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Rhodius-Meester HFM, van Maurik IS, Collij LE, van Gils AM, Koikkalainen J, Tolonen A, Pijnenburg YAL, Berkhof J, Barkhof F, van de Giessen E, Lötjönen J, van der Flier WM. Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET. PLoS One 2024; 19:e0303111. [PMID: 38768188 PMCID: PMC11104589 DOI: 10.1371/journal.pone.0303111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.
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Affiliation(s)
- Hanneke F. M. Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aniek M. van Gils
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | | | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Elsmarieke van de Giessen
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
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van Gils AM, Rhodius-Meester HFM, Handgraaf D, Hendriksen HMA, van Strien A, Schoonenboom N, Schipper A, Kleijer M, Griffioen A, Muller M, Tolonen A, Lötjönen J, van der Flier WM, Visser LNC. Use of a digital tool to support the diagnostic process in memory clinics-a usability study. Alzheimers Res Ther 2024; 16:75. [PMID: 38589933 PMCID: PMC11003066 DOI: 10.1186/s13195-024-01433-8] [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: 10/27/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report). METHODS We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians). RESULTS We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability). CONCLUSION This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.
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Affiliation(s)
- Aniek M van Gils
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands.
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Dédé Handgraaf
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
| | - Heleen M A Hendriksen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
| | - Astrid van Strien
- Department of Geriatric medicine, Jeroen Bosch Ziekenhuis, Den Bosch, The Netherlands
| | | | - Annemieke Schipper
- Department of Neurology, HagaZiekenhuis, location Zoetermeer, Zoetermeer, The Netherlands
| | - Mariska Kleijer
- Department of Neurology, HagaZiekenhuis, location Zoetermeer, Zoetermeer, The Netherlands
| | - Annemiek Griffioen
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | | | | | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Epidemiology and Data Sciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Leonie N C Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Medical Psychology, Amsterdam UMC location University of Amsterdam/AMC, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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3
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Hendriksen HMA, van Gils AM, van Harten AC, Hartmann T, Mangialasche F, Kamondi A, Kivipelto M, Rhodius-Meester HFM, Smets EMA, van der Flier WM, Visser LNC. Communication about diagnosis, prognosis, and prevention in the memory clinic: perspectives of European memory clinic professionals. Alzheimers Res Ther 2023; 15:131. [PMID: 37543608 PMCID: PMC10404377 DOI: 10.1186/s13195-023-01276-9] [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: 03/20/2023] [Accepted: 07/19/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND The paradigm shift towards earlier Alzheimer's disease (AD) stages and personalized medicine creates new challenges for clinician-patient communication. We conducted a survey among European memory clinic professionals to identify opinions on communication about (etiological) diagnosis, prognosis, and prevention, and inventory needs for augmenting communication skills. METHODS Memory clinic professionals (N = 160) from 21 European countries completed our online survey (59% female, 14 ± 10 years' experience, 73% working in an academic hospital). We inventoried (1) opinions on communication about (etiological) diagnosis, prognosis, and prevention using 11 statements; (2) current communication practices in response to five hypothetical cases (AD dementia, mild cognitive impairment (MCI), subjective cognitive decline (SCD), with ( +) or without ( -) abnormal AD biomarkers); and (3) needs for communication support regarding ten listed communication skills. RESULTS The majority of professionals agreed that communication on diagnosis, prognosis, and prevention should be personalized to the individual patient. In response to the hypothetical patient cases, disease stage influenced the inclination to communicate an etiological AD diagnosis: 97% would explicitly mention the presence of AD to the patient with AD dementia, 68% would do so in MCI + , and 29% in SCD + . Furthermore, 58% would explicitly rule out AD in case of MCI - when talking to patients, and 69% in case of SCD - . Almost all professionals (79-99%) indicated discussing prognosis and prevention with all patients, of which a substantial part (48-86%) would personalize their communication to patients' diagnostic test results (39-68%) or patients' anamnestic information (33-82%). The majority of clinicians (79%) would like to use online tools, training, or both to support them in communicating with patients. Topics for which professionals desired support most were: stimulating patients' understanding of information, and communicating uncertainty, dementia risk, remotely/online, and with patients not (fluently) speaking the language of the country of residence. CONCLUSIONS In a survey of European memory clinic professionals, we found a strong positive attitude towards communication with patients about (etiological) diagnosis, prognosis, and prevention, and personalization of communication to characteristics and needs of individual patients. In addition, professionals expressed a need for supporting tools and skills training to further improve their communication with patients.
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Affiliation(s)
- Heleen M A Hendriksen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
| | - Aniek M van Gils
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Tobias Hartmann
- Experimental Neurology, Saarland University, 66424, Homburg, Germany
- Deutsches Institut Für DemenzPrävention, Saarland University, 66424, Homburg, Germany
| | - Francesca Mangialasche
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Medical Unit Aging, Theme Inflammation and Aging, Stockholm, Sweden
| | - Anita Kamondi
- Department of Neurology, Neurology and Neurosurgery, National Institute of Mental Health, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Medical Unit Aging, Theme Inflammation and Aging, Stockholm, Sweden
- Ageing and Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Helsinki, Finland
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
- Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Ellen M A Smets
- Medical Psychology, Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Personalized Medicine, , Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Leonie N C Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Medical Psychology, Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Personalized Medicine, , Amsterdam, The Netherlands
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4
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Tate A, Suárez-Calvet M, Ekelund M, Eriksson S, Eriksdotter M, Van Der Flier WM, Georges J, Kivipelto M, Kramberger MG, Lindgren P, López JDG, Lötjönen J, Persson S, Pla S, Solomon A, Thurfjell L, Wimo A, Winblad B, Jönsson L. Precision medicine in neurodegeneration: the IHI-PROMINENT project. Front Neurol 2023; 14:1175922. [PMID: 37602259 PMCID: PMC10433183 DOI: 10.3389/fneur.2023.1175922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023] Open
Abstract
Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Recent advances in diagnostics and approval for new pharmaceutical treatments for Alzheimer's disease (AD), the most common etiology of dementia, heralds the beginning of precision medicine in this field. However, their implementation will challenge an already over-burdened healthcare systems. There is a need for innovative digital solutions that can drive the related clinical pathways and optimize and personalize care delivery. Public-private partnerships are ideal vehicles to tackle these challenges. Here we describe the Innovative Health Initiative (IHI) public-private partnership project PROMINENT that has been initiated by connecting leading dementia researchers, medical professionals, dementia patients and their care partners with the latest innovative health technologies using a precision medicine based digital platform. The project builds upon the knowledge and already implemented digital tools from several collaborative initiatives that address new models for early detection, diagnosis, and monitoring of AD and other neurodegenerative disorders. The project aims to provide support to improvement efforts to each aspect of the care pathway including diagnosis, prognosis, treatment, and data collection for real world evidence and cost effectiveness studies. Ultimately the PROMINENT project is expected to lead to cost-effective care and improved health outcomes.
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Affiliation(s)
- Ashley Tate
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
- Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | | | | | - Maria Eriksdotter
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Theme Inflammation and Aging, Stockholm, Sweden
| | - Wiesje M. Van Der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | | | - Miia Kivipelto
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Theme Inflammation and Aging, Stockholm, Sweden
| | - Milica G. Kramberger
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- University Medical Center Ljubljana and Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Peter Lindgren
- IHE, The Swedish Institute for Health Economics, Lund, Sweden
| | - Juan Domingo Gispert López
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pomepu Fabra, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | | | - Sofie Persson
- IHE, The Swedish Institute for Health Economics, Lund, Sweden
| | - Sandra Pla
- Synapse Research Management Partners SL, Madrid, Spain
| | - Alina Solomon
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Institute of Clinical Medicine and Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | | | - Anders Wimo
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Bengt Winblad
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Linus Jönsson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Waldemar G. Data-driven care for patients with neurodegenerative disorders. Nat Rev Neurol 2023:10.1038/s41582-023-00828-9. [PMID: 37400548 DOI: 10.1038/s41582-023-00828-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Affiliation(s)
- Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Kleiman MJ, Ariko T, Galvin JE. Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias. J Alzheimers Dis 2023; 91:895-909. [PMID: 36502329 PMCID: PMC10515190 DOI: 10.3233/jad-220891] [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] [Indexed: 12/13/2022]
Abstract
BACKGROUND The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. OBJECTIVE In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. METHODS The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic. RESULTS The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine. CONCLUSION The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.
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Affiliation(s)
- Michael J. Kleiman
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Taylor Ariko
- Department of Neurology, Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - James E. Galvin
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
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van Gils AM, Visser LNC, Hendriksen HMA, Georges J, van der Flier WM, Rhodius‐Meester HFM. Development and design of a diagnostic report to support communication in dementia: Co‐creation with patients and care partners. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12333. [PMID: 36092691 PMCID: PMC9446898 DOI: 10.1002/dad2.12333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Aniek M. van Gils
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
| | - Leonie N. C. Visser
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Neurobiology Care Sciences and Society Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet Stockholm Sweden
- Department of Medical Psychology Amsterdam Public Health Research Institute Amsterdam UMC location AMC Amsterdam The Netherlands
| | - Heleen M. A. Hendriksen
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Epidemiology and Biostatistics Amsterdam Neuroscience VU University Medical Center Amsterdam UMC Amsterdam The Netherlands
| | - Hanneke F. M. Rhodius‐Meester
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Internal Medicine Geriatric Medicine Section Amsterdam Cardiovascular Sciences Institute Amsterdam UMC location VUmc Amsterdam The Netherlands
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8
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van Gils AM, Visser LN, Hendriksen HM, Georges J, Muller M, Bouwman FH, van der Flier WM, Rhodius-Meester HF. Assessing the Views of Professionals, Patients, and Care Partners Concerning the Use of Computer Tools in Memory Clinics: International Survey Study. JMIR Form Res 2021; 5:e31053. [PMID: 34870612 PMCID: PMC8686488 DOI: 10.2196/31053] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 12/20/2022] Open
Abstract
Background Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis. Objective This study aims to identify the preferences as well as the main barriers and facilitators related to using computer tools in memory clinics for all end users, that is, clinicians, patients, and care partners. Methods Between July and October 2020, we sent out invitations to a web-based survey to clinicians using the European Alzheimer’s Disease Centers network and the Dutch Memory Clinic network, and 109 clinicians participated (mean age 45 years, SD 10; 53/109, 48.6% female). A second survey was created for patients and care partners. They were invited via Alzheimer Europe, Alzheimer’s Society United Kingdom, Amsterdam Dementia Cohort, and Amsterdam Aging Cohort. A total of 50 patients with subjective cognitive decline, mild cognitive impairment, or dementia (mean age 73 years, SD 8; 17/34, 34% female) and 46 care partners (mean age 65 years, SD 12; 25/54, 54% female) participated in this survey. Results Most clinicians reported a willingness to use diagnostic (88/109, 80.7%) and prognostic (83/109, 76.1%) computer tools. User-friendliness (71/109, 65.1%); Likert scale mean 4.5, SD 0.7), and increasing diagnostic accuracy (76/109, 69.7%; mean 4.3, SD 0.7) were reported as the main factors stimulating the adoption of a tool. Tools should also save time and provide clear information on reliability and validity. Inadequate integration with electronic patient records (46/109, 42.2%; mean 3.8, SD 1.0) and fear of losing important clinical information (48/109, 44%; mean 3.7, SD 1.2) were most frequently indicated as barriers. Patients and care partners were equally positive about the use of computer tools by clinicians, both for diagnosis (69/96, 72%) and prognosis (73/96, 76%). In addition, most of them thought favorably regarding the possibility of using the tools themselves. Conclusions This study showed that computer tools in memory clinics are positively valued by most end users. For further development and implementation, it is essential to overcome the technical and practical barriers of a tool while paying utmost attention to its reliability and validity.
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Affiliation(s)
- Aniek M van Gils
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Leonie Nc Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Heleen Ma Hendriksen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Femke H Bouwman
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Hanneke Fm Rhodius-Meester
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
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Scheijbeler EP, Schoonhoven DN, Engels MMA, Scheltens P, Stam CJ, Gouw AA, Hillebrand A. Generating diagnostic profiles of cognitive decline and dementia using magnetoencephalography. Neurobiol Aging 2021; 111:82-94. [PMID: 34906377 DOI: 10.1016/j.neurobiolaging.2021.11.002] [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: 06/04/2021] [Revised: 10/11/2021] [Accepted: 11/04/2021] [Indexed: 10/19/2022]
Abstract
Accurate identification of the underlying cause(s) of cognitive decline and dementia is challenging due to significant symptomatic overlap between subtypes. This study presents a multi-class classification framework for subjects with subjective cognitive decline, mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies, fronto-temporal dementia and cognitive decline due to psychiatric illness, trained on source-localized resting-state magnetoencephalography data. Diagnostic profiles, describing probability estimates for each of the 6 diagnoses, were assigned to individual subjects. A balanced accuracy rate of 41% and multi-class area under the curve value of 0.75 were obtained for 6-class classification. Classification primarily depended on posterior relative delta, theta and beta power and amplitude-based functional connectivity in the beta and gamma frequency band. Dementia with Lewy bodies (sensitivity: 100%, precision: 20%) and Alzheimer's disease subjects (sensitivity: 51%, precision: 90%) could be classified most accurately. Fronto-temporal dementia subjects (sensitivity: 11%, precision: 3%) were most frequently misclassified. Magnetoencephalography biomarkers hold promise to increase diagnostic accuracy in a noninvasive manner. Diagnostic profiles could provide an intuitive tool to clinicians and may facilitate implementation of the classifier in the memory clinic.
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Affiliation(s)
- Elliz P Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marjolein M A Engels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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10
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Thorlacius-Ussing G, Bruun M, Gjerum L, Frederiksen KS, Rhodius-Meester HFM, van der Flier WM, Waldemar G, Hasselbalch SG. Comparing a Single Clinician Versus a Multidisciplinary Consensus Conference Approach for Dementia Diagnostics. J Alzheimers Dis 2021; 83:741-751. [PMID: 34366342 PMCID: PMC8543265 DOI: 10.3233/jad-210278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Background: Evidence-based recommendations on the optimal evaluation approach for dementia diagnostics are limited. This impedes a harmonized workup across clinics and nations. Objective: To evaluate the diagnostic performance of a multidisciplinary consensus conference compared to a single clinician approach. Methods: In this prospective study, we enrolled 457 patients with suspected cognitive decline, from two European memory clinics. A diagnostic evaluation was performed at baseline independently in two ways: 1) by a single clinician and 2) at a multidisciplinary consensus conference. A syndrome diagnosis and an etiological diagnosis was made. The confidence in the diagnosis was recorded using a visual analogue scale. An expert panel re-evaluation diagnosis served as reference for the baseline syndrome diagnosis and a 12-24-month follow-up diagnosis for the etiological diagnosis. Results: 439 patients completed the study. We observed 12.5%discrepancy (k = 0.81) comparing the baseline syndrome diagnoses of the single clinician to the consensus conference, and 22.3%discrepancy (k = 0.68) for the baseline etiological diagnosis. The accuracy of the baseline etiological diagnosis was significantly higher at the consensus conference and was driven mainly by increased accuracy in the MCI group. Confidence in the etiological diagnosis at baseline was significantly higher at the consensus conference (p < 0.005), especially for the frontotemporal dementia diagnosis. Conclusion: The multidisciplinary consensus conference performed better on diagnostic accuracy of disease etiology and increased clinicians’ confidence. This highlights the importance of a multidisciplinary diagnostic evaluation approach for dementia diagnostics, especially when evaluating patients in the MCI stage.
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Affiliation(s)
- Gorm Thorlacius-Ussing
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Marie Bruun
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Le Gjerum
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Kristian S Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Gunhild Waldemar
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen G Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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11
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Frederiksen KS, Nielsen TR, Winblad B, Schmidt R, Kramberger MG, Jones RW, Hort J, Grimmer T, Georges J, Frölich L, Engelborghs S, Dubois B, Waldemar G. European Academy of Neurology/European Alzheimer's Disease Consortium position statement on diagnostic disclosure, biomarker counseling, and management of patients with mild cognitive impairment. Eur J Neurol 2021; 28:2147-2155. [PMID: 33368924 PMCID: PMC8246881 DOI: 10.1111/ene.14668] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND PURPOSE Careful counseling through the diagnostic process and adequate postdiagnostic support in patients with mild cognitive impairment (MCI) is important. Previous studies have indicated heterogeneity in practice and the need for guidance for clinicians. METHODS A joint European Academy of Neurology/European Alzheimer's Disease Consortium panel of dementia specialists was appointed. Through online meetings and emails, positions were developed regarding disclosing a syndrome diagnosis of MCI, pre- and postbiomarker sampling counseling, and postdiagnostic support. RESULTS Prior to diagnostic evaluation, motives and wishes of the patient should be sought. Diagnostic disclosure should be carried out by a dementia specialist taking the ethical principles of "the right to know" versus "the wish not to know" into account. Disclosure should be accompanied by written information and a follow-up plan. It should be made clear that MCI is not dementia. Prebiomarker counseling should always be carried out if biomarker sampling is considered and postbiomarker counseling if sampling is carried out. A dementia specialist knowledgeable about biomarkers should inform about pros and cons, including alternatives, to enable an autonomous and informed decision. Postbiomarker counseling will depend in part on the results of biomarkers. Follow-up should be considered for all patients with MCI and include brain-healthy advice and possibly treatment for specific underlying causes. Advice on advance directives may be relevant. CONCLUSIONS Guidance to clinicians on various aspects of the diagnostic process in patients with MCI is presented here as position statements. Further studies are needed to enable more evidence-based and standardized recommendations in the future.
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Affiliation(s)
| | - T. Rune Nielsen
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagenDenmark
| | - Bengt Winblad
- Division of NeurogeriatricsDepartment of Neurobiology, Care Sciences and SocietyCenter for Alzheimer ResearchKarolinska InstituteSolnaSweden
- Theme AgingKarolinska University HospitalStockholmSweden
| | | | - Milica G. Kramberger
- Department of NeurologyCenter for Cognitive ImpairmentsUniversity Medical CentreLjubljanaSlovenia
| | - Roy W. Jones
- RICE (The Research Institute for the Care of Older People)Royal United HospitalBath and University of BristolBristolUK
| | - Jakub Hort
- Department of NeurologyCognitive CenterSecond Faculty of Medicine and Motol University HospitalCharles UniversityPragueCzech Republic
| | - Timo Grimmer
- Department of Psychiatry and PsychotherapySchool of MedicineRechts der Isar HospitalTechnical University of MunichMunichGermany
| | | | - Lutz Frölich
- Department of Geriatric PsychiatryUniversity of HeidelbergMannheimGermany
| | - Sebastiaan Engelborghs
- Department of Neurology and Center for NeurosciencesUZ Brussel and Free University of Brussels (VUBBrusselsBelgium
- Reference Center for Biological Markers of Dementia (BIODEM)Institute Born‐BungeUniversity of AntwerpAntwerpBelgium
| | - Bruno Dubois
- Department of NeurologyDementia Research CenterSalpêtrière HospitalSorbonne UniversityParisFrance
| | - Gunhild Waldemar
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagenDenmark
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12
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Jonell P, Moëll B, Håkansson K, Henter GE, Kucherenko T, Mikheeva O, Hagman G, Holleman J, Kivipelto M, Kjellström H, Gustafson J, Beskow J. Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.642633] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.
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13
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Gjerum L, Andersen BB, Bruun M, Simonsen AH, Henriksen OM, Law I, Hasselbalch SG, Frederiksen KS. Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer's disease. PLoS One 2021; 16:e0248413. [PMID: 33711065 PMCID: PMC7954298 DOI: 10.1371/journal.pone.0248413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/26/2021] [Indexed: 12/14/2022] Open
Abstract
Background The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer’s disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting. Objective The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer’s disease. Methods Eighty-one patients clinically suspected of Alzheimer’s disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0–100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course. Results The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer’s disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET. Conclusion The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer’s disease compared to 2-[18F]FDG-PET.
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Affiliation(s)
- Le Gjerum
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Marie Bruun
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anja Hviid Simonsen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
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14
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Louwerse I, Huysmans MA, van Rijssen HJ, Gielen CLI, van der Beek AJ, Anema JR. Use of a Decision Support Tool on Prognosis of Work Ability in Work Disability Assessments: An Experimental Study Among Insurance Physicians. JOURNAL OF OCCUPATIONAL REHABILITATION 2021; 31:185-196. [PMID: 32529340 PMCID: PMC7954760 DOI: 10.1007/s10926-020-09907-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Purpose Assessment of prognosis of work disability is a challenging task for occupational health professionals. An evidence-based decision support tool, based on a prediction model, could aid professionals in the decision-making process. This study aimed to evaluate the efficacy of such a tool on Dutch insurance physicians' (IPs) prognosis of work ability and their prognostic confidence, and assess IPs' attitudes towards use of the tool. Methods We conducted an experimental study including six case vignettes among 29 IPs. For each vignette, IPs first specified their own prognosis of future work ability and prognostic confidence. Next, IPs were informed about the outcome of the prediction model and asked whether this changed their initial prognosis and prognostic confidence. Finally, respondents reported their attitude towards use of the tool in real practice. Results The concordance between IPs' prognosis and the outcome of the prediction model was low: IPs' prognosis was more positive in 72 (41%) and more negative in 20 (11%) cases. Using the decision support tool, IPs changed their prognosis in only 13% of the cases. IPs prognostic confidence decreased when prognosis was discordant, and remained unchanged when it was concordant. Concerning attitudes towards use, the wish to know more about the tool was considered as the main barrier. Conclusion The efficacy of the tool on IPs' prognosis of work ability and their prognostic confidence was low. Although the perceived barriers were overall limited, only a minority of the IPs indicated that they would be willing to use the tool in practice.
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Affiliation(s)
- I Louwerse
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands.
- Dutch Institute of Employee Benefit Schemes (UWV), Amsterdam, The Netherlands.
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands.
| | - M A Huysmans
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands
| | - H J van Rijssen
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands
- Dutch Institute of Employee Benefit Schemes (UWV), Amsterdam, The Netherlands
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands
| | - C L I Gielen
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands
- Dutch Institute of Employee Benefit Schemes (UWV), Amsterdam, The Netherlands
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands
| | - A J van der Beek
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands
| | - J R Anema
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, NL-1081 BT, Amsterdam, The Netherlands
- Research Center for Insurance Medicine, AMC-UMCG-VUmc-UWV, Amsterdam, The Netherlands
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15
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Rhodius‐Meester HF, Paajanen T, Koikkalainen J, Mahdiani S, Bruun M, Baroni M, Lemstra AW, Scheltens P, Herukka S, Pikkarainen M, Hall A, Hänninen T, Ngandu T, Kivipelto M, van Gils M, Hasselbalch SG, Mecocci P, Remes A, Soininen H, van der Flier WM, Lötjönen J. cCOG: A web-based cognitive test tool for detecting neurodegenerative disorders. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12083. [PMID: 32864411 PMCID: PMC7446945 DOI: 10.1002/dad2.12083] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/13/2020] [Accepted: 07/13/2020] [Indexed: 12/21/2022]
Abstract
INTRODUCTION Web-based cognitive tests have potential for standardized screening in neurodegenerative disorders. We examined accuracy and consistency of cCOG, a computerized cognitive tool, in detecting mild cognitive impairment (MCI) and dementia. METHODS Clinical data of 306 cognitively normal, 120 mild cognitive impairment (MCI), and 69 dementia subjects from three European cohorts were analyzed. Global cognitive score was defined from standard neuropsychological tests and compared to the corresponding estimated score from the cCOG tool containing seven subtasks. The consistency of cCOG was assessed comparing measurements administered in clinical settings and in the home environment. RESULTS cCOG produced accuracies (receiver operating characteristic-area under the curve [ROC-AUC]) between 0.71 and 0.84 in detecting MCI and 0.86 and 0.94 in detecting dementia when administered at the clinic and at home. The accuracy was comparable to the results of standard neuropsychological tests (AUC 0.69-0.77 MCI/0.91-0.92 dementia). DISCUSSION cCOG provides a promising tool for detecting MCI and dementia with potential for a cost-effective approach including home-based cognitive assessments.
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Affiliation(s)
- Hanneke F.M. Rhodius‐Meester
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Internal MedicineGeriatric Medicine SectionVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Teemu Paajanen
- Research and Service CentreFinnish Institute of Occupational HealthHelsinkiFinland
| | | | - Shadi Mahdiani
- VTT Technical Research Centre of Finland LtdTampereFinland
| | - Marie Bruun
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagen University HospitalCopenhagenDenmark
| | - Marta Baroni
- Section of Gerontology and GeriatricsUniversity of PerugiaPerugiaItaly
| | - Afina W. Lemstra
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Philip Scheltens
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Sanna‐Kaisa Herukka
- Department of NeurologyUniversity of Eastern FinlandKuopioFinland
- Department of NeurologyNeurocenterKuopio University HospitalKuopioFinland
| | | | - Anette Hall
- Department of NeurologyUniversity of Eastern FinlandKuopioFinland
| | - Tuomo Hänninen
- Department of NeurologyNeurocenterKuopio University HospitalKuopioFinland
| | - Tiia Ngandu
- Finnish Institute for Health and WelfareHelsinkiFinland
- Department of Clinical GeriatricsKarolinska InstitutetNVSCenter for Alzheimer ResearchStockholmSweden
| | - Miia Kivipelto
- Department of NeurologyUniversity of Eastern FinlandKuopioFinland
- Finnish Institute for Health and WelfareHelsinkiFinland
- Department of Clinical GeriatricsKarolinska InstitutetNVSCenter for Alzheimer ResearchStockholmSweden
| | - Mark van Gils
- VTT Technical Research Centre of Finland LtdTampereFinland
| | - Steen Gregers Hasselbalch
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagen University HospitalCopenhagenDenmark
| | - Patrizia Mecocci
- Section of Gerontology and GeriatricsUniversity of PerugiaPerugiaItaly
| | - Anne Remes
- Unit of Clinical NeuroscienceNeurology and Medical Research CenterUniversity of OuluOuluFinland
| | - Hilkka Soininen
- Department of NeurologyUniversity of Eastern FinlandKuopioFinland
- Department of NeurologyNeurocenterKuopio University HospitalKuopioFinland
| | - Wiesje M. van der Flier
- Department of NeurologyAlzheimer Center AmsterdamAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsVU University Medical CentreAmsterdamthe Netherlands
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16
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Gramkow MH, Gjerum L, Koikkalainen J, Lötjönen J, Law I, Hasselbalch SG, Waldemar G, Frederiksen KS. Prognostic value of complementary biomarkers of neurodegeneration in a mixed memory clinic cohort. PeerJ 2020; 8:e9498. [PMID: 32714664 PMCID: PMC7354835 DOI: 10.7717/peerj.9498] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/17/2020] [Indexed: 11/20/2022] Open
Abstract
Background Biomarkers of neurodegeneration, e.g. MRI brain atrophy and [18F]FDG-PET hypometabolism, are often evaluated in patients suspected of neurodegenerative disease. Objective Our primary objective was to investigate prognostic properties of atrophy and hypometabolism. Methods From March 2015-June 2016, 149 patients referred to a university hospital memory clinic were included. The primary outcome was progression/stable disease course as assessed by a clinician at 12 months follow-up. Intracohort defined z-scores of baseline MRI automatic quantified volume and [18F]FDG-PET standardized uptake value ratios were calculated for all unilaterally defined brain lobes and dichotomized as pronounced atrophy (+A)/ pronounced hypometabolism (+H) at z-score <0. A logistic regression model with progression status as the outcome was carried out with number of lobes with the patterns +A/-H, -A/+H, +A/+H respectively as predictors. The model was mutually adjusted along with adjustment for age and sex. A sensitivity analysis with a z-score dichotomization at −0.1 and −0.5 and dichotomization regarding number of lobes affected at one and three lobes was done. Results Median follow-up time was 420 days [IQR: 387-461 days] and 50 patients progressed. Patients with two or more lobes affected by the pattern +A/+H compared to patients with 0–1 lobes affected had a statistically significant increased risk of progression (odds ratio, 95 % confidence interval: 4.33, 1.90–9.86) in a multivariable model. The model was partially robust to the applied sensitivity analysis. Conclusion Combined atrophy and hypometabolism as assessed by MRI and [18F]FDG-PET in patients under suspicion of neurodegenerative disease predicts progression over 1 year.
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Affiliation(s)
- Mathias Holsey Gramkow
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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Turner RS, Stubbs T, Davies DA, Albensi BC. Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias. Front Neurol 2020; 11:496. [PMID: 32582013 PMCID: PMC7290039 DOI: 10.3389/fneur.2020.00496] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/06/2020] [Indexed: 12/21/2022] Open
Abstract
Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.
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Affiliation(s)
- R Scott Turner
- Department of Neurology, Georgetown University, Washington, DC, United States
| | - Terry Stubbs
- ActivMed, Practices & Research, Methuen, MA, United States
| | - Don A Davies
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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18
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Evaluating 2-[ 18F]FDG-PET in differential diagnosis of dementia using a data-driven decision model. NEUROIMAGE-CLINICAL 2020; 27:102267. [PMID: 32417727 PMCID: PMC7229490 DOI: 10.1016/j.nicl.2020.102267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/06/2020] [Accepted: 04/08/2020] [Indexed: 12/14/2022]
Abstract
Addition of 2-[18F]FDG-PET to common diagnostic tests improved the accuracy for DLB and FTD. Two new 2-[18F]FDG-PET biomarkers demonstrated specific disease patterns for DLB and FTD. Different combinations of diagnostic tests were valuable for each subtype of dementia.
2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[18F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[18F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[18F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[18F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.
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Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice. Eur Radiol 2020; 30:2821-2829. [PMID: 32002640 DOI: 10.1007/s00330-019-06602-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/23/2019] [Accepted: 11/27/2019] [Indexed: 01/31/2023]
Abstract
OBJECTIVES Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders. METHODS NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer's disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients' clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated. RESULTS NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively). CONCLUSION NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases. KEY POINTS • Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone. • Differential diagnosis of regionally distinct atrophy patterns is improved. • Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.
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Rhodius-Meester HFM, van Maurik IS, Koikkalainen J, Tolonen A, Frederiksen KS, Hasselbalch SG, Soininen H, Herukka SK, Remes AM, Teunissen CE, Barkhof F, Pijnenburg YAL, Scheltens P, Lötjönen J, van der Flier WM. Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy. PLoS One 2020; 15:e0226784. [PMID: 31940390 PMCID: PMC6961870 DOI: 10.1371/journal.pone.0226784] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/03/2019] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.
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Affiliation(s)
- Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Kristian S Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen G Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anne M Remes
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
- MRC Oulu, Oulu University Hospital, Oulu, Finland
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M, Soininen H, Remes AM, Hartikainen P, Waldemar G, Mecocci P, Barkhof F, Pijnenburg Y, van der Flier WM, Hasselbalch SG, Lötjönen J, Frederiksen KS. Detecting frontotemporal dementia syndromes using MRI biomarkers. NEUROIMAGE-CLINICAL 2019; 22:101711. [PMID: 30743135 PMCID: PMC6369219 DOI: 10.1016/j.nicl.2019.101711] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/01/2019] [Accepted: 02/03/2019] [Indexed: 12/20/2022]
Abstract
Background Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes. API differentiated FTD from other diagnostic groups with AUC of 83%. ASI and TPL showed highest performance for PPA subtypes. A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark.
| | | | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, Finland
| | | | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; UCL institutes of Neurology and Healthcare Engineering, London, UK
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | | | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
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