PS, Sperling RA, Iwata A, Iwatsubo T. Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial-Ready Cohort study. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12135. [PMID: 33778148 PMCID: PMC7988864 DOI: 10.1002/trc2.12135] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/01/2020] [Accepted: 12/08/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD). METHODS Based on the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine-learning models and applied them to our ongoing Japanese Trial-Ready Cohort (J-TRC) webstudy participants registered within the first 9 months (n = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography. RESULTS Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J-TRC webstudy participants with known amyloid status (n = 37), the predicted SUVr corresponded well with the self-reported amyloid test results (area under the curve = 0.806 [0.619-0.992]). DISCUSSION Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J-TRC webstudy to in-person study, maximizing efficiency for the identification of preclinical AD participants.
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Affiliation(s)
- Kenichiro Sato
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Ryoko Ihara
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Kazushi Suzuki
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Tatsushi Toda
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Gustavo Jimenez‐Maggiora
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Oliver Langford
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Michael C. Donohue
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Rema Raman
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | | | - Atsushi Iwata
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Department of NeurologyTokyo Metropolitan Geriatric Medical Center HospitalTokyoJapan
| | - Takeshi Iwatsubo
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
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11
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Zwan MD, van der Flier WM, Cleutjens S, Schouten TC, Vermunt L, Jutten RJ, van Maurik IS, Sikkes SA, Flenniken D, Howell T, Weiner MW, Scheltens P, Prins ND. Dutch Brain Research Registry for study participant recruitment: Design and first results. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12132. [PMID: 33614897 PMCID: PMC7882519 DOI: 10.1002/trc2.12132] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/11/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION The Dutch Brain Research Registry aims to facilitate online recruitment of participants for brain disease studies. METHODS Registrants were primarily recruited through an online social media campaign. The registration process included a short questionnaire, which was subsequently used in the prescreening process to match participants to studies. RESULTS In the first 18 months, 17,218 registrants signed up (58±11 years old, 78% female). Out of 34,696 study invitations that were sent, 36% were accepted by registrants, of which 50% to 84% were finally enrolled, resulting in 10,661 participants in 28 studies. Compared to non-participants, study participants were more often older, male, more highly educated, retired or unemployed, non-smoking, healthier, and more often had a family member with dementia. DISCUSSION The Dutch Brain Research Registry facilitates effective matching of participants to brain disease studies. Participant factors related to study enrollment may reflect facilitators or barriers for participation, which is useful for improving recruitment strategies.
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Affiliation(s)
- Marissa D. Zwan
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Wiesje M. van der Flier
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Solange Cleutjens
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Tamara C Schouten
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Lisa Vermunt
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
- Department of Clinical ChemistryNeurochemistry LaboratoryAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Roos J. Jutten
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Ingrid S. van Maurik
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Sietske A.M. Sikkes
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Derek Flenniken
- Center for Imaging of Neurodegenerative Diseases (CIND)San Francisco Veterans Affair Medical CenterSan FranciscoCaliforniaUSA
| | - Taylor Howell
- Center for Imaging of Neurodegenerative Diseases (CIND)San Francisco Veterans Affair Medical CenterSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Center for Imaging of Neurodegenerative Diseases (CIND)San Francisco Veterans Affair Medical CenterSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Philip Scheltens
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Niels D. Prins
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceAmsterdam University Medical CenterAmsterdamthe Netherlands
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12
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Ashford MT, Neuhaus J, Jin C, Camacho MR, Fockler J, Truran D, Mackin RS, Rabinovici GD, Weiner MW, Nosheny RL. Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12102. [PMID: 33005723 PMCID: PMC7513627 DOI: 10.1002/dad2.12102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 07/30/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging. METHODS Aβ positron emission tomography (PET) was obtained from multiple in-clinic studies. Using logistic regression, we predicted Aβ using self-report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross-validated area under the curve (cAUC) evaluated the predictive performance. RESULTS The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short-Form, self-reported Everyday Cognition, and self-reported cognitive impairment. The cross-validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations. DISUCSSION The findings suggest that a novel, online approach could aid in Aβ prediction.
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Affiliation(s)
- Miriam T. Ashford
- Northern California Institute for Research and Education (NCIRE)Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - John Neuhaus
- Department of Epidemiology and BiostatisticsUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Chengshi Jin
- Department of Epidemiology and BiostatisticsUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Monica R. Camacho
- Northern California Institute for Research and Education (NCIRE)Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Juliet Fockler
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Diana Truran
- Northern California Institute for Research and Education (NCIRE)Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - R. Scott Mackin
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Gil D. Rabinovici
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Northern California Institute for Research and Education (NCIRE)Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Rachel L. Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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